Module LFPy

Initialization of LFPy, a Python module for simulating extracellular potentials.

Group of Computational Neuroscience, Department of Mathematical Sciences and Technology, Norwegian University of Life Sciences.

Copyright (C) 2012 Computational Neuroscience Group, NMBU.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Classes

• Cell - object built on top of NEURON representing biological neuron

• TemplateCell - Similar to Cell, but for models using cell templates

• NetworkCell - Similar to TemplateCell with some attributes and methods for spike communication between parallel RANKs

• PointProcess - Parent class of Synapse and StimIntElectrode

• Synapse - Convenience class for inserting synapses onto Cell objects

• StimIntElectrode - Convenience class for inserting stimulating electrodes into Cell objects

• Network - Class for creating distributed populations of cells and handling connections between cells in populations

• NetworkPopulation - Class representing group of Cell objects distributed across MPI RANKs

• RecExtElectrode - Class for setup of simulations of extracellular potentials

• RecMEAElectrode - Class for setup of simulations of in vitro (slice) extracellular potentials

• PointSourcePotential - Base forward-model for extracellular potentials assuming point current sources in conductive media

• LineSourcePotential - Base forward-model for extracellular potentials assuming line current sources in conductive media

• OneSphereVolumeConductor - For computing extracellular potentials within and outside a homogeneous sphere

• CurrentDipoleMoment - For computing the current dipole moment,

• FourSphereVolumeConductor - For computing extracellular potentials in four-sphere head model (brain, CSF, skull, scalp)

• InfiniteVolumeConductor - To compute extracellular potentials with current dipoles in infinite volume conductor

• InfiniteHomogeneousVolCondMEG - Class for computing magnetic field from current dipole moment assuming an infinite homogeneous volume conductor

• SphericallySymmetricVolCondMEG - Class for computing magnetic field from current dipole moment assuming a spherically symmetric volume conductor

Modules

• lfpcalc - Misc. functions used by RecExtElectrode class

• tools - Some convenient functions

• inputgenerators - Functions for synaptic input time generation

• eegmegcalc - Classes for calculating current dipole moment vector P and P_tot from currents and distances.

• run_simulations - Functions to run NEURON simulations

Cell classes

class Cell

class LFPy.Cell(morphology, v_init=-70.0, Ra=None, cm=None, passive=False, passive_parameters=None, extracellular=False, tstart=0.0, tstop=100.0, dt=0.0625, nsegs_method='lambda100', lambda_f=100.0, d_lambda=0.1, max_nsegs_length=None, delete_sections=True, custom_code=None, custom_fun=None, custom_fun_args=None, pt3d=False, celsius=None, verbose=False, **kwargs)

Bases: object

The main cell class used in LFPy.

Parameters

morphology: str or neuron.h.SectionList

File path of morphology on format that NEURON can understand (w. file ending .hoc, .asc, .swc or .xml), or neuron.h.SectionList instance filled with references to neuron.h.Section instances.

v_init: float

Initial membrane potential. Defaults to -70 mV.

Ra: float or None

Axial resistance. Defaults to None (unit Ohm*cm)

cm: float

Membrane capacitance. Defaults to None (unit uF/cm2)

passive: bool

Passive mechanisms are initialized if True. Defaults to False

passive_parameters: dict

parameter dictionary with values for the passive membrane mechanism in NEURON (‘pas’). The dictionary must contain keys ‘g_pas’ [S/cm^2] and ‘e_pas’ [mV], like the default: passive_parameters=dict(g_pas=0.001, e_pas=-70)

extracellular: bool

Switch for NEURON’s extracellular mechanism. Defaults to False

dt: float

simulation timestep. Defaults to 2^-4 ms

tstart: float

Initialization time for simulation <= 0 ms. Defaults to 0.

tstop: float

Stop time for simulation > 0 ms. Defaults to 100 ms.

nsegs_method: ‘lambda100’ or ‘lambda_f’ or ‘fixed_length’ or None

nseg rule, used by NEURON to determine number of segments. Defaults to ‘lambda100’

max_nsegs_length: float or None

Maximum segment length for method ‘fixed_length’. Defaults to None

lambda_f: float

AC frequency for method ‘lambda_f’. Defaults to 100. (Hz)

d_lambda: float

Parameter for d_lambda rule. Defaults to 0.1

delete_sections: bool

Delete pre-existing section-references. Defaults to True

custom_code: list or None

List of model-specific code files ([.py/.hoc]). Defaults to None

custom_fun: list or None

List of model-specific functions with args. Defaults to None

custom_fun_args: list or None

List of args passed to custom_fun functions. Defaults to None

pt3d: bool

Use pt3d-info of the cell geometries switch. Defaults to False

celsius: float or None

Temperature in celsius. If nothing is specified here or in custom code it is 6.3 celcius

verbose: bool

Verbose output switch. Defaults to False

See also

TemplateCell

NetworkCell

Examples

Simple example of how to use the Cell class with a passive-circuit morphology (modify morphology path accordingly):

>>> import os
>>> import LFPy
>>> cellParameters = {
>>>     'morphology': os.path.join('examples', 'morphologies',
>>>                                'L5_Mainen96_LFPy.hoc'),
>>>     'v_init': -65.,
>>>     'cm': 1.0,
>>>     'Ra': 150,
>>>     'passive': True,
>>>     'passive_parameters': {'g_pas': 1./30000, 'e_pas': -65},
>>>     'dt': 2**-3,
>>>     'tstart': 0,
>>>     'tstop': 50,
>>> }
>>> cell = LFPy.Cell(**cellParameters)
>>> cell.simulate()
>>> print(cell.somav)

cellpickler(filename, pickler=<built-in function dump>)

Save data in cell to filename, using cPickle. It will however destroy any neuron.h objects upon saving, as c-objects cannot be pickled

Parameters

filename: str

Where to save cell

Returns

None or pickle

Examples

>>> # To save a cell, issue:
>>> cell.cellpickler('cell.cpickle')
>>> # To load this cell again in another session:
>>> import cPickle
>>> with file('cell.cpickle', 'rb') as f:
>>>     cell = cPickle.load(f)

chiral_morphology(axis='x')

Mirror the morphology around given axis, (default x-axis), useful to introduce more heterogeneouties in morphology shapes

Parameters

axis: str

‘x’ or ‘y’ or ‘z’

distort_geometry(factor=0.0, axis='z', nu=0.0)

Distorts cellular morphology with a relative factor along a chosen axis preserving Poisson’s ratio. A ratio nu=0.5 assumes uncompressible and isotropic media that embeds the cell. A ratio nu=0 will only affect geometry along the chosen axis. A ratio nu=-1 will isometrically scale the neuron geometry along each axis. This method does not affect the underlying cable properties of the cell, only predictions of extracellular measurements (by affecting the relative locations of sources representing the segments).

Parameters

factor: float

relative compression/stretching factor of morphology. Default is 0 (no compression/stretching). Positive values implies a compression along the chosen axis.

axis: str

which axis to apply compression/stretching. Default is “z”.

nu: float

Poisson’s ratio. Ratio between axial and transversal compression/stretching. Default is 0.

enable_extracellular_stimulation(electrode, t_ext=None, n=1, model='inf')

Enable extracellular stimulation with NEURON’s extracellular mechanism. Extracellular potentials are computed from electrode currents using the point-source approximation. If model is 'inf' (default), potentials are computed as (\(r_i\) is the position of a segment \(i\), \(r_n\) is the position of an electrode \(n\), \(\sigma\) is the conductivity of the medium):

\[V_e(r_i) = \sum_n \frac{I_n}{4 \pi \sigma |r_i - r_n|}\]

If model is 'semi', the method of images is used:

\[V_e(r_i) = \sum_n \frac{I_n}{2 \pi \sigma |r_i - r_n|}\]

Parameters

electrode: RecExtElectrode

Electrode object with stimulating currents

t_ext: np.ndarray or list

Time in ms corresponding to step changes in the provided currents. If None, currents are assumed to have the same time steps as the NEURON simulation.

n: int

Points per electrode for spatial averaging

model: str

'inf' or 'semi'. If 'inf' the medium is assumed to be infinite and homogeneous. If 'semi', the method of images is used.

Returns

v_ext: np.ndarray

Computed extracellular potentials at cell mid points

get_axial_currents_from_vmem(timepoints=None)

Compute axial currents from cell sim: get current magnitude, distance vectors and position vectors.

Parameters

timepoints: ndarray, dtype=int

array of timepoints in simulation at which you want to compute the axial currents. Defaults to False. If not given, all simulation timesteps will be included.

Returns

i_axial: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, len(timepoints)) array of axial current magnitudes I in units of (nA) in cell at all timesteps in timepoints, or at all timesteps of the simulation if timepoints=None. Contains two current magnitudes per segment, (except for the root segment): 1) the current from the mid point of the segment to the segment start point, and 2) the current from the segment start point to the mid point of the parent segment.

d_vectors: ndarray, dtype=float

Shape (3, (cell.totnsegs-1)*2) array of distance vectors traveled by each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and pos_vectors.

pos_vectors: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, 3) array of position vectors pointing to the mid point of each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and d_vectors.

Raises

AttributeError

Raises an exeption if the cell.vmem attribute cannot be found

get_axial_resistance()

Return NEURON axial resistance for all cell segments.

Returns

ri_list: ndarray, dtype=float

Shape (cell.totnsegs, ) array containing neuron.h.ri(seg.x) in units of (MOhm) for all segments in cell calculated using the neuron.h.ri(seg.x) method. neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the middle of the parent segment. Note: If seg is the first segment in a section, i.e. the parent segment belongs to a different section or there is no parent section, then neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the node connecting the segment to the parent section (or a ghost node if there is no parent)

get_closest_idx(x=0.0, y=0.0, z=0.0, section='allsec')

Get the index number of a segment in specified section which midpoint is closest to the coordinates defined by the user

Parameters

x: float

x-coordinate

y: float

y-coordinate

z: float

z-coordinate

section: str

String matching a section-name. Defaults to ‘allsec’.

Returns

int

segment index

get_dict_of_children_idx()

Return dictionary with children segment indices for all sections.

Returns

children_dict: dictionary

Dictionary containing a list for each section, with the segment index of all the section’s children. The dictionary is needed to find the sibling of a segment.

get_dict_parent_connections()

Return dictionary with parent connection point for all sections.

Returns

connection_dict: dictionary

Dictionary containing a float in range [0, 1] for each section in cell. The float gives the location on the parent segment to which the section is connected. The dictionary is needed for computing axial currents.

get_idx(section='allsec', z_min=-inf, z_max=inf)

Returns segment idx of segments from sections with names that match the pattern defined in input section on interval [z_min, z_max].

Parameters

section: str

Any entry in cell.allsecnames or just ‘allsec’.

z_min: float

Depth filter. Specify minimum z-position

z_max: float

Depth filter. Specify maximum z-position

Returns

ndarray, dtype=int

segment indices

Examples

>>> idx = cell.get_idx(section='allsec')
>>> print(idx)
>>> idx = cell.get_idx(section=['soma', 'dend', 'apic'])
>>> print(idx)

get_idx_children(parent='soma[0]')

Get the idx of parent’s children sections, i.e. segments ids of sections connected to parent-argument

Parameters

parent: str

name-pattern matching a sectionname. Defaults to “soma[0]”

Returns

ndarray, dtype=int

get_idx_name(idx=array([0]))

Return NEURON convention name of segments with index idx. The returned argument is an array of tuples with corresponding segment idx, section name, and position along the section, like; [(0, ‘neuron.h.soma[0]’, 0.5),]

Parameters

idx: ndarray, dtype int

segment indices, must be between 0 and cell.totnsegs

Returns

ndarray, dtype=object

tuples with section names of segments

get_idx_parent_children(parent='soma[0]')

Get all idx of segments of parent and children sections, i.e. segment idx of sections connected to parent-segment, and also of the parent segments

Parameters

parent: str

name-pattern matching a valid section name. Defaults to “soma[0]”

Returns

ndarray, dtype=int

Raises

Exception

In case keyword argument parent is invalid

get_idx_polygons(projection=('x', 'z'))

For each segment idx in cell create a polygon in the plane determined by the projection kwarg (default (‘x’, ‘z’)), that can be visualized using plt.fill() or mpl.collections.PolyCollection

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

polygons: list

list of (ndarray, ndarray) tuples giving the trajectory of each section

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_idx_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_intersegment_distance(idx0=0, idx1=0)

Return the Euclidean distance between midpoints of two segments.

Parameters

idx0: int

idx1: int

Returns

float

distance (µm).

get_intersegment_vector(idx0=0, idx1=0)

Return the distance between midpoints of two segments with index idx0 and idx1. The argument returned is a list [x, y, z], where x = self.x[idx1].mean(axis=-1) - self.x[idx0].mean(axis=-1) etc.

Parameters

idx0: int

idx1: int

Returns

list of floats

distance between midpoints along x,y,z axis in µm

get_multi_current_dipole_moments(timepoints=None)

Return 3D current dipole moment vector and middle position vector from each axial current in space.

Parameters

timepoints: ndarray, dtype=int or None

array of timepoints at which you want to compute the current dipole moments. Defaults to None. If not given, all simulation timesteps will be included.

Returns

multi_dipoles: ndarray, dtype = float

Shape (n_axial_currents, 3, n_timepoints) array containing the x-,y-,z-components of the current dipole moment from each axial current in cell, at all timepoints. The number of axial currents, n_axial_currents = (cell.totnsegs-1) * 2 and the number of timepoints, n_timepoints = cell.tvec.size. The current dipole moments are given in units of (nA µm).

pos_axial: ndarray, dtype = float

Shape (n_axial_currents, 3) array containing the x-, y-, and z-components giving the mid position in space of each multi_dipole in units of (µm).

Examples

Get all current dipole moments and positions from all axial currents in a single neuron simulation:

>>> import LFPy
>>> import numpy as np
>>> cell = LFPy.Cell('PATH/TO/MORPHOLOGY', extracellular=False)
>>> syn = LFPy.Synapse(cell, idx=cell.get_closest_idx(0,0,1000),
>>>                   syntype='ExpSyn', e=0., tau=1., weight=0.001)
>>> syn.set_spike_times(np.mgrid[20:100:20])
>>> cell.simulate(rec_vmem=True, rec_imem=False)
>>> timepoints = np.array([1,2,3,4])
>>> multi_dipoles, dipole_locs = cell.get_multi_current_dipole_moments(
>>>     timepoints=timepoints)

get_pt3d_polygons(projection=('x', 'z'))

For each section create a polygon in the plane determined by keyword argument projection=(‘x’, ‘z’), that can be visualized using e.g., plt.fill()

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

list

list of (x, z) tuples giving the trajectory of each section that can be plotted using PolyCollection

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_pt3d_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_rand_idx_area_and_distribution_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0, fun=<scipy.stats._continuous_distns.norm_gen object>, funargs={'loc': 0, 'scale': 100}, funweights=None)

Return nidx segment indices in section with random probability normalized to the membrane area of each segment multiplied by the value of the probability density function of “fun”, a function in the scipy.stats module with corresponding function arguments in “funargs” on the interval [z_min, z_max]

Parameters

section: str

string matching a section name

nidx: int

number of random indices

z_min: float

lower depth interval

z_max: float

upper depth interval

fun: function or str, or iterable of function or str

if function a scipy.stats method, if str, must be method in scipy.stats module with the same name (like ‘norm’), if iterable (list, tuple, numpy.array) of function or str some probability distribution in scipy.stats module

funargs: dict or iterable

iterable (list, tuple, numpy.array) of dict, arguments to fun.pdf method (e.g., w. keys ‘loc’ and ‘scale’)

funweights: None or iterable

iterable (list, tuple, numpy.array) of floats, scaling of each individual fun (i.e., introduces layer specificity)

Examples

>>> import LFPy
>>> import numpy as np
>>> import scipy.stats as ss
>>> import matplotlib.pyplot as plt
>>> from os.path import join
>>> cell = LFPy.Cell(morphology=join('cells', 'cells', 'j4a.hoc'))
>>> cell.set_rotation(x=4.99, y=-4.33, z=3.14)
>>> idx = cell.get_rand_idx_area_and_distribution_norm(
    nidx=10000, fun=ss.norm, funargs=dict(loc=0, scale=200))
>>> bins = np.arange(-30, 120)*10
>>> plt.hist(cell.zmid[idx], bins=bins, alpha=0.5)
>>> plt.show()

get_rand_idx_area_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0)

Return nidx segment indices in section with random probability normalized to the membrane area of segment on interval [z_min, z_max]

Parameters

section: str

String matching a section-name

nidx: int

Number of random indices

z_min: float

Depth filter

z_max: float

Depth filter

Returns

ndarray, dtype=int

segment indices

get_rand_prob_area_norm(section='allsec', z_min=-10000, z_max=10000)

Return the probability (0-1) for synaptic coupling on segments in section sum(prob)=1 over all segments in section. Probability normalized by area.

Parameters

section: str

string matching a section-name. Defaults to ‘allsec’

z_min: float

depth filter

z_max: float

depth filter

Returns

ndarray, dtype=float

get_rand_prob_area_norm_from_idx(idx=array([0]))

Return the normalized probability (0-1) for synaptic coupling on segments in idx-array. Normalised probability determined by area of segments.

Parameters

idx: ndarray, dtype=int.

array of segment indices

Returns

ndarray, dtype=float

insert_v_ext(v_ext, t_ext)

Set external extracellular potential around cell. Playback of some extracellular potential v_ext on each cell.totnseg segments. Assumes that the “extracellular”-mechanism is inserted on each segment. Can be used to study ephaptic effects and similar The inputs will be copied and attached to the cell object as cell.v_ext, cell.t_ext, and converted to (list of) neuron.h.Vector types, to allow playback into each segment e_extracellular reference. Can not be deleted prior to running cell.simulate()

Parameters

v_ext: ndarray

Numpy array of size cell.totnsegs x t_ext.size, unit mV

t_ext: ndarray

Time vector of v_ext in ms

Examples

>>> import LFPy
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> #create cell
>>> cell = LFPy.Cell(morphology='morphologies/example_morphology.hoc',
>>>                  passive=True)
>>> #time vector and extracellular field for every segment:
>>> t_ext = np.arange(cell.tstop / cell.dt+ 1) * cell.dt
>>> v_ext = np.random.rand(cell.totnsegs, t_ext.size)-0.5
>>> #insert potentials and record response:
>>> cell.insert_v_ext(v_ext, t_ext)
>>> cell.simulate(rec_imem=True, rec_vmem=True)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(311)
>>> ax2 = fig.add_subplot(312)
>>> ax3 = fig.add_subplot(313)
>>> eim = ax1.matshow(np.array(cell.v_ext), cmap='spectral')
>>> cb1 = fig.colorbar(eim, ax=ax1)
>>> cb1.set_label('v_ext')
>>> ax1.axis(ax1.axis('tight'))
>>> iim = ax2.matshow(cell.imem, cmap='spectral')
>>> cb2 = fig.colorbar(iim, ax=ax2)
>>> cb2.set_label('imem')
>>> ax2.axis(ax2.axis('tight'))
>>> vim = ax3.matshow(cell.vmem, cmap='spectral')
>>> ax3.axis(ax3.axis('tight'))
>>> cb3 = fig.colorbar(vim, ax=ax3)
>>> cb3.set_label('vmem')
>>> ax3.set_xlabel('tstep')
>>> plt.show()

set_point_process(idx, pptype, record_current=False, record_potential=False, **kwargs)

Insert pptype-electrode type pointprocess on segment numbered idx on cell object

Parameters

idx: int

Index of segment where point process is inserted

pptype: str

Type of pointprocess. Examples: SEClamp, VClamp, IClamp, SinIClamp, ChirpIClamp

record_current: bool

Decides if current is stored

kwargs

Parameters passed on from class StimIntElectrode

Returns

int

index of point process on cell

set_pos(x=0.0, y=0.0, z=0.0)

Set the cell position. Move the cell geometry so that midpoint of soma section is in (x, y, z). If no soma pos, use the first segment

Parameters

x: float

x position defaults to 0.0

y: float

y position defaults to 0.0

z: float

z position defaults to 0.0

set_rotation(x=None, y=None, z=None, rotation_order='xyz')

Rotate geometry of cell object around the x-, y-, z-axis in the order described by rotation_order parameter.

Parameters

x: float or None

rotation angle in radians. Default: None

y: float or None

rotation angle in radians. Default: None

z: float or None

rotation angle in radians. Default: None

rotation_order: str

string with 3 elements containing x, y and z e.g. ‘xyz’, ‘zyx’. Default: ‘xyz’

Examples

>>> cell = LFPy.Cell(**kwargs)
>>> rotation = {'x': 1.233, 'y': 0.236, 'z': np.pi}
>>> cell.set_rotation(**rotation)

set_synapse(idx, syntype, record_current=False, record_potential=False, weight=None, **kwargs)

Insert synapse on cell segment

Parameters

idx: int

Index of segment where synapse is inserted

syntype: str

Type of synapse. Built-in types in NEURON: ExpSyn, Exp2Syn

record_current: bool

If True, record synapse current

record_potential: bool

If True, record postsynaptic potential seen by the synapse

weight: float

Strength of synapse

kwargs

arguments passed on from class Synapse

Returns

int

index of synapse object on cell

simulate(probes=None, rec_imem=False, rec_vmem=False, rec_ipas=False, rec_icap=False, rec_variables=[], variable_dt=False, atol=0.001, rtol=0.0, to_memory=True, to_file=False, file_name=None, **kwargs)

This is the main function running the simulation of the NEURON model. Start NEURON simulation and record variables specified by arguments.

Parameters

probes: list of :obj:, optional

None or list of LFPykit.RecExtElectrode like object instances that each have a public method get_transformation_matrix returning a matrix that linearly maps each segments’ transmembrane current to corresponding measurement as

\[\mathbf{P} = \mathbf{M} \mathbf{I}\]

rec_imem: bool

If true, segment membrane currents will be recorded If no electrode argument is given, it is necessary to set rec_imem=True in order to make predictions later on. Units of (nA).

rec_vmem: bool

Record segment membrane voltages (mV)

rec_ipas: bool

Record passive segment membrane currents (nA)

rec_icap: bool

Record capacitive segment membrane currents (nA)

rec_variables: list

List of segment state variables to record, e.g. arg=[‘cai’, ]

variable_dt: bool

Use NEURON’s variable timestep method

atol: float

Absolute local error tolerance for NEURON variable timestep method

rtol: float

Relative local error tolerance for NEURON variable timestep method

to_memory: bool

Only valid with probes=[:obj:], store measurements as :obj:.data

to_file: bool

Only valid with probes, save simulated data in hdf5 file format

file_name: str

Name of hdf5 file, ‘.h5’ is appended if it doesnt exist

strip_hoc_objects()

Destroy any NEURON hoc objects in the cell object

class TemplateCell

class LFPy.TemplateCell(templatefile='LFPyCellTemplate.hoc', templatename='LFPyCellTemplate', templateargs=None, verbose=False, **kwargs)

Bases: Cell

LFPy.Cell like class allowing use of NEURON templates with some limitations.

This takes all the same parameters as the Cell class, but requires three more template related parameters templatefile, templatename and templateargs

Parameters

morphologystr

path to morphology file

templatefilestr

File with cell template definition(s)

templatenamestr

Cell template-name used for this cell object

templateargsstr

Parameters provided to template-definition

v_initfloat

Initial membrane potential. Default to -65.

Rafloat

axial resistance. Defaults to 150.

cmfloat

membrane capacitance. Defaults to 1.0

passivebool

Passive mechanisms are initialized if True. Defaults to True

passive_parametersdict

parameter dictionary with values for the passive membrane mechanism in NEURON (‘pas’). The dictionary must contain keys ‘g_pas’ and ‘e_pas’, like the default: passive_parameters=dict(g_pas=0.001, e_pas=-70)

extracellularbool

switch for NEURON’s extracellular mechanism. Defaults to False

dt: float

Simulation time step. Defaults to 2**-4

tstartfloat

initialization time for simulation <= 0 ms. Defaults to 0.

tstopfloat

stop time for simulation > 0 ms. Defaults to 100.

nsegs_method‘lambda100’ or ‘lambda_f’ or ‘fixed_length’ or None

nseg rule, used by NEURON to determine number of segments. Defaults to ‘lambda100’

max_nsegs_lengthfloat or None

max segment length for method ‘fixed_length’. Defaults to None

lambda_fint

AC frequency for method ‘lambda_f’. Defaults to 100

d_lambdafloat

parameter for d_lambda rule. Defaults to 0.1

delete_sectionsbool

delete pre-existing section-references. Defaults to True

custom_codelist or None

list of model-specific code files ([.py/.hoc]). Defaults to None

custom_funlist or None

list of model-specific functions with args. Defaults to None

custom_fun_argslist or None

list of args passed to custom_fun functions. Defaults to None

pt3dbool

use pt3d-info of the cell geometries switch. Defaults to False

celsiusfloat or None

Temperature in celsius. If nothing is specified here or in custom code it is 6.3 celcius

verbosebool

verbose output switch. Defaults to False

See also

Cell

NetworkCell

Examples

>>> import LFPy
>>> cellParameters = {
>>>     'morphology' : '<path to morphology.hoc>',
>>>     'templatefile' :  '<path to template_file.hoc>'
>>>     'templatename' :  'templatename'
>>>     'templateargs' :  None
>>>     'v_init' : -65,
>>>     'cm' : 1.0,
>>>     'Ra' : 150,
>>>     'passive' : True,
>>>     'passive_parameters' : {'g_pas' : 0.001, 'e_pas' : -65.},
>>>     'dt' : 2**-3,
>>>     'tstart' : 0,
>>>     'tstop' : 50,
>>> }
>>> cell = LFPy.TemplateCell(**cellParameters)
>>> cell.simulate()

cellpickler(filename, pickler=<built-in function dump>)

Save data in cell to filename, using cPickle. It will however destroy any neuron.h objects upon saving, as c-objects cannot be pickled

Parameters

filename: str

Where to save cell

Returns

None or pickle

Examples

>>> # To save a cell, issue:
>>> cell.cellpickler('cell.cpickle')
>>> # To load this cell again in another session:
>>> import cPickle
>>> with file('cell.cpickle', 'rb') as f:
>>>     cell = cPickle.load(f)

chiral_morphology(axis='x')

Mirror the morphology around given axis, (default x-axis), useful to introduce more heterogeneouties in morphology shapes

Parameters

axis: str

‘x’ or ‘y’ or ‘z’

distort_geometry(factor=0.0, axis='z', nu=0.0)

Distorts cellular morphology with a relative factor along a chosen axis preserving Poisson’s ratio. A ratio nu=0.5 assumes uncompressible and isotropic media that embeds the cell. A ratio nu=0 will only affect geometry along the chosen axis. A ratio nu=-1 will isometrically scale the neuron geometry along each axis. This method does not affect the underlying cable properties of the cell, only predictions of extracellular measurements (by affecting the relative locations of sources representing the segments).

Parameters

factor: float

relative compression/stretching factor of morphology. Default is 0 (no compression/stretching). Positive values implies a compression along the chosen axis.

axis: str

which axis to apply compression/stretching. Default is “z”.

nu: float

Poisson’s ratio. Ratio between axial and transversal compression/stretching. Default is 0.

enable_extracellular_stimulation(electrode, t_ext=None, n=1, model='inf')

Enable extracellular stimulation with NEURON’s extracellular mechanism. Extracellular potentials are computed from electrode currents using the point-source approximation. If model is 'inf' (default), potentials are computed as (\(r_i\) is the position of a segment \(i\), \(r_n\) is the position of an electrode \(n\), \(\sigma\) is the conductivity of the medium):

\[V_e(r_i) = \sum_n \frac{I_n}{4 \pi \sigma |r_i - r_n|}\]

If model is 'semi', the method of images is used:

\[V_e(r_i) = \sum_n \frac{I_n}{2 \pi \sigma |r_i - r_n|}\]

Parameters

electrode: RecExtElectrode

Electrode object with stimulating currents

t_ext: np.ndarray or list

Time in ms corresponding to step changes in the provided currents. If None, currents are assumed to have the same time steps as the NEURON simulation.

n: int

Points per electrode for spatial averaging

model: str

'inf' or 'semi'. If 'inf' the medium is assumed to be infinite and homogeneous. If 'semi', the method of images is used.

Returns

v_ext: np.ndarray

Computed extracellular potentials at cell mid points

get_axial_currents_from_vmem(timepoints=None)

Compute axial currents from cell sim: get current magnitude, distance vectors and position vectors.

Parameters

timepoints: ndarray, dtype=int

array of timepoints in simulation at which you want to compute the axial currents. Defaults to False. If not given, all simulation timesteps will be included.

Returns

i_axial: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, len(timepoints)) array of axial current magnitudes I in units of (nA) in cell at all timesteps in timepoints, or at all timesteps of the simulation if timepoints=None. Contains two current magnitudes per segment, (except for the root segment): 1) the current from the mid point of the segment to the segment start point, and 2) the current from the segment start point to the mid point of the parent segment.

d_vectors: ndarray, dtype=float

Shape (3, (cell.totnsegs-1)*2) array of distance vectors traveled by each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and pos_vectors.

pos_vectors: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, 3) array of position vectors pointing to the mid point of each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and d_vectors.

Raises

AttributeError

Raises an exeption if the cell.vmem attribute cannot be found

get_axial_resistance()

Return NEURON axial resistance for all cell segments.

Returns

ri_list: ndarray, dtype=float

Shape (cell.totnsegs, ) array containing neuron.h.ri(seg.x) in units of (MOhm) for all segments in cell calculated using the neuron.h.ri(seg.x) method. neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the middle of the parent segment. Note: If seg is the first segment in a section, i.e. the parent segment belongs to a different section or there is no parent section, then neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the node connecting the segment to the parent section (or a ghost node if there is no parent)

get_closest_idx(x=0.0, y=0.0, z=0.0, section='allsec')

Get the index number of a segment in specified section which midpoint is closest to the coordinates defined by the user

Parameters

x: float

x-coordinate

y: float

y-coordinate

z: float

z-coordinate

section: str

String matching a section-name. Defaults to ‘allsec’.

Returns

int

segment index

get_dict_of_children_idx()

Return dictionary with children segment indices for all sections.

Returns

children_dict: dictionary

Dictionary containing a list for each section, with the segment index of all the section’s children. The dictionary is needed to find the sibling of a segment.

get_dict_parent_connections()

Return dictionary with parent connection point for all sections.

Returns

connection_dict: dictionary

Dictionary containing a float in range [0, 1] for each section in cell. The float gives the location on the parent segment to which the section is connected. The dictionary is needed for computing axial currents.

get_idx(section='allsec', z_min=-inf, z_max=inf)

Returns segment idx of segments from sections with names that match the pattern defined in input section on interval [z_min, z_max].

Parameters

section: str

Any entry in cell.allsecnames or just ‘allsec’.

z_min: float

Depth filter. Specify minimum z-position

z_max: float

Depth filter. Specify maximum z-position

Returns

ndarray, dtype=int

segment indices

Examples

>>> idx = cell.get_idx(section='allsec')
>>> print(idx)
>>> idx = cell.get_idx(section=['soma', 'dend', 'apic'])
>>> print(idx)

get_idx_children(parent='soma[0]')

Get the idx of parent’s children sections, i.e. segments ids of sections connected to parent-argument

Parameters

parent: str

name-pattern matching a sectionname. Defaults to “soma[0]”

Returns

ndarray, dtype=int

get_idx_name(idx=array([0]))

Return NEURON convention name of segments with index idx. The returned argument is an array of tuples with corresponding segment idx, section name, and position along the section, like; [(0, ‘neuron.h.soma[0]’, 0.5),]

Parameters

idx: ndarray, dtype int

segment indices, must be between 0 and cell.totnsegs

Returns

ndarray, dtype=object

tuples with section names of segments

get_idx_parent_children(parent='soma[0]')

Get all idx of segments of parent and children sections, i.e. segment idx of sections connected to parent-segment, and also of the parent segments

Parameters

parent: str

name-pattern matching a valid section name. Defaults to “soma[0]”

Returns

ndarray, dtype=int

Raises

Exception

In case keyword argument parent is invalid

get_idx_polygons(projection=('x', 'z'))

For each segment idx in cell create a polygon in the plane determined by the projection kwarg (default (‘x’, ‘z’)), that can be visualized using plt.fill() or mpl.collections.PolyCollection

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

polygons: list

list of (ndarray, ndarray) tuples giving the trajectory of each section

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_idx_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_intersegment_distance(idx0=0, idx1=0)

Return the Euclidean distance between midpoints of two segments.

Parameters

idx0: int

idx1: int

Returns

float

distance (µm).

get_intersegment_vector(idx0=0, idx1=0)

Return the distance between midpoints of two segments with index idx0 and idx1. The argument returned is a list [x, y, z], where x = self.x[idx1].mean(axis=-1) - self.x[idx0].mean(axis=-1) etc.

Parameters

idx0: int

idx1: int

Returns

list of floats

distance between midpoints along x,y,z axis in µm

get_multi_current_dipole_moments(timepoints=None)

Return 3D current dipole moment vector and middle position vector from each axial current in space.

Parameters

timepoints: ndarray, dtype=int or None

array of timepoints at which you want to compute the current dipole moments. Defaults to None. If not given, all simulation timesteps will be included.

Returns

multi_dipoles: ndarray, dtype = float

Shape (n_axial_currents, 3, n_timepoints) array containing the x-,y-,z-components of the current dipole moment from each axial current in cell, at all timepoints. The number of axial currents, n_axial_currents = (cell.totnsegs-1) * 2 and the number of timepoints, n_timepoints = cell.tvec.size. The current dipole moments are given in units of (nA µm).

pos_axial: ndarray, dtype = float

Shape (n_axial_currents, 3) array containing the x-, y-, and z-components giving the mid position in space of each multi_dipole in units of (µm).

Examples

Get all current dipole moments and positions from all axial currents in a single neuron simulation:

>>> import LFPy
>>> import numpy as np
>>> cell = LFPy.Cell('PATH/TO/MORPHOLOGY', extracellular=False)
>>> syn = LFPy.Synapse(cell, idx=cell.get_closest_idx(0,0,1000),
>>>                   syntype='ExpSyn', e=0., tau=1., weight=0.001)
>>> syn.set_spike_times(np.mgrid[20:100:20])
>>> cell.simulate(rec_vmem=True, rec_imem=False)
>>> timepoints = np.array([1,2,3,4])
>>> multi_dipoles, dipole_locs = cell.get_multi_current_dipole_moments(
>>>     timepoints=timepoints)

get_pt3d_polygons(projection=('x', 'z'))

For each section create a polygon in the plane determined by keyword argument projection=(‘x’, ‘z’), that can be visualized using e.g., plt.fill()

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

list

list of (x, z) tuples giving the trajectory of each section that can be plotted using PolyCollection

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_pt3d_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_rand_idx_area_and_distribution_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0, fun=<scipy.stats._continuous_distns.norm_gen object>, funargs={'loc': 0, 'scale': 100}, funweights=None)

Return nidx segment indices in section with random probability normalized to the membrane area of each segment multiplied by the value of the probability density function of “fun”, a function in the scipy.stats module with corresponding function arguments in “funargs” on the interval [z_min, z_max]

Parameters

section: str

string matching a section name

nidx: int

number of random indices

z_min: float

lower depth interval

z_max: float

upper depth interval

fun: function or str, or iterable of function or str

if function a scipy.stats method, if str, must be method in scipy.stats module with the same name (like ‘norm’), if iterable (list, tuple, numpy.array) of function or str some probability distribution in scipy.stats module

funargs: dict or iterable

iterable (list, tuple, numpy.array) of dict, arguments to fun.pdf method (e.g., w. keys ‘loc’ and ‘scale’)

funweights: None or iterable

iterable (list, tuple, numpy.array) of floats, scaling of each individual fun (i.e., introduces layer specificity)

Examples

>>> import LFPy
>>> import numpy as np
>>> import scipy.stats as ss
>>> import matplotlib.pyplot as plt
>>> from os.path import join
>>> cell = LFPy.Cell(morphology=join('cells', 'cells', 'j4a.hoc'))
>>> cell.set_rotation(x=4.99, y=-4.33, z=3.14)
>>> idx = cell.get_rand_idx_area_and_distribution_norm(
    nidx=10000, fun=ss.norm, funargs=dict(loc=0, scale=200))
>>> bins = np.arange(-30, 120)*10
>>> plt.hist(cell.zmid[idx], bins=bins, alpha=0.5)
>>> plt.show()

get_rand_idx_area_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0)

Return nidx segment indices in section with random probability normalized to the membrane area of segment on interval [z_min, z_max]

Parameters

section: str

String matching a section-name

nidx: int

Number of random indices

z_min: float

Depth filter

z_max: float

Depth filter

Returns

ndarray, dtype=int

segment indices

get_rand_prob_area_norm(section='allsec', z_min=-10000, z_max=10000)

Return the probability (0-1) for synaptic coupling on segments in section sum(prob)=1 over all segments in section. Probability normalized by area.

Parameters

section: str

string matching a section-name. Defaults to ‘allsec’

z_min: float

depth filter

z_max: float

depth filter

Returns

ndarray, dtype=float

get_rand_prob_area_norm_from_idx(idx=array([0]))

Return the normalized probability (0-1) for synaptic coupling on segments in idx-array. Normalised probability determined by area of segments.

Parameters

idx: ndarray, dtype=int.

array of segment indices

Returns

ndarray, dtype=float

insert_v_ext(v_ext, t_ext)

Set external extracellular potential around cell. Playback of some extracellular potential v_ext on each cell.totnseg segments. Assumes that the “extracellular”-mechanism is inserted on each segment. Can be used to study ephaptic effects and similar The inputs will be copied and attached to the cell object as cell.v_ext, cell.t_ext, and converted to (list of) neuron.h.Vector types, to allow playback into each segment e_extracellular reference. Can not be deleted prior to running cell.simulate()

Parameters

v_ext: ndarray

Numpy array of size cell.totnsegs x t_ext.size, unit mV

t_ext: ndarray

Time vector of v_ext in ms

Examples

>>> import LFPy
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> #create cell
>>> cell = LFPy.Cell(morphology='morphologies/example_morphology.hoc',
>>>                  passive=True)
>>> #time vector and extracellular field for every segment:
>>> t_ext = np.arange(cell.tstop / cell.dt+ 1) * cell.dt
>>> v_ext = np.random.rand(cell.totnsegs, t_ext.size)-0.5
>>> #insert potentials and record response:
>>> cell.insert_v_ext(v_ext, t_ext)
>>> cell.simulate(rec_imem=True, rec_vmem=True)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(311)
>>> ax2 = fig.add_subplot(312)
>>> ax3 = fig.add_subplot(313)
>>> eim = ax1.matshow(np.array(cell.v_ext), cmap='spectral')
>>> cb1 = fig.colorbar(eim, ax=ax1)
>>> cb1.set_label('v_ext')
>>> ax1.axis(ax1.axis('tight'))
>>> iim = ax2.matshow(cell.imem, cmap='spectral')
>>> cb2 = fig.colorbar(iim, ax=ax2)
>>> cb2.set_label('imem')
>>> ax2.axis(ax2.axis('tight'))
>>> vim = ax3.matshow(cell.vmem, cmap='spectral')
>>> ax3.axis(ax3.axis('tight'))
>>> cb3 = fig.colorbar(vim, ax=ax3)
>>> cb3.set_label('vmem')
>>> ax3.set_xlabel('tstep')
>>> plt.show()

set_point_process(idx, pptype, record_current=False, record_potential=False, **kwargs)

Insert pptype-electrode type pointprocess on segment numbered idx on cell object

Parameters

idx: int

Index of segment where point process is inserted

pptype: str

Type of pointprocess. Examples: SEClamp, VClamp, IClamp, SinIClamp, ChirpIClamp

record_current: bool

Decides if current is stored

kwargs

Parameters passed on from class StimIntElectrode

Returns

int

index of point process on cell

set_pos(x=0.0, y=0.0, z=0.0)

Set the cell position. Move the cell geometry so that midpoint of soma section is in (x, y, z). If no soma pos, use the first segment

Parameters

x: float

x position defaults to 0.0

y: float

y position defaults to 0.0

z: float

z position defaults to 0.0

set_rotation(x=None, y=None, z=None, rotation_order='xyz')

Rotate geometry of cell object around the x-, y-, z-axis in the order described by rotation_order parameter.

Parameters

x: float or None

rotation angle in radians. Default: None

y: float or None

rotation angle in radians. Default: None

z: float or None

rotation angle in radians. Default: None

rotation_order: str

string with 3 elements containing x, y and z e.g. ‘xyz’, ‘zyx’. Default: ‘xyz’

Examples

>>> cell = LFPy.Cell(**kwargs)
>>> rotation = {'x': 1.233, 'y': 0.236, 'z': np.pi}
>>> cell.set_rotation(**rotation)

set_synapse(idx, syntype, record_current=False, record_potential=False, weight=None, **kwargs)

Insert synapse on cell segment

Parameters

idx: int

Index of segment where synapse is inserted

syntype: str

Type of synapse. Built-in types in NEURON: ExpSyn, Exp2Syn

record_current: bool

If True, record synapse current

record_potential: bool

If True, record postsynaptic potential seen by the synapse

weight: float

Strength of synapse

kwargs

arguments passed on from class Synapse

Returns

int

index of synapse object on cell

simulate(probes=None, rec_imem=False, rec_vmem=False, rec_ipas=False, rec_icap=False, rec_variables=[], variable_dt=False, atol=0.001, rtol=0.0, to_memory=True, to_file=False, file_name=None, **kwargs)

This is the main function running the simulation of the NEURON model. Start NEURON simulation and record variables specified by arguments.

Parameters

probes: list of :obj:, optional

None or list of LFPykit.RecExtElectrode like object instances that each have a public method get_transformation_matrix returning a matrix that linearly maps each segments’ transmembrane current to corresponding measurement as

\[\mathbf{P} = \mathbf{M} \mathbf{I}\]

rec_imem: bool

If true, segment membrane currents will be recorded If no electrode argument is given, it is necessary to set rec_imem=True in order to make predictions later on. Units of (nA).

rec_vmem: bool

Record segment membrane voltages (mV)

rec_ipas: bool

Record passive segment membrane currents (nA)

rec_icap: bool

Record capacitive segment membrane currents (nA)

rec_variables: list

List of segment state variables to record, e.g. arg=[‘cai’, ]

variable_dt: bool

Use NEURON’s variable timestep method

atol: float

Absolute local error tolerance for NEURON variable timestep method

rtol: float

Relative local error tolerance for NEURON variable timestep method

to_memory: bool

Only valid with probes=[:obj:], store measurements as :obj:.data

to_file: bool

Only valid with probes, save simulated data in hdf5 file format

file_name: str

Name of hdf5 file, ‘.h5’ is appended if it doesnt exist

strip_hoc_objects()

Destroy any NEURON hoc objects in the cell object

class NetworkCell

class LFPy.NetworkCell(**args)

Bases: TemplateCell

Similar to LFPy.TemplateCell with the addition of some attributes and methods allowing for spike communication between parallel RANKs.

This class allow using NEURON templates with some limitations.

This takes all the same parameters as the Cell class, but requires three more template related parameters

Parameters

morphology: str

path to morphology file

templatefile: str

File with cell template definition(s)

templatename: str

Cell template-name used for this cell object

templateargs: str

Parameters provided to template-definition

v_init: float

Initial membrane potential. Default to -65.

Ra: float

axial resistance. Defaults to 150.

cm: float

membrane capacitance. Defaults to 1.0

passive: bool

Passive mechanisms are initialized if True. Defaults to True

passive_parameters: dict

parameter dictionary with values for the passive membrane mechanism in NEURON (‘pas’). The dictionary must contain keys ‘g_pas’ and ‘e_pas’, like the default: passive_parameters=dict(g_pas=0.001, e_pas=-70)

extracellular: bool

switch for NEURON’s extracellular mechanism. Defaults to False

dt: float

Simulation time step. Defaults to 2**-4

tstart: float

initialization time for simulation <= 0 ms. Defaults to 0.

tstop: float

stop time for simulation > 0 ms. Defaults to 100.

nsegs_method: ‘lambda100’ or ‘lambda_f’ or ‘fixed_length’ or None

nseg rule, used by NEURON to determine number of segments. Defaults to ‘lambda100’

max_nsegs_length: float or None

max segment length for method ‘fixed_length’. Defaults to None

lambda_f: int

AC frequency for method ‘lambda_f’. Defaults to 100

d_lambda: float

parameter for d_lambda rule. Defaults to 0.1

delete_sections: bool

delete pre-existing section-references. Defaults to True

custom_code: list or None

list of model-specific code files ([.py/.hoc]). Defaults to None

custom_fun: list or None

list of model-specific functions with args. Defaults to None

custom_fun_args: list or None

list of args passed to custom_fun functions. Defaults to None

pt3d: bool

use pt3d-info of the cell geometries switch. Defaults to False

celsius: float or None

Temperature in celsius. If nothing is specified here or in custom code it is 6.3 celcius

verbose: bool

verbose output switch. Defaults to False

See also

Cell

TemplateCell

Examples

>>> import LFPy
>>> cellParameters = {
>>>     'morphology': '<path to morphology.hoc>',
>>>     'templatefile':  '<path to template_file.hoc>',
>>>     'templatename':  'templatename',
>>>     'templateargs':  None,
>>>     'v_init': -65,
>>>     'cm': 1.0,
>>>     'Ra': 150,
>>>     'passive': True,
>>>     'passive_parameters': {'g_pas': 0.001, 'e_pas': -65.},
>>>     'dt': 2**-3,
>>>     'tstart': 0,
>>>     'tstop': 50,
>>> }
>>> cell = LFPy.NetworkCell(**cellParameters)
>>> cell.simulate()

cellpickler(filename, pickler=<built-in function dump>)

Save data in cell to filename, using cPickle. It will however destroy any neuron.h objects upon saving, as c-objects cannot be pickled

Parameters

filename: str

Where to save cell

Returns

None or pickle

Examples

>>> # To save a cell, issue:
>>> cell.cellpickler('cell.cpickle')
>>> # To load this cell again in another session:
>>> import cPickle
>>> with file('cell.cpickle', 'rb') as f:
>>>     cell = cPickle.load(f)

chiral_morphology(axis='x')

Mirror the morphology around given axis, (default x-axis), useful to introduce more heterogeneouties in morphology shapes

Parameters

axis: str

‘x’ or ‘y’ or ‘z’

create_spike_detector(target=None, threshold=-10.0, weight=0.0, delay=0.0)

Create spike-detecting NetCon object attached to the cell’s soma midpoint, but this could be extended to having multiple spike-detection sites. The NetCon object created is attached to the cell’s _hoc_sd_netconlist attribute, and will be used by the Network class when creating connections between all presynaptic cells and postsynaptic cells on each local RANK.

Parameters

target: None (default) or a NEURON point process

threshold: float

spike detection threshold

weight: float

connection weight (not used unless target is a point process)

delay: float

connection delay (not used unless target is a point process)

create_synapse(cell, sec, x=0.5, syntype=ExpSyn(), synparams={'e': 0.0, 'tau': 2.0}, assert_syn_values=False)

Create synapse object of type syntype on sec(x) of cell and append to list cell.netconsynapses

TODO: Use LFPy.Synapse class if possible.

Parameters

cell: object

instantiation of class NetworkCell or similar

sec: neuron.h.Section object,

section reference on cell

x: float in [0, 1],

relative position along section

syntype: hoc.HocObject

NEURON synapse model reference, e.g., neuron.h.ExpSyn

synparams: dict

parameters for syntype, e.g., for neuron.h.ExpSyn we have:

tau: float, synapse time constant e: float, synapse reversal potential

assert_syn_values: bool

if True, raise AssertionError if synapse attribute values do not match the values in the synparams dictionary

Raises

AssertionError

distort_geometry(factor=0.0, axis='z', nu=0.0)

Distorts cellular morphology with a relative factor along a chosen axis preserving Poisson’s ratio. A ratio nu=0.5 assumes uncompressible and isotropic media that embeds the cell. A ratio nu=0 will only affect geometry along the chosen axis. A ratio nu=-1 will isometrically scale the neuron geometry along each axis. This method does not affect the underlying cable properties of the cell, only predictions of extracellular measurements (by affecting the relative locations of sources representing the segments).

Parameters

factor: float

relative compression/stretching factor of morphology. Default is 0 (no compression/stretching). Positive values implies a compression along the chosen axis.

axis: str

which axis to apply compression/stretching. Default is “z”.

nu: float

Poisson’s ratio. Ratio between axial and transversal compression/stretching. Default is 0.

enable_extracellular_stimulation(electrode, t_ext=None, n=1, model='inf')

Enable extracellular stimulation with NEURON’s extracellular mechanism. Extracellular potentials are computed from electrode currents using the point-source approximation. If model is 'inf' (default), potentials are computed as (\(r_i\) is the position of a segment \(i\), \(r_n\) is the position of an electrode \(n\), \(\sigma\) is the conductivity of the medium):

\[V_e(r_i) = \sum_n \frac{I_n}{4 \pi \sigma |r_i - r_n|}\]

If model is 'semi', the method of images is used:

\[V_e(r_i) = \sum_n \frac{I_n}{2 \pi \sigma |r_i - r_n|}\]

Parameters

electrode: RecExtElectrode

Electrode object with stimulating currents

t_ext: np.ndarray or list

Time in ms corresponding to step changes in the provided currents. If None, currents are assumed to have the same time steps as the NEURON simulation.

n: int

Points per electrode for spatial averaging

model: str

'inf' or 'semi'. If 'inf' the medium is assumed to be infinite and homogeneous. If 'semi', the method of images is used.

Returns

v_ext: np.ndarray

Computed extracellular potentials at cell mid points

get_axial_currents_from_vmem(timepoints=None)

Compute axial currents from cell sim: get current magnitude, distance vectors and position vectors.

Parameters

timepoints: ndarray, dtype=int

array of timepoints in simulation at which you want to compute the axial currents. Defaults to False. If not given, all simulation timesteps will be included.

Returns

i_axial: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, len(timepoints)) array of axial current magnitudes I in units of (nA) in cell at all timesteps in timepoints, or at all timesteps of the simulation if timepoints=None. Contains two current magnitudes per segment, (except for the root segment): 1) the current from the mid point of the segment to the segment start point, and 2) the current from the segment start point to the mid point of the parent segment.

d_vectors: ndarray, dtype=float

Shape (3, (cell.totnsegs-1)*2) array of distance vectors traveled by each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and pos_vectors.

pos_vectors: ndarray, dtype=float

Shape ((cell.totnsegs-1)*2, 3) array of position vectors pointing to the mid point of each axial current in i_axial in units of (µm). The indices of the first axis, correspond to the first axis of i_axial and d_vectors.

Raises

AttributeError

Raises an exeption if the cell.vmem attribute cannot be found

get_axial_resistance()

Return NEURON axial resistance for all cell segments.

Returns

ri_list: ndarray, dtype=float

Shape (cell.totnsegs, ) array containing neuron.h.ri(seg.x) in units of (MOhm) for all segments in cell calculated using the neuron.h.ri(seg.x) method. neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the middle of the parent segment. Note: If seg is the first segment in a section, i.e. the parent segment belongs to a different section or there is no parent section, then neuron.h.ri(seg.x) returns the axial resistance from the middle of the segment to the node connecting the segment to the parent section (or a ghost node if there is no parent)

get_closest_idx(x=0.0, y=0.0, z=0.0, section='allsec')

Get the index number of a segment in specified section which midpoint is closest to the coordinates defined by the user

Parameters

x: float

x-coordinate

y: float

y-coordinate

z: float

z-coordinate

section: str

String matching a section-name. Defaults to ‘allsec’.

Returns

int

segment index

get_dict_of_children_idx()

Return dictionary with children segment indices for all sections.

Returns

children_dict: dictionary

Dictionary containing a list for each section, with the segment index of all the section’s children. The dictionary is needed to find the sibling of a segment.

get_dict_parent_connections()

Return dictionary with parent connection point for all sections.

Returns

connection_dict: dictionary

Dictionary containing a float in range [0, 1] for each section in cell. The float gives the location on the parent segment to which the section is connected. The dictionary is needed for computing axial currents.

get_idx(section='allsec', z_min=-inf, z_max=inf)

Returns segment idx of segments from sections with names that match the pattern defined in input section on interval [z_min, z_max].

Parameters

section: str

Any entry in cell.allsecnames or just ‘allsec’.

z_min: float

Depth filter. Specify minimum z-position

z_max: float

Depth filter. Specify maximum z-position

Returns

ndarray, dtype=int

segment indices

Examples

>>> idx = cell.get_idx(section='allsec')
>>> print(idx)
>>> idx = cell.get_idx(section=['soma', 'dend', 'apic'])
>>> print(idx)

get_idx_children(parent='soma[0]')

Get the idx of parent’s children sections, i.e. segments ids of sections connected to parent-argument

Parameters

parent: str

name-pattern matching a sectionname. Defaults to “soma[0]”

Returns

ndarray, dtype=int

get_idx_name(idx=array([0]))

Return NEURON convention name of segments with index idx. The returned argument is an array of tuples with corresponding segment idx, section name, and position along the section, like; [(0, ‘neuron.h.soma[0]’, 0.5),]

Parameters

idx: ndarray, dtype int

segment indices, must be between 0 and cell.totnsegs

Returns

ndarray, dtype=object

tuples with section names of segments

get_idx_parent_children(parent='soma[0]')

Get all idx of segments of parent and children sections, i.e. segment idx of sections connected to parent-segment, and also of the parent segments

Parameters

parent: str

name-pattern matching a valid section name. Defaults to “soma[0]”

Returns

ndarray, dtype=int

Raises

Exception

In case keyword argument parent is invalid

get_idx_polygons(projection=('x', 'z'))

For each segment idx in cell create a polygon in the plane determined by the projection kwarg (default (‘x’, ‘z’)), that can be visualized using plt.fill() or mpl.collections.PolyCollection

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

polygons: list

list of (ndarray, ndarray) tuples giving the trajectory of each section

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_idx_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_intersegment_distance(idx0=0, idx1=0)

Return the Euclidean distance between midpoints of two segments.

Parameters

idx0: int

idx1: int

Returns

float

distance (µm).

get_intersegment_vector(idx0=0, idx1=0)

Return the distance between midpoints of two segments with index idx0 and idx1. The argument returned is a list [x, y, z], where x = self.x[idx1].mean(axis=-1) - self.x[idx0].mean(axis=-1) etc.

Parameters

idx0: int

idx1: int

Returns

list of floats

distance between midpoints along x,y,z axis in µm

get_multi_current_dipole_moments(timepoints=None)

Return 3D current dipole moment vector and middle position vector from each axial current in space.

Parameters

timepoints: ndarray, dtype=int or None

array of timepoints at which you want to compute the current dipole moments. Defaults to None. If not given, all simulation timesteps will be included.

Returns

multi_dipoles: ndarray, dtype = float

Shape (n_axial_currents, 3, n_timepoints) array containing the x-,y-,z-components of the current dipole moment from each axial current in cell, at all timepoints. The number of axial currents, n_axial_currents = (cell.totnsegs-1) * 2 and the number of timepoints, n_timepoints = cell.tvec.size. The current dipole moments are given in units of (nA µm).

pos_axial: ndarray, dtype = float

Shape (n_axial_currents, 3) array containing the x-, y-, and z-components giving the mid position in space of each multi_dipole in units of (µm).

Examples

Get all current dipole moments and positions from all axial currents in a single neuron simulation:

>>> import LFPy
>>> import numpy as np
>>> cell = LFPy.Cell('PATH/TO/MORPHOLOGY', extracellular=False)
>>> syn = LFPy.Synapse(cell, idx=cell.get_closest_idx(0,0,1000),
>>>                   syntype='ExpSyn', e=0., tau=1., weight=0.001)
>>> syn.set_spike_times(np.mgrid[20:100:20])
>>> cell.simulate(rec_vmem=True, rec_imem=False)
>>> timepoints = np.array([1,2,3,4])
>>> multi_dipoles, dipole_locs = cell.get_multi_current_dipole_moments(
>>>     timepoints=timepoints)

get_pt3d_polygons(projection=('x', 'z'))

For each section create a polygon in the plane determined by keyword argument projection=(‘x’, ‘z’), that can be visualized using e.g., plt.fill()

Parameters

projection: tuple of strings

Determining projection. Defaults to (‘x’, ‘z’)

Returns

list

list of (x, z) tuples giving the trajectory of each section that can be plotted using PolyCollection

Examples

>>> from matplotlib.collections import PolyCollection
>>> import matplotlib.pyplot as plt
>>> cell = LFPy.Cell(morphology='PATH/TO/MORPHOLOGY')
>>> zips = []
>>> for x, z in cell.get_pt3d_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> fig = plt.figure()
>>> ax = fig.add_subplot(111)
>>> ax.add_collection(polycol)
>>> ax.axis(ax.axis('equal'))
>>> plt.show()

get_rand_idx_area_and_distribution_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0, fun=<scipy.stats._continuous_distns.norm_gen object>, funargs={'loc': 0, 'scale': 100}, funweights=None)

Return nidx segment indices in section with random probability normalized to the membrane area of each segment multiplied by the value of the probability density function of “fun”, a function in the scipy.stats module with corresponding function arguments in “funargs” on the interval [z_min, z_max]

Parameters

section: str

string matching a section name

nidx: int

number of random indices

z_min: float

lower depth interval

z_max: float

upper depth interval

fun: function or str, or iterable of function or str

if function a scipy.stats method, if str, must be method in scipy.stats module with the same name (like ‘norm’), if iterable (list, tuple, numpy.array) of function or str some probability distribution in scipy.stats module

funargs: dict or iterable

iterable (list, tuple, numpy.array) of dict, arguments to fun.pdf method (e.g., w. keys ‘loc’ and ‘scale’)

funweights: None or iterable

iterable (list, tuple, numpy.array) of floats, scaling of each individual fun (i.e., introduces layer specificity)

Examples

>>> import LFPy
>>> import numpy as np
>>> import scipy.stats as ss
>>> import matplotlib.pyplot as plt
>>> from os.path import join
>>> cell = LFPy.Cell(morphology=join('cells', 'cells', 'j4a.hoc'))
>>> cell.set_rotation(x=4.99, y=-4.33, z=3.14)
>>> idx = cell.get_rand_idx_area_and_distribution_norm(
    nidx=10000, fun=ss.norm, funargs=dict(loc=0, scale=200))
>>> bins = np.arange(-30, 120)*10
>>> plt.hist(cell.zmid[idx], bins=bins, alpha=0.5)
>>> plt.show()

get_rand_idx_area_norm(section='allsec', nidx=1, z_min=-1000000.0, z_max=1000000.0)

Return nidx segment indices in section with random probability normalized to the membrane area of segment on interval [z_min, z_max]

Parameters

section: str

String matching a section-name

nidx: int

Number of random indices

z_min: float

Depth filter

z_max: float

Depth filter

Returns

ndarray, dtype=int

segment indices

get_rand_prob_area_norm(section='allsec', z_min=-10000, z_max=10000)

Return the probability (0-1) for synaptic coupling on segments in section sum(prob)=1 over all segments in section. Probability normalized by area.

Parameters

section: str

string matching a section-name. Defaults to ‘allsec’

z_min: float

depth filter

z_max: float

depth filter

Returns

ndarray, dtype=float

get_rand_prob_area_norm_from_idx(idx=array([0]))

Return the normalized probability (0-1) for synaptic coupling on segments in idx-array. Normalised probability determined by area of segments.

Parameters

idx: ndarray, dtype=int.

array of segment indices

Returns

ndarray, dtype=float

insert_v_ext(v_ext, t_ext)

Set external extracellular potential around cell. Playback of some extracellular potential v_ext on each cell.totnseg segments. Assumes that the “extracellular”-mechanism is inserted on each segment. Can be used to study ephaptic effects and similar The inputs will be copied and attached to the cell object as cell.v_ext, cell.t_ext, and converted to (list of) neuron.h.Vector types, to allow playback into each segment e_extracellular reference. Can not be deleted prior to running cell.simulate()

Parameters

v_ext: ndarray

Numpy array of size cell.totnsegs x t_ext.size, unit mV

t_ext: ndarray

Time vector of v_ext in ms

Examples

>>> import LFPy
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> #create cell
>>> cell = LFPy.Cell(morphology='morphologies/example_morphology.hoc',
>>>                  passive=True)
>>> #time vector and extracellular field for every segment:
>>> t_ext = np.arange(cell.tstop / cell.dt+ 1) * cell.dt
>>> v_ext = np.random.rand(cell.totnsegs, t_ext.size)-0.5
>>> #insert potentials and record response:
>>> cell.insert_v_ext(v_ext, t_ext)
>>> cell.simulate(rec_imem=True, rec_vmem=True)
>>> fig = plt.figure()
>>> ax1 = fig.add_subplot(311)
>>> ax2 = fig.add_subplot(312)
>>> ax3 = fig.add_subplot(313)
>>> eim = ax1.matshow(np.array(cell.v_ext), cmap='spectral')
>>> cb1 = fig.colorbar(eim, ax=ax1)
>>> cb1.set_label('v_ext')
>>> ax1.axis(ax1.axis('tight'))
>>> iim = ax2.matshow(cell.imem, cmap='spectral')
>>> cb2 = fig.colorbar(iim, ax=ax2)
>>> cb2.set_label('imem')
>>> ax2.axis(ax2.axis('tight'))
>>> vim = ax3.matshow(cell.vmem, cmap='spectral')
>>> ax3.axis(ax3.axis('tight'))
>>> cb3 = fig.colorbar(vim, ax=ax3)
>>> cb3.set_label('vmem')
>>> ax3.set_xlabel('tstep')
>>> plt.show()

set_point_process(idx, pptype, record_current=False, record_potential=False, **kwargs)

Insert pptype-electrode type pointprocess on segment numbered idx on cell object

Parameters

idx: int

Index of segment where point process is inserted

pptype: str

Type of pointprocess. Examples: SEClamp, VClamp, IClamp, SinIClamp, ChirpIClamp

record_current: bool

Decides if current is stored

kwargs

Parameters passed on from class StimIntElectrode

Returns

int

index of point process on cell

set_pos(x=0.0, y=0.0, z=0.0)

Set the cell position. Move the cell geometry so that midpoint of soma section is in (x, y, z). If no soma pos, use the first segment

Parameters

x: float

x position defaults to 0.0

y: float

y position defaults to 0.0

z: float

z position defaults to 0.0

set_rotation(x=None, y=None, z=None, rotation_order='xyz')

Rotate geometry of cell object around the x-, y-, z-axis in the order described by rotation_order parameter.

Parameters

x: float or None

rotation angle in radians. Default: None

y: float or None

rotation angle in radians. Default: None

z: float or None

rotation angle in radians. Default: None

rotation_order: str

string with 3 elements containing x, y and z e.g. ‘xyz’, ‘zyx’. Default: ‘xyz’

Examples

>>> cell = LFPy.Cell(**kwargs)
>>> rotation = {'x': 1.233, 'y': 0.236, 'z': np.pi}
>>> cell.set_rotation(**rotation)

set_synapse(idx, syntype, record_current=False, record_potential=False, weight=None, **kwargs)

Insert synapse on cell segment

Parameters

idx: int

Index of segment where synapse is inserted

syntype: str

Type of synapse. Built-in types in NEURON: ExpSyn, Exp2Syn

record_current: bool

If True, record synapse current

record_potential: bool

If True, record postsynaptic potential seen by the synapse

weight: float

Strength of synapse

kwargs

arguments passed on from class Synapse

Returns

int

index of synapse object on cell

simulate(probes=None, rec_imem=False, rec_vmem=False, rec_ipas=False, rec_icap=False, rec_variables=[], variable_dt=False, atol=0.001, rtol=0.0, to_memory=True, to_file=False, file_name=None, **kwargs)

This is the main function running the simulation of the NEURON model. Start NEURON simulation and record variables specified by arguments.

Parameters

probes: list of :obj:, optional

None or list of LFPykit.RecExtElectrode like object instances that each have a public method get_transformation_matrix returning a matrix that linearly maps each segments’ transmembrane current to corresponding measurement as

\[\mathbf{P} = \mathbf{M} \mathbf{I}\]

rec_imem: bool

If true, segment membrane currents will be recorded If no electrode argument is given, it is necessary to set rec_imem=True in order to make predictions later on. Units of (nA).

rec_vmem: bool

Record segment membrane voltages (mV)

rec_ipas: bool

Record passive segment membrane currents (nA)

rec_icap: bool

Record capacitive segment membrane currents (nA)

rec_variables: list

List of segment state variables to record, e.g. arg=[‘cai’, ]

variable_dt: bool

Use NEURON’s variable timestep method

atol: float

Absolute local error tolerance for NEURON variable timestep method

rtol: float

Relative local error tolerance for NEURON variable timestep method

to_memory: bool

Only valid with probes=[:obj:], store measurements as :obj:.data

to_file: bool

Only valid with probes, save simulated data in hdf5 file format

file_name: str

Name of hdf5 file, ‘.h5’ is appended if it doesnt exist

strip_hoc_objects()

Destroy any NEURON hoc objects in the cell object

Point processes

class PointProcess

class LFPy.PointProcess(cell, idx, record_current=False, record_potential=False, **kwargs)

Bases: object

Parent class of Synapse, StimIntElectrode. Created in order to import and set some shared variables and extract Cartesian coordinates of segments

Parameters

cell: obj

LFPy.Cell object

idx: int

index of segment

record_current: bool

Must be set to True for recording of pointprocess currents

record_potential: bool

Must be set to True for recording potential of pointprocess target idx

kwargs: pointprocess specific variables passed on to cell/neuron

See also

Synapse

StimIntElectrode

update_pos(cell)

Extract coordinates of point-process

class Synapse

class LFPy.Synapse(cell, idx, syntype, record_current=False, record_potential=False, **kwargs)

Bases: PointProcess

The synapse class, pointprocesses that spawn membrane currents. See http://www.neuron.yale.edu/neuron/static/docs/help/neuron/neuron/ mech.html#pointprocesses for details, or corresponding mod-files.

This class is meant to be used with synaptic mechanisms, giving rise to currents that will be part of the membrane currents at times governed by the methods set_spike_times or set_spike_times_w_netstim.

Parameters

cell: obj

LFPy.Cell or LFPy.TemplateCell instance to receive synapptic input

idx: int

Cell index where the synaptic input arrives

syntype: str

Type of synapse, such as ‘ExpSyn’, ‘Exp2Syn’, ‘AlphaSynapse’

record_current: bool

If True, record synapse to <synapse>.i in units of nA

**kwargs

Additional arguments to be passed on to NEURON in Cell.set_synapse

See also

StimIntElectrode

Examples

>>> import pylab as pl
>>> pl.interactive(1)
>>> import LFPy
>>> import os
>>> cellParameters = {
>>>     'morphology':  os.path.join('examples', 'morphologies',
>>>                                 'L5_Mainen96_LFPy.hoc'),
>>>     'passive': True,
>>>     'tstop':     50,
>>> }
>>> cell = LFPy.Cell(**cellParameters)

>>> synapseParameters = {
>>>     'idx': cell.get_closest_idx(x=0, y=0, z=800),
>>>     'e': 0,                                # reversal potential
>>>     'syntype': 'ExpSyn',                   # synapse type
>>>     'tau': 2,                              # syn. time constant
>>>     'weight': 0.01,                        # syn. weight
>>>     'record_current': True                 # syn. current record
>>> }
>>> synapse = LFPy.Synapse(cell, **synapseParameters)
>>> synapse.set_spike_times(pl.array([10, 15, 20, 25]))
>>> cell.simulate()

>>> pl.subplot(211)
>>> pl.plot(cell.tvec, synapse.i)
>>> pl.title('Synapse current (nA)')
>>> pl.subplot(212)
>>> pl.plot(cell.tvec, cell.somav)
>>> pl.title('Somatic potential (mV)')

collect_current(cell)

Collect synapse current. Sets <synapse>.i

Parameters

cell: LFPy.Cell like object

collect_potential(cell)

Collect membrane potential of segment with synapse. Sets <synapse>.v

Parameters

cell: LFPy.Cell like object

set_spike_times(sptimes=array([], dtype=float64))

Set the spike times explicitly using numpy arrays

Parameters

ndarray, dtype=float

Sequence of synapse activation times

set_spike_times_w_netstim(noise=1.0, start=0.0, number=1000.0, interval=10.0, seed=1234.0)

Generate a train of pre-synaptic stimulus times by setting up the neuron NetStim object associated with this synapse

Parameters

noise: float in range [0, 1]

Fractional randomness, from deterministic to intervals that drawn from negexp distribution (Poisson spiketimes).

start: float

ms, (most likely) start time of first spike

number: int

(average) number of spikes

interval: float

ms, (mean) time between spikes

seed: float

Random seed value

class StimIntElectrode

class LFPy.StimIntElectrode(cell, idx, pptype='SEClamp', record_current=False, record_potential=False, **kwargs)

Bases: PointProcess

Class for NEURON point processes representing electrode currents, such as VClamp, SEClamp and ICLamp.

Membrane currents will no longer sum to zero if these mechanisms are used, as the equivalent circuit is akin to a current input to the segment from a far away extracellular location (“ground”), not immediately from the surface to the inside of the segment as with transmembrane currents.

Refer to NEURON documentation @ neuron.yale.edu for keyword arguments or class documentation in Python issuing e.g.

help(neuron.h.VClamp)

Will insert pptype on cell-instance, pass the corresponding kwargs onto cell.set_point_process.

Parameters

cell: obj

LFPy.Cell or LFPy.TemplateCell instance to receive Stimulation

electrode input

idx: int

Cell segment index where the stimulation electrode is placed

pptype: str

Type of point process. Built-in examples: VClamp, SEClamp and ICLamp. Defaults to ‘SEClamp’.

record_current: bool

Decides if current is recorded

record_potential: bool

switch for recording the potential on postsynaptic segment index

**kwargs

Additional arguments to be passed on to NEURON in cell.set_point_process

See also

Synapse

Examples

>>> import pylab as pl
>>> pl.ion()
>>> import os
>>> import LFPy
>>> # define a list of different electrode implementations from NEURON
>>> pointprocesses = [
>>>     {
>>>         'idx': 0,
>>>         'record_current': True,
>>>         'pptype': 'IClamp',
>>>         'amp': 1,
>>>         'dur': 20,
>>>         'delay': 10,
>>>     },
>>>     {
>>>         'idx': 0,
>>>         'record_current': True,
>>>         'pptype': 'VClamp',
>>>         'amp': [-70, 0, -70],
>>>         'dur': [10, 20, 10],
>>>    },
>>>    {
>>>        'idx': 0,
>>>        'record_current': True,
>>>        'pptype': 'SEClamp',
>>>        'dur1': 10,
>>>        'amp1': -70,
>>>        'dur2': 20,
>>>        'amp2': 0,
>>>        'dur3': 10,
>>>        'amp3': -70,
>>>     },
>>> ]
>>> # create a cell instance for each electrode
>>> fix, axes = pl.subplots(2, 1, sharex=True)
>>> for pointprocess in pointprocesses:
>>>     cell = LFPy.Cell(morphology=os.path.join('examples',
>>>                                              'morphologies',
>>>                                              'L5_Mainen96_LFPy.hoc'),
>>>                      passive=True)
>>>     stimulus = LFPy.StimIntElectrode(cell, **pointprocess)
>>>     cell.simulate()
>>>     axes[0].plot(cell.tvec, stimulus.i, label=pointprocess['pptype'])
>>>     axes[0].legend(loc='best')
>>>     axes[0].set_title('Stimulus currents (nA)')
>>>     axes[1].plot(cell.tvec, cell.somav, label=pointprocess['pptype'])
>>>     axes[1].legend(loc='best')
>>>     axes[1].set_title('Somatic potential (mV)')

collect_current(cell)

Fetch electrode current. Sets Sets ``<stimintelectrode>.i

Parameters

cell: LFPy.Cell like object

collect_potential(cell)

Collect membrane potential of segment with PointProcess. Sets <stimintelectrode>.v

Parameters

cell: LFPy.Cell like object

Networks

class Network

class LFPy.Network(dt=0.1, tstart=0.0, tstop=1000.0, v_init=-65.0, celsius=6.3, OUTPUTPATH='example_parallel_network', verbose=False)

Bases: object

Network class, creating distributed populations of cells of type Cell and handling connections between cells in the respective populations.

Parameters

dt: float

Simulation timestep size

tstart: float

Start time of simulation

tstop: float

End time of simulation

v_init: float

Membrane potential set at first timestep across all cells

celsius: float

Global control of temperature, affect channel kinetics. It will also be forced when creating the different Cell objects, as LFPy.Cell and LFPy.TemplateCell also accept the same keyword argument.

verbose: bool

if True, print out misc. messages

connect(pre, post, connectivity, syntype=ExpSyn(), synparams={'e': 0.0, 'tau': 2.0}, weightfun=<built-in method normal of numpy.random.mtrand.RandomState object>, weightargs={'loc': 0.1, 'scale': 0.01}, minweight=0, delayfun=<scipy.stats._continuous_distns.truncnorm_gen object>, delayargs={'a': 0.3, 'b': inf, 'loc': 2, 'scale': 0.2}, mindelay=None, multapsefun=<scipy.stats._continuous_distns.truncnorm_gen object>, multapseargs={'a': -3.0, 'b': 6.0, 'loc': 4, 'scale': 1}, syn_pos_args={'fun': [<scipy.stats._continuous_distns.norm_gen object>, <scipy.stats._continuous_distns.norm_gen object>], 'funargs': [{'loc': 0, 'scale': 100}, {'loc': 0, 'scale': 100}], 'funweights': [0.5, 0.5], 'section': ['soma', 'dend', 'apic'], 'z_max': 1000000.0, 'z_min': -1000000.0}, save_connections=False)

Connect presynaptic cells to postsynaptic cells. Connections are drawn from presynaptic cells to postsynaptic cells, hence connectivity array must only be specified for postsynaptic units existing on this RANK.

Parameters

pre: str

presynaptic population name

post: str

postsynaptic population name

connectivity: ndarray / (scipy.sparse array)

boolean connectivity matrix between pre and post.

syntype: hoc.HocObject

reference to NEURON synapse mechanism, e.g., neuron.h.ExpSyn

synparams: dict

dictionary of parameters for synapse mechanism, keys ‘e’, ‘tau’ etc.

weightfun: function

function used to draw weights from a numpy.random distribution

weightargs: dict

parameters passed to weightfun

minweight: float,

minimum weight in units of nS

delayfun: function

function used to draw delays from a subclass of scipy.stats.rv_continuous or numpy.random distribution

delayargs: dict

parameters passed to delayfun

mindelay: float,

minimum delay in multiples of dt. Ignored if delayfun is an inherited from scipy.stats.rv_continuous

multapsefun: function or None

function reference, e.g., scipy.stats.rv_continuous used to draw a number of synapses for a cell-to-cell connection. If None, draw only one connection

multapseargs: dict

arguments passed to multapsefun

syn_pos_args: dict

arguments passed to inherited LFPy.Cell method NetworkCell.get_rand_idx_area_and_distribution_norm to find synapse locations.

save_connections: bool

if True (default False), save instantiated connections to HDF5 file Network.OUTPUTPATH/synapse_connections.h5 as dataset <pre>:<post> using a structured ndarray with dtype

[('gid_pre'), ('gid', 'i8'), ('weight', 'f8'), ('delay', 'f8'),
('sec', 'U64'), ('sec.x', 'f8'),
('x', 'f8'), ('y', 'f8'), ('z', 'f8')],

where gid_pre is presynapic cell id, gid is postsynaptic cell id, weight connection weight, delay connection delay, sec section name, sec.x relative location on section, and x, y, z the corresponding midpoint coordinates of the target segment.

Returns

list

Length 2 list with ndarrays [conncount, syncount] with numbers of instantiated connections and synapses.

Raises

DeprecationWarning

if delayfun is not a subclass of scipy.stats.rv_continuous

create_population(CWD=None, CELLPATH=None, Cell=<class 'LFPy.network.NetworkCell'>, POP_SIZE=4, name='L5PC', cell_args=None, pop_args=None, rotation_args=None)

Create and append a distributed POP_SIZE-sized population of cells of type Cell with the corresponding name. Cell-object references, gids on this RANK, population size POP_SIZE and names will be added to the lists Network.gids, Network.cells, Network.sizes and Network.names, respectively

Parameters

CWD: path

Current working directory

CELLPATH: path

Relative path from CWD to source files for cell model (morphology, hoc routines etc.)

Cell: class

class defining a Cell-like object, see class NetworkCell

POP_SIZE: int

number of cells in population

name: str

population name reference

cell_args: dict

keys and values for Cell object

pop_args: dict

keys and values for Network.draw_rand_pos assigning cell positions

rotation_arg: dict

default cell rotations around x and y axis on the form { ‘x’: np.pi/2, ‘y’: 0 }. Can only have the keys ‘x’ and ‘y’. Cells are randomly rotated around z-axis using the Cell.set_rotation method.

enable_extracellular_stimulation(electrode, t_ext=None, n=1, model='inf')

Enable extracellular stimulation with NEURON’s extracellular mechanism. Extracellular potentials are computed from electrode currents using the point-source approximation. If model is 'inf' (default), potentials are computed as (\(r_i\) is the position of a segment \(i\), \(r_n\) is the position of an electrode \(n\), \(\sigma\) is the conductivity of the medium):

\[V_e(r_i) = \sum_n \frac{I_n}{4 \pi \sigma |r_i - r_n|}\]

If model is 'semi', the method of images is used:

\[V_e(r_i) = \sum_n \frac{I_n}{2 \pi \sigma |r_i - r_n|}\]

Parameters

electrode: RecExtElectrode

Electrode object with stimulating currents

t_ext: np.ndarray or list

Time in ms corresponding to step changes in the provided currents. If None, currents are assumed to have the same time steps as the NEURON simulation.

n: int

Points per electrode for spatial averaging

model: str

'inf' or 'semi'. If 'inf' the medium is assumed to be infinite and homogeneous. If 'semi', the method of images is used.

Returns

v_ext: dict of np.ndarrays

Computed extracellular potentials at cell mid points for each cell of the network’s populations. Formatted as v_ext = {'pop1': np.ndarray[cell, cell_seg,t_ext]}

get_connectivity_rand(pre='L5PC', post='L5PC', connprob=0.2)

Dummy function creating a (boolean) cell to cell connectivity matrix between pre and postsynaptic populations.

Connections are drawn randomly between presynaptic cell gids in population ‘pre’ and postsynaptic cell gids in ‘post’ on this RANK with a fixed connection probability. self-connections are disabled if presynaptic and postsynaptic populations are the same.

Parameters

pre: str

presynaptic population name

post: str

postsynaptic population name

connprob: float in [0, 1]

connection probability, connections are drawn on random

Returns

ndarray, dtype bool

n_pre x n_post array of connections between n_pre presynaptic neurons and n_post postsynaptic neurons on this RANK. Entries with True denotes a connection.

simulate(probes=None, rec_imem=False, rec_vmem=False, rec_ipas=False, rec_icap=False, rec_isyn=False, rec_vmemsyn=False, rec_istim=False, rec_pop_contributions=False, rec_variables=[], variable_dt=False, atol=0.001, to_memory=True, to_file=False, file_name='OUTPUT.h5', **kwargs)

This is the main function running the simulation of the network model.

Parameters

probes: list of :obj:, optional

None or list of LFPykit.RecExtElectrode like object instances that each have a public method get_transformation_matrix returning a matrix that linearly maps each segments’ transmembrane current to corresponding measurement as

\[\mathbf{P} = \mathbf{M} \mathbf{I}\]

rec_imem: bool

If true, segment membrane currents will be recorded If no electrode argument is given, it is necessary to set rec_imem=True in order to calculate LFP later on. Units of (nA).

rec_vmem: bool

record segment membrane voltages (mV)

rec_ipas: bool

record passive segment membrane currents (nA)

rec_icap: bool

record capacitive segment membrane currents (nA)

rec_isyn: bool

record synaptic currents of from Synapse class (nA)

rec_vmemsyn: bool

record membrane voltage of segments with Synapse (mV)

rec_istim: bool

record currents of StimIntraElectrode (nA)

rec_pop_contributions: bool

If True, compute and return single-population contributions to the extracellular potential during simulation time

rec_variables: list of str

variables to record, i.e arg=[‘cai’, ]

variable_dt: boolean

use variable timestep in NEURON. Can not be combimed with to_file

atol: float

absolute tolerance used with NEURON variable timestep

to_memory: bool

Simulate to memory. Only valid with probes=[<probe>, …], which store measurements to -> <probe>.data

to_file: bool

only valid with probes=[<probe>, …], saves measurement in hdf5 file format.

file_name: str

If to_file is True, file which measurements will be written to. The file format is HDF5, default is “OUTPUT.h5”, put in folder Network.OUTPUTPATH

**kwargs: keyword argument dict values passed along to function

__run_simulation_with_probes(), containing some or all of the boolean flags: use_ipas, use_icap, use_isyn (defaulting to False).

Returns

events

Dictionary with keys times and gids, where values are ndarrays with detected spikes and global neuron identifiers

Raises

Exception

if CVode().use_fast_imem() method not found

AssertionError

if rec_pop_contributions==True and probes==None

class NetworkPopulation

class LFPy.NetworkPopulation(CWD=None, CELLPATH=None, first_gid=0, Cell=<class 'LFPy.network.NetworkCell'>, POP_SIZE=4, name='L5PC', cell_args=None, pop_args=None, rotation_args=None, OUTPUTPATH='example_parallel_network')

Bases: object

NetworkPopulation class representing a group of Cell objects distributed across RANKs.

Parameters

CWD: path or None

Current working directory

CELLPATH: path or None

Relative path from CWD to source files for cell model (morphology, hoc routines etc.)

first_gid: int

The global identifier of the first cell created in this population instance. The first_gid in the first population created should be 0 and cannot exist in previously created NetworkPopulation instances

Cell: class

class defining a Cell object, see class NetworkCell above

POP_SIZE: int

number of cells in population

name: str

population name reference

cell_args: dict

keys and values for Cell object

pop_args: dict

keys and values for Network.draw_rand_pos assigning cell positions

rotation_arg: dict

default cell rotations around x and y axis on the form { ‘x’: np.pi/2, ‘y’: 0 }. Can only have the keys ‘x’ and ‘y’. Cells are randomly rotated around z-axis using the Cell.set_rotation() method.

OUTPUTPATH: str

path to output file destination

draw_rand_pos(POP_SIZE, radius, loc, scale, cap=None)

Draw some random location for POP_SIZE cells within radius radius, at mean depth loc and standard deviation scale.

Returned argument is a list of dicts [{‘x’, ‘y’, ‘z’},].

Parameters

POP_SIZE: int

Population size

radius: float

Radius of population.

loc: float

expected mean depth of somas of population.

scale: float

expected standard deviation of depth of somas of population.

cap: None, float or length to list of floats

if float, cap distribution between [loc-cap, loc+cap), if list, cap distribution between [loc-cap[0], loc+cap[1]]

Returns

soma_pos: list

List of dicts of len POP_SIZE where dict have keys x, y, z specifying xyz-coordinates of cell at list entry i.

Forward models

class CurrentDipoleMoment

class LFPy.CurrentDipoleMoment(cell)

Bases: LinearModel

LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding current dipole moment \(\mathbf{P}\) (nA µm) [1] as

\[\mathbf{P} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and the rows of \(\mathbf{P}\) represent the x-, y- and z-components of the current diple moment for every time step.

The current dipole moment can be used to compute distal measures of neural activity such as the EEG and MEG using lfpykit.eegmegcalc.FourSphereVolumeConductor or lfpykit.eegmegcalc.MEG, respectively

Parameters

cell: object

CellGeometry instance or similar.

See also

LinearModel

eegmegcalc.FourSphereVolumeConductor

eegmegcalc.MEG

eegmegcalc.NYHeadModel

References

1

H. Lindén, K. H. Pettersen, G. T. Einevoll (2010). Intrinsic dendritic filtering gives low-pass power spectra of local field potentials. J Comput Neurosci, 29:423–444. DOI: 10.1007/s10827-010-0245-4

Examples

Compute the current dipole moment of a 3-compartment neuron model:

>>> import numpy as np
>>> from lfpykit import CellGeometry, CurrentDipoleMoment
>>> n_seg = 3
>>> cell = CellGeometry(x=np.array([[0.]*2]*n_seg),
                        y=np.array([[0.]*2]*n_seg),
                        z=np.array([[1.*x, 1.*(x+1)]
                                    for x in range(n_seg)]),
                        d=np.array([1.]*n_seg))
>>> cdm = CurrentDipoleMoment(cell)
>>> M = cdm.get_transformation_matrix()
>>> imem = np.array([[-1., 1.],
                     [0., 0.],
                     [1., -1.]])
>>> P = M@imem
>>> P
array([[ 0.,  0.],
       [ 0.,  0.],
       [ 2., -2.]])

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (3, n_seg) ndarray

Raises

AttributeError

if cell is None

class PointSourcePotential

class LFPy.PointSourcePotential(cell, x, y, z, sigma=0.3)

Bases: LinearModel

LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding extracellular electric potential \(\mathbf{V}_{ex}\) (mV) as

\[\mathbf{V}_{ex} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{V}_{ex}\) represents the electric potential at each measurement site for every time step.

The elements of \(\mathbf{M}\) are computed as

\[M_{ji} = 1 / (4 \pi \sigma |\mathbf{r}_i - \mathbf{r}_j|)\]

where \(\sigma\) is the electric conductivity of the extracellular medium, \(\mathbf{r}_i\) the midpoint coordinate of segment \(i\) and \(\mathbf{r}_j\) the coordinate of measurement site \(j\) [1], [2].

Assumptions:

• the extracellular conductivity \(\sigma\) is infinite, homogeneous, frequency independent (linear) and isotropic.

• each segment is treated as a point source located at the midpoint between its start and end point coordinate.

• each measurement site \(\mathbf{r}_j = (x_j, y_j, z_j)\) is treated as a point.

• \(|\mathbf{r}_i - \mathbf{r}_j| >= d_i / 2\), where \(d_i\) is the segment diameter.

Parameters

cell: object

CellGeometry instance or similar.

x: ndarray of floats

x-position of measurement sites (µm)

y: ndarray of floats

y-position of measurement sites (µm)

z: ndarray of floats

z-position of measurement sites (µm)

sigma: float > 0

scalar extracellular conductivity (S/m)

See also

LinearModel

LineSourcePotential

RecExtElectrode

References

1

Linden H, Hagen E, Leski S, Norheim ES, Pettersen KH, Einevoll GT (2014) LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Front. Neuroinform. 7:41. doi: 10.3389/fninf.2013.00041

2

Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front. Neuroinform. 12:92. doi: 10.3389/fninf.2018.00092

Examples

Compute the current dipole moment of a 3-compartment neuron model:

>>> import numpy as np
>>> from lfpykit import CellGeometry, PointSourcePotential
>>> n_seg = 3
>>> cell = CellGeometry(x=np.array([[0.]*2]*n_seg),
                        y=np.array([[0.]*2]*n_seg),
                        z=np.array([[10.*x, 10.*(x+1)]
                                    for x in range(n_seg)]),
                        d=np.array([1.]*n_seg))
>>> psp = PointSourcePotential(cell,
                               x=np.ones(10)*10,
                               y=np.zeros(10),
                               z=np.arange(10)*10,
                               sigma=0.3)
>>> M = psp.get_transformation_matrix()
>>> imem = np.array([[-1., 1.],
                     [0., 0.],
                     [1., -1.]])
>>> V_ex = M @ imem
>>> V_ex
array([[-0.01387397,  0.01387397],
       [-0.00901154,  0.00901154],
       [ 0.00901154, -0.00901154],
       [ 0.01387397, -0.01387397],
       [ 0.00742668, -0.00742668],
       [ 0.00409718, -0.00409718],
       [ 0.00254212, -0.00254212],
       [ 0.00172082, -0.00172082],
       [ 0.00123933, -0.00123933],
       [ 0.00093413, -0.00093413]])

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (n_coords, n_seg) ndarray

Raises

AttributeError

if cell is None

class LineSourcePotential

class LFPy.LineSourcePotential(cell, x, y, z, sigma=0.3)

Bases: LinearModel

LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding extracellular electric potential \(\mathbf{V}_{ex}\) (mV) as

\[\mathbf{V}_{ex} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{V}_{ex}\) represents the electric potential at each measurement site for every time step.

The elements of \(\mathbf{M}\) are computed as

\[M_{ji} = \frac{1}{ 4 \pi \sigma L_i } \log \left| \frac{\sqrt{h_{ji}^2+r_{ji}^2}-h_{ji} }{ \sqrt{l_{ji}^2+r_{ji}^2}-l_{ji}} \right|\]

Segment length is denoted \(L_i\), perpendicular distance from the electrode point contact to the axis of the line segment is denoted \(r_{ji}\), longitudinal distance measured from the start of the segment is denoted \(h_{ji}\), and longitudinal distance from the other end of the segment is denoted \(l_{ji}= L_i + h_{ji}\) [1], [2].

Assumptions:

• the extracellular conductivity \(\sigma\) is infinite, homogeneous, frequency independent (linear) and isotropic

• each segment is treated as a straight line source with homogeneous current density between its start and end point coordinate

• each measurement site \(\mathbf{r}_j = (x_j, y_j, z_j)\) is treated as a point

• The minimum distance to a line source is set equal to segment radius.

Parameters

cell: object

CellGeometry instance or similar.

x: ndarray of floats

x-position of measurement sites (µm)

y: ndarray of floats

y-position of measurement sites (µm)

z: ndarray of floats

z-position of measurement sites (µm)

sigma: float > 0

scalar extracellular conductivity (S/m)

See also

LinearModel

PointSourcePotential

RecExtElectrode

References

1

Linden H, Hagen E, Leski S, Norheim ES, Pettersen KH, Einevoll GT (2014) LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Front. Neuroinform. 7:41. doi: 10.3389/fninf.2013.00041

2

Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front. Neuroinform. 12:92. doi: 10.3389/fninf.2018.00092

Examples

Compute the current dipole moment of a 3-compartment neuron model:

>>> import numpy as np
>>> from lfpykit import CellGeometry, LineSourcePotential
>>> n_seg = 3
>>> cell = CellGeometry(x=np.array([[0.]*2]*n_seg),
                        y=np.array([[0.]*2]*n_seg),
                        z=np.array([[10.*x, 10.*(x+1)]
                                    for x in range(n_seg)]),
                        d=np.array([1.]*n_seg))
>>> lsp = LineSourcePotential(cell,
                              x=np.ones(10)*10,
                              y=np.zeros(10),
                              z=np.arange(10)*10,
                              sigma=0.3)
>>> M = lsp.get_transformation_matrix()
>>> imem = np.array([[-1., 1.],
                     [0., 0.],
                     [1., -1.]])
>>> V_ex = M @ imem
>>> V_ex
array([[-0.01343699,  0.01343699],
       [-0.0084647 ,  0.0084647 ],
       [ 0.0084647 , -0.0084647 ],
       [ 0.01343699, -0.01343699],
       [ 0.00758627, -0.00758627],
       [ 0.00416681, -0.00416681],
       [ 0.002571  , -0.002571  ],
       [ 0.00173439, -0.00173439],
       [ 0.00124645, -0.00124645],
       [ 0.0009382 , -0.0009382 ]])

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (n_coords, n_seg) ndarray

Raises

AttributeError

if cell is None

class RecExtElectrode

class LFPy.RecExtElectrode(cell, sigma=0.3, probe=None, x=None, y=None, z=None, N=None, r=None, n=None, contact_shape='circle', method='linesource', verbose=False, seedvalue=None, **kwargs)

Bases: LinearModel

class RecExtElectrode

Main class that represents an extracellular electric recording devices such as a laminar probe.

This class is a LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding extracellular electric potential \(\mathbf{V}_{ex}\) (mV) as

\[\mathbf{V}_{ex} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{V}_{ex}\) represents the electric potential at each measurement site for every time step.

The class differ from PointSourcePotential and LineSourcePotential by:

• supporting anisotropic volume conductors [1]

• supporting probe geometry specifications using MEAutility (https://meautility.readthedocs.io/en/latest/, https://github.com/alejoe91/MEAutility).

• supporting electrode contact points with finite extents [2], [3]

• switching between point- and linesources, and a combined method that assumes that the root element at segment index 0 is spherical.

Parameters

cell: object

CellGeometry instance or similar.

sigma: float or list/ndarray of floats

extracellular conductivity in units of (S/m). A scalar value implies an isotropic extracellular conductivity. If a length 3 list or array of floats is provided, these values corresponds to an anisotropic conductor with conductivities \([\sigma_x,\sigma_y,\sigma_z]\).

probe: MEAutility MEA object or None

MEAutility probe object

x, y, z: ndarray

coordinates or same length arrays of coordinates in units of (µm).

N: None or list of lists

Normal vectors [x, y, z] of each circular electrode contact surface, default None

r: float

radius of each contact surface, default None (µm)

n: int

if N is not None and r > 0, the number of discrete points used to compute the n-point average potential on each circular contact point.

contact_shape: str

‘circle’/’square’ (default ‘circle’) defines the contact point shape If ‘circle’ r is the radius, if ‘square’ r is the side length

method: str

switch between the assumption of ‘linesource’, ‘pointsource’, ‘root_as_point’ to represent each compartment when computing extracellular potentials

verbose: bool

Flag for verbose output, i.e., print more information

seedvalue: int

random seed when finding random position on contact with r > 0

**kwargs:

Additional keyword arguments parsed to RecExtElectrode.lfp_method() which is determined by method parameter.

See also

LinearModel

PointSourcePotential

LineSourcePotential

References

1

Ness, T. V., Chintaluri, C., Potworowski, J., Leski, S., Glabska, H., Wójcik, D. K., et al. (2015). Modelling and analysis of electrical potentials recorded in microelectrode arrays (MEAs). Neuroinformatics 13:403–426. doi: 10.1007/s12021-015-9265-6

2

Linden H, Hagen E, Leski S, Norheim ES, Pettersen KH, Einevoll GT (2014) LFPy: a tool for biophysical simulation of extracellular potentials generated by detailed model neurons. Front. Neuroinform. 7:41. doi: 10.3389/fninf.2013.00041

3

Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front. Neuroinform. 12:92. doi: 10.3389/fninf.2018.00092

Examples

Mock cell geometry and transmembrane currents:

>>> import numpy as np
>>> from lfpykit import CellGeometry, RecExtElectrode
>>> # cell geometry with three segments (µm)
>>> cell = CellGeometry(x=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     y=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     z=np.array([[0, 10], [10, 20], [20, 30]]),
>>>                     d=np.array([1, 1, 1]))
>>> # transmembrane currents, three time steps (nA)
>>> I_m = np.array([[0., -1., 1.], [-1., 1., 0.], [1., 0., -1.]])
>>> # electrode locations (µm)
>>> r = np.array([[28.24653166, 8.97563241, 18.9492774, 3.47296614,
>>>                1.20517729, 9.59849603, 21.91956616, 29.84686727,
>>>                4.41045505, 3.61146625],
>>>               [24.4954352, 24.04977922, 22.41262238, 10.09702942,
>>>                3.28610789, 23.50277637, 8.14044367, 4.46909208,
>>>                10.93270117, 24.94698813],
>>>               [19.16644585, 15.20196335, 18.08924828, 24.22864702,
>>>                5.85216751, 14.8231048, 24.72666694, 17.77573431,
>>>                29.34508292, 9.28381892]])
>>> # instantiate electrode, get linear response matrix
>>> el = RecExtElectrode(cell=cell, x=r[0, ], y=r[1, ], z=r[2, ],
>>>                      sigma=0.3,
>>>                      method='pointsource')
>>> M = el.get_transformation_matrix()
>>> # compute extracellular potential
>>> M @ I_m
array([[-4.11657148e-05,  4.16621950e-04, -3.75456235e-04],
       [-6.79014892e-04,  7.30256301e-04, -5.12414088e-05],
       [-1.90930536e-04,  7.34007655e-04, -5.43077119e-04],
       [ 5.98270144e-03,  6.73490846e-03, -1.27176099e-02],
       [-1.34547752e-02, -4.65520036e-02,  6.00067788e-02],
       [-7.49957880e-04,  7.03763787e-04,  4.61940938e-05],
       [ 8.69330232e-04,  1.80346156e-03, -2.67279180e-03],
       [-2.04546513e-04,  6.58419628e-04, -4.53873115e-04],
       [ 6.82640209e-03,  4.47953560e-03, -1.13059377e-02],
       [-1.33289553e-03, -1.11818140e-04,  1.44471367e-03]])

Compute extracellular potentials after simulating and storage of transmembrane currents with the LFPy.Cell class:

>>> import os
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import LFPy
>>> from lfpykit import CellGeometry, RecExtElectrode
>>>
>>> cellParameters = {
>>>     'morphology': os.path.join(LFPy.__path__[0], 'test',
>>>                                'ball_and_sticks.hoc'),
>>>     'v_init': -65,                         # initial voltage
>>>     'cm': 1.0,                             # membrane capacitance
>>>     'Ra': 150,                             # axial resistivity
>>>     'passive': True,                       # insert passive channels
>>>     'passive_parameters': {"g_pas":1./3E4,
>>>                            "e_pas":-65}, # passive params
>>>     'dt': 2**-4,                         # simulation time res
>>>     'tstart': 0.,                        # start t of simulation
>>>     'tstop': 50.,                        # end t of simulation
>>> }
>>> cell = LFPy.Cell(**cellParameters)
>>>
>>> synapseParameters = {
>>>     'idx': cell.get_closest_idx(x=0, y=0, z=800), # segment
>>>     'e': 0,                                # reversal potential
>>>     'syntype': 'ExpSyn',                   # synapse type
>>>     'tau': 2,                              # syn. time constant
>>>     'weight': 0.01,                        # syn. weight
>>>     'record_current': True                 # syn. current record
>>> }
>>> synapse = LFPy.Synapse(cell, **synapseParameters)
>>> synapse.set_spike_times(np.array([10., 15., 20., 25.]))
>>>
>>> cell.simulate(rec_imem=True)
>>>
>>> N = np.empty((16, 3))
>>> for i in range(N.shape[0]): N[i,] = [1, 0, 0] # normal vectors
>>> electrodeParameters = {         # parameters for RecExtElectrode class
>>>     'sigma': 0.3,              # Extracellular potential
>>>     'x': np.zeros(16)+25,      # Coordinates of electrode contacts
>>>     'y': np.zeros(16),
>>>     'z': np.linspace(-500,1000,16),
>>>     'n': 20,
>>>     'r': 10,
>>>     'N': N,
>>> }
>>> electrode = RecExtElectrode(cell, **electrodeParameters)
>>> M = electrode.get_transformation_matrix()
>>> V_ex = M @ cell.imem
>>> plt.matshow(V_ex)
>>> plt.colorbar()
>>> plt.axis('tight')
>>> plt.show()

Compute extracellular potentials during simulation:

>>> import os
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import LFPy
>>> from lfpykit import CellGeometry, RecExtElectrode
>>>
>>> cellParameters = {
>>>     'morphology': os.path.join(LFPy.__path__[0], 'test',
>>>                                'ball_and_sticks.hoc'),
>>>     'v_init': -65,                         # initial voltage
>>>     'cm': 1.0,                             # membrane capacitance
>>>     'Ra': 150,                             # axial resistivity
>>>     'passive': True,                       # insert passive channels
>>>     'passive_parameters': {"g_pas":1./3E4,
>>>                            "e_pas":-65}, # passive params
>>>     'dt': 2**-4,                         # simulation time res
>>>     'tstart': 0.,                        # start t of simulation
>>>     'tstop': 50.,                        # end t of simulation
>>> }
>>> cell = LFPy.Cell(**cellParameters)
>>>
>>> synapseParameters = {
>>>     'idx': cell.get_closest_idx(x=0, y=0, z=800), # compartment
>>>     'e': 0,                                # reversal potential
>>>     'syntype': 'ExpSyn',                   # synapse type
>>>     'tau': 2,                              # syn. time constant
>>>     'weight': 0.01,                        # syn. weight
>>>     'record_current': True                 # syn. current record
>>> }
>>> synapse = LFPy.Synapse(cell, **synapseParameters)
>>> synapse.set_spike_times(np.array([10., 15., 20., 25.]))
>>>
>>> N = np.empty((16, 3))
>>> for i in range(N.shape[0]): N[i,] = [1, 0, 0] #normal vec. of contacts
>>> electrodeParameters = {         # parameters for RecExtElectrode class
>>>     'sigma': 0.3,              # Extracellular potential
>>>     'x': np.zeros(16)+25,      # Coordinates of electrode contacts
>>>     'y': np.zeros(16),
>>>     'z': np.linspace(-500,1000,16),
>>>     'n': 20,
>>>     'r': 10,
>>>     'N': N,
>>> }
>>> electrode = RecExtElectrode(cell, **electrodeParameters)
>>> cell.simulate(probes=[electrode])
>>> plt.matshow(electrode.data)
>>> plt.colorbar()
>>> plt.axis('tight')
>>> plt.show()

Use MEAutility to to handle probes

>>> import os
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import MEAutility as mu
>>> import LFPy
>>> from lfpykit import CellGeometry, RecExtElectrode
>>>
>>> cellParameters = {
>>>     'morphology': os.path.join(LFPy.__path__[0], 'test',
>>>                                'ball_and_sticks.hoc'),
>>>     'v_init': -65,                         # initial voltage
>>>     'cm': 1.0,                             # membrane capacitance
>>>     'Ra': 150,                             # axial resistivity
>>>     'passive': True,                       # insert passive channels
>>>     'passive_parameters': {"g_pas":1./3E4,
>>>                            "e_pas":-65}, # passive params
>>>     'dt': 2**-4,                         # simulation time res
>>>     'tstart': 0.,                        # start t of simulation
>>>     'tstop': 50.,                        # end t of simulation
>>> }
>>> cell = LFPy.Cell(**cellParameters)
>>>
>>> synapseParameters = {
>>>     'idx': cell.get_closest_idx(x=0, y=0, z=800), # compartment
>>>     'e': 0,                                # reversal potential
>>>     'syntype': 'ExpSyn',                   # synapse type
>>>     'tau': 2,                              # syn. time constant
>>>     'weight': 0.01,                        # syn. weight
>>>     'record_current': True                 # syn. current record
>>> }
>>> synapse = LFPy.Synapse(cell, **synapseParameters)
>>> synapse.set_spike_times(np.array([10., 15., 20., 25.]))
>>>
>>> cell.simulate(rec_imem=True)
>>>
>>> probe = mu.return_mea('Neuropixels-128')
>>> electrode = RecExtElectrode(cell, probe=probe)
>>> V_ex = electrode.get_transformation_matrix() @ cell.imem
>>> mu.plot_mea_recording(V_ex, probe)
>>> plt.axis('tight')
>>> plt.show()

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (n_contacts, n_seg) ndarray

Raises

AttributeError

if cell is None

class RecMEAElectrode

class LFPy.RecMEAElectrode(cell, sigma_T=0.3, sigma_S=1.5, sigma_G=0.0, h=300.0, z_shift=0.0, steps=20, probe=None, x=array([0]), y=array([0]), z=array([0]), N=None, r=None, n=None, method='linesource', verbose=False, seedvalue=None, squeeze_cell_factor=None, **kwargs)

Bases: RecExtElectrode

class RecMEAElectrode

Electrode class that represents an extracellular in vitro slice recording as a Microelectrode Array (MEA). Inherits RecExtElectrode class

Illustration:

          Above neural tissue (Saline) -> sigma_S
<----------------------------------------------------> z = z_shift + h

          Neural Tissue -> sigma_T

               o -> source_pos = [x',y',z']

<-----------*----------------------------------------> z = z_shift + 0
             \-> elec_pos = [x,y,z]

          Below neural tissue (MEA Glass plate) -> sigma_G

For further details, see reference [1].

Parameters

cell: object

GeometryCell instance or similar.

sigma_T: float

extracellular conductivity of neural tissue in unit (S/m)

sigma_S: float

conductivity of saline bath that the neural slice is immersed in [1.5] (S/m)

sigma_G: float

conductivity of MEA glass electrode plate. Most commonly assumed non-conducting [0.0] (S/m)

h: float, int

Thickness in um of neural tissue layer containing current the current sources (i.e., in vitro slice or cortex)

z_shift: float, int

Height in um of neural tissue layer bottom. If e.g., top of neural tissue layer should be z=0, use z_shift=-h. Defaults to z_shift = 0, so that the neural tissue layer extends from z=0 to z=h.

squeeze_cell_factor: float or None

Factor to squeeze the cell in the z-direction. This is needed for large cells that are thicker than the slice, since no part of the cell is allowed to be outside the slice. The squeeze is done after the neural simulation, and therefore does not affect neuronal simulation, only calculation of extracellular potentials.

probe: MEAutility MEA object or None

MEAutility probe object

x, y, z: np.ndarray

coordinates or arrays of coordinates in units of (um). Must be same length

N: None or list of lists

Normal vectors [x, y, z] of each circular electrode contact surface, default None

r: float

radius of each contact surface, default None

n: int

if N is not None and r > 0, the number of discrete points used to compute the n-point average potential on each circular contact point.

contact_shape: str

‘circle’/’square’ (default ‘circle’) defines the contact point shape If ‘circle’ r is the radius, if ‘square’ r is the side length

method: str

switch between the assumption of ‘linesource’, ‘pointsource’, ‘root_as_point’ to represent each compartment when computing extracellular potentials

verbose: bool

Flag for verbose output, i.e., print more information

seedvalue: int

random seed when finding random position on contact with r > 0

See also

LinearModel

PointSourcePotential

LineSourcePotential

RecExtElectrode

References

1

Ness, T. V., Chintaluri, C., Potworowski, J., Leski, S., Glabska, H., Wójcik, D. K., et al. (2015). Modelling and analysis of electrical potentials recorded in microelectrode arrays (MEAs). Neuroinformatics 13:403–426. doi: 10.1007/s12021-015-9265-6

Examples

Mock cell geometry and transmembrane currents:

>>> import numpy as np
>>> from lfpykit import CellGeometry, RecMEAElectrode
>>> # cell geometry with four segments (µm)
>>> cell = CellGeometry(
>>>     x=np.array([[0, 10], [10, 20], [20, 30], [30, 40]]),
>>>     y=np.array([[0, 0], [0, 0], [0, 0], [0, 0]]),
>>>     z=np.array([[0, 0], [0, 0], [0, 0], [0, 0]]) + 10,
>>>     d=np.array([1, 1, 1, 1]))
>>> # transmembrane currents, three time steps (nA)
>>> I_m = np.array([[0.25, -1., 1.],
>>>                 [-1., 1., -0.25],
>>>                 [1., -0.25, -1.],
>>>                 [-0.25, 0.25, 0.25]])
>>> # electrode locations (µm)
>>> r = np.stack([np.arange(10)*4 + 2, np.zeros(10), np.zeros(10)])
>>> # instantiate electrode, get linear response matrix
>>> el = RecMEAElectrode(cell=cell,
>>>                      sigma_T=0.3, sigma_S=1.5, sigma_G=0.0,
>>>                      x=r[0, ], y=r[1, ], z=r[2, ],
>>>                      method='pointsource')
>>> M = el.get_transformation_matrix()
>>> # compute extracellular potential
>>> M @ I_m
array([[-0.00233572, -0.01990957,  0.02542055],
       [-0.00585075, -0.01520865,  0.02254483],
       [-0.01108601, -0.00243107,  0.01108601],
       [-0.01294584,  0.01013595, -0.00374823],
       [-0.00599067,  0.01432711, -0.01709416],
       [ 0.00599067,  0.01194602, -0.0266944 ],
       [ 0.01294584,  0.00953841, -0.02904238],
       [ 0.01108601,  0.00972426, -0.02324134],
       [ 0.00585075,  0.01075236, -0.01511768],
       [ 0.00233572,  0.01038382, -0.00954429]])

See also <LFPy>/examples/example_MEA.py

>>> import os
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> import LFPy
>>> from lfpykit import CellGeometry, RecMEAElectrode
>>>
>>> cellParameters = {
>>>     'morphology': os.path.join(LFPy.__path__[0], 'test',
>>>                                'ball_and_sticks.hoc'),
>>>     'v_init': -65,                          # initial voltage
>>>     'cm': 1.0,                             # membrane capacitance
>>>     'Ra': 150,                             # axial resistivity
>>>     'passive': True,                        # insert passive channels
>>>     'passive_parameters': {"g_pas":1./3E4,
>>>                            "e_pas":-65}, # passive params
>>>     'dt': 2**-4,                           # simulation time res
>>>     'tstart': 0.,                        # start t of simulation
>>>     'tstop': 50.,                        # end t of simulation
>>> }
>>> cell = LFPy.Cell(**cellParameters)
>>> cell.set_rotation(x=np.pi/2, z=np.pi/2)
>>> cell.set_pos(z=100)
>>> synapseParameters = {
>>>     'idx': cell.get_closest_idx(x=800, y=0, z=100), # segment
>>>     'e': 0,                                # reversal potential
>>>     'syntype': 'ExpSyn',                   # synapse type
>>>     'tau': 2,                              # syn. time constant
>>>     'weight': 0.01,                       # syn. weight
>>>     'record_current': True                 # syn. current record
>>> }
>>> synapse = LFPy.Synapse(cell, **synapseParameters)
>>> synapse.set_spike_times(np.array([10., 15., 20., 25.]))
>>>
>>> MEA_electrode_parameters = {
>>>     'sigma_T': 0.3,      # extracellular conductivity
>>>     'sigma_G': 0.0,      # MEA glass electrode plate conductivity
>>>     'sigma_S': 1.5,      # Saline bath conductivity
>>>     'x': np.linspace(0, 1200, 16),  # 1d vector of positions
>>>     'y': np.zeros(16),
>>>     'z': np.zeros(16),
>>>     "method": "pointsource",
>>>     "h": 300,
>>>     "squeeze_cell_factor": 0.5,
>>> }
>>> cell.simulate(rec_imem=True)
>>>
>>> MEA = RecMEAElectrode(cell, **MEA_electrode_parameters)
>>> V_ext = MEA.get_transformation_matrix() @ lfpy_cell.imem
>>>
>>> plt.matshow(V_ext)
>>> plt.colorbar()
>>> plt.axis('tight')
>>> plt.show()

distort_cell_geometry(axis='z', nu=0.0)

Distorts cellular morphology with a relative squeeze_cell_factor along a chosen axis preserving Poisson’s ratio. A ratio nu=0.5 assumes uncompressible and isotropic media that embeds the cell. A ratio nu=0 will only affect geometry along the chosen axis. A ratio nu=-1 will isometrically scale the neuron geometry along each axis. This method does not affect the underlying cable properties of the cell, only predictions of extracellular measurements (by affecting the relative locations of sources representing the compartments).

Parameters

axis: str

which axis to apply compression/stretching. Default is “z”.

nu: float

Poisson’s ratio. Ratio between axial and transversal compression/stretching. Default is 0.

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (n_contacts, n_seg) ndarray

Raises

AttributeError

if cell is None

class OneSphereVolumeConductor

class LFPy.OneSphereVolumeConductor(cell, r, R=10000.0, sigma_i=0.3, sigma_o=0.03)

Bases: LinearModel

Computes extracellular potentials within and outside a spherical volume- conductor model that assumes homogeneous, isotropic, linear (frequency independent) conductivity in and outside the sphere with a radius R. The conductivity in and outside the sphere must be greater than 0, and the current source(s) must be located within the radius R.

The implementation is based on the description of electric potentials of point charge in an dielectric sphere embedded in dielectric media [1], which is mathematically equivalent to a current source in conductive media.

This class is a LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding extracellular electric potential \(\mathbf{V}_{ex}\) (mV) as

\[\mathbf{V}_{ex} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{V}_{ex}\) represents the electric potential at each measurement site for every time step.

Parameters

cell: object or None

CellGeometry instance or similar.

r: ndarray, dtype=float

shape(3, n_points) observation points in space in spherical coordinates (radius, theta, phi) relative to the center of the sphere.

R: float

sphere radius (µm)

sigma_i: float

electric conductivity for radius r <= R (S/m)

sigma_o: float

electric conductivity for radius r > R (S/m)

References

1

Shaozhong Deng (2008), Journal of Electrostatics 66:549-560. DOI: 10.1016/j.elstat.2008.06.003

Examples

Compute the potential for a single monopole along the x-axis:

>>> # import modules
>>> from lfpykit import OneSphereVolumeConductor
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> # observation points in spherical coordinates (flattened)
>>> X, Y = np.mgrid[-15000:15100:1000., -15000:15100:1000.]
>>> r = np.array([np.sqrt(X**2 + Y**2).flatten(),
>>>               np.arctan2(Y, X).flatten(),
>>>               np.zeros(X.size)])
>>> # set up class object and compute electric potential in all locations
>>> sphere = OneSphereVolumeConductor(cell=None, r=r, R=10000.,
>>>                                   sigma_i=0.3, sigma_o=0.03)
>>> Phi = sphere.calc_potential(rs=8000, current=1.).reshape(X.shape)
>>> # plot
>>> fig, ax = plt.subplots(1,1)
>>> im=ax.contourf(X, Y, Phi,
>>>                levels=np.linspace(Phi.min(),
>>>                np.median(Phi[np.isfinite(Phi)]) * 4, 30))
>>> circle = plt.Circle(xy=(0,0), radius=sphere.R, fc='none', ec='k')
>>> ax.add_patch(circle)
>>> fig.colorbar(im, ax=ax)
>>> plt.show()

calc_potential(rs, current, min_distance=1.0, n_max=1000)

Return the electric potential at observation points for source current as function of time.

Parameters

rs: float

monopole source location along the horizontal x-axis (µm)

current: float or ndarray, dtype float

float or shape (n_tsteps, ) array containing source current (nA)

min_distance: None or float

minimum distance between source location and observation point (µm) (in order to avoid singularities)

n_max: int

Number of elements in polynomial expansion to sum over (see [1]).

Returns

Phi: ndarray

shape (n-points, ) ndarray of floats if I is float like. If I is an 1D ndarray, and shape (n-points, I.size) ndarray is returned. Unit (mV).

References

1

Shaozhong Deng (2008), Journal of Electrostatics 66:549-560. DOI: 10.1016/j.elstat.2008.06.003

get_transformation_matrix(n_max=1000)

Compute linear mapping between transmembrane currents of CellGeometry like object instance and extracellular potential in and outside of sphere.

Parameters

n_max: int

Number of elements in polynomial expansion to sum over (see [1]).

Returns

ndarray

Shape (n_points, n_compartments) mapping between individual segments and extracellular potential in extracellular locations

Raises

AttributeError

if cell is None

Notes

Each segment is treated as a point source in space. The minimum source to measurement site distance will be set to the diameter of each segment

References

1

Shaozhong Deng (2008), Journal of Electrostatics 66:549-560. DOI: 10.1016/j.elstat.2008.06.003

Examples

Compute extracellular potential in one-sphere volume conductor model from LFPy.Cell object:

>>> # import modules
>>> import LFPy
>>> from lfpykit import CellGeometry,         >>>     OneSphereVolumeConductor
>>> import os
>>> import numpy as np
>>> import matplotlib.pyplot as plt
>>> from matplotlib.collections import PolyCollection
>>> # create cell
>>> cell = LFPy.Cell(morphology=os.path.join(LFPy.__path__[0], 'test',
>>>                                          'ball_and_sticks.hoc'),
>>>                  tstop=10.)
>>> cell.set_pos(z=9800.)
>>> # stimulus
>>> syn = LFPy.Synapse(cell, idx=cell.totnsegs-1, syntype='Exp2Syn',
>>>                    weight=0.01)
>>> syn.set_spike_times(np.array([1.]))
>>> # simulate
>>> cell.simulate(rec_imem=True)
>>> # observation points in spherical coordinates (flattened)
>>> X, Z = np.mgrid[-500:501:10., 9500:10501:10.]
>>> Y = np.zeros(X.shape)
>>> r = np.array([np.sqrt(X**2 + Z**2).flatten(),
>>>               np.arccos(Z / np.sqrt(X**2 + Z**2)).flatten(),
>>>               np.arctan2(Y, X).flatten()])
>>> # set up class object and compute mapping between segment currents
>>> # and electric potential in space
>>> sphere = OneSphereVolumeConductor(cell, r=r, R=10000.,
>>>                                   sigma_i=0.3, sigma_o=0.03)
>>> M = sphere.get_transformation_matrix(n_max=1000)
>>> # pick out some time index for the potential and compute potential
>>> ind = cell.tvec==2.
>>> V_ex = (M @ cell.imem)[:, ind].reshape(X.shape)
>>> # plot potential
>>> fig, ax = plt.subplots(1,1)
>>> zips = []
>>> for x, z in cell.get_idx_polygons(projection=('x', 'z')):
>>>     zips.append(list(zip(x, z)))
>>> polycol = PolyCollection(zips,
>>>                          edgecolors='none',
>>>                          facecolors='gray')
>>> vrange = 1E-3 # limits for color contour plot
>>> im=ax.contour(X, Z, V_ex,
>>>              levels=np.linspace(-vrange, vrange, 41))
>>> circle = plt.Circle(xy=(0,0), radius=sphere.R, fc='none', ec='k')
>>> ax.add_collection(polycol)
>>> ax.add_patch(circle)
>>> ax.axis(ax.axis('equal'))
>>> ax.set_xlim(X.min(), X.max())
>>> ax.set_ylim(Z.min(), Z.max())
>>> fig.colorbar(im, ax=ax)
>>> plt.show()

Current Dipole Moment forward models

class InfiniteVolumeConductor

class LFPy.InfiniteVolumeConductor(sigma=0.3)

Bases: InfiniteVolumeConductor

Main class for computing extracellular potentials with current dipole moment \(\mathbf{P}\) in an infinite 3D volume conductor model that assumes homogeneous, isotropic, linear (frequency independent) conductivity \(\sigma\). The potential \(V\) is computed as [1]:

\[V = \frac{\mathbf{P} \cdot \mathbf{r}}{4 \pi \sigma r^3}\]

Parameters

sigma: float

Electrical conductivity in extracellular space in units of (S/cm)

See also

FourSphereVolumeConductor

MEG

References

1

Nunez and Srinivasan, Oxford University Press, 2006

Examples

Computing the potential from dipole moment valid in the far field limit. Theta correspond to the dipole alignment angle from the vertical z-axis:

>>> from lfpykit.eegmegcalc import InfiniteVolumeConductor
>>> import numpy as np
>>> inf_model = InfiniteVolumeConductor(sigma=0.3)
>>> p = np.array([[10.], [10.], [10.]])  # [nA µm]
>>> r = np.array([[1000., 0., 5000.]])  # [µm]
>>> inf_model.get_dipole_potential(p, r)  # [mV]
array([[1.20049432e-07]])

get_dipole_potential(p, r)

Return electric potential from current dipole moment p in locations r relative to dipole

Parameters

p: ndarray, dtype=float

Shape (3, n_timesteps) array containing the x,y,z components of the current dipole moment in units of (nA*µm) for all timesteps

r: ndarray, dtype=float

Shape (n_contacts, 3) array containing the displacement vectors from dipole location to measurement location

Returns

potential: ndarray, dtype=float

Shape (n_contacts, n_timesteps) array containing the electric potential at contact point(s) r in units of (mV) for all timesteps of current dipole moment p

get_multi_dipole_potential(cell, electrode_locs, timepoints=None)

Return electric potential from multiple current dipoles from cell

The multiple current dipoles corresponds to dipoles computed from all axial currents in a neuron simulation, typically two axial currents per segment, excluding the root segment.

Parameters

cell: LFPy.Cell object

electrode_locs: ndarray, dtype=float

Shape (n_contacts, 3) array containing n_contacts electrode locations in cartesian coordinates in units of [µm]. All r_el in electrode_locs must be placed so that |r_el| is less than or equal to scalp radius and larger than the distance between dipole and sphere center: |rz| < |r_el| <= radii[3].

timepoints: ndarray, dtype=int

array of timepoints at which you want to compute the electric potential. Defaults to None. If not given, all simulation timesteps will be included.

Returns

potential: ndarray, dtype=float

Shape (n_contacts, n_timesteps) array containing the electric potential at contact point(s) electrode_locs in units of [mV] for all timesteps of neuron simulation

Examples

Compute extracellular potential from neuron simulation in four-sphere head model. Instead of simplifying the neural activity to a single dipole, we compute the contribution from every multi dipole from all axial currents in neuron simulation:

>>> import LFPy
>>> from lfpykit.eegmegcalc import InfiniteVolumeConductor
>>> import numpy as np
>>> cell = LFPy.Cell('PATH/TO/MORPHOLOGY', extracellular=False)
>>> syn = LFPy.Synapse(cell, idx=cell.get_closest_idx(0,0,100),
>>>                   syntype='ExpSyn', e=0., tau=1., weight=0.001)
>>> syn.set_spike_times(np.mgrid[20:100:20])
>>> cell.simulate(rec_vmem=True, rec_imem=False)
>>> sigma = 0.3
>>> timepoints = np.array([10, 20, 50, 100])
>>> electrode_locs = np.array([[50., -50., 250.]])
>>> MD_INF = InfiniteVolumeConductor(sigma)
>>> phi = MD_INF.get_multi_dipole_potential(cell, electrode_locs,
>>>                                         timepoints = timepoints)

get_transformation_matrix(r)

Get linear response matrix mapping current dipole moment in (nA µm) to extracellular potential in (mV) at recording sites r (µm)

Parameters

r: ndarray, dtype=float

Shape (n_contacts, 3) array contaning the displacement vectors from dipole location to measurement location (µm)

Returns

response_matrix: ndarray

shape (n_contacts, 3) ndarray

class FourSphereVolumeConductor

class LFPy.FourSphereVolumeConductor(r_electrodes, radii=None, sigmas=None, iter_factor=2.0202020202020204e-08)

Bases: FourSphereVolumeConductor

Main class for computing extracellular potentials in a four-sphere volume conductor model that assumes homogeneous, isotropic, linear (frequency independent) conductivity within the inner sphere and outer shells. The conductance outside the outer shell is 0 (air).

This class implements the corrected 4-sphere model described in [1], [2]

Parameters

r_electrodes: ndarray, dtype=float

Shape (n_contacts, 3) array containing n_contacts electrode locations in cartesian coordinates in units of (µm). All r_el in r_electrodes must be less than or equal to scalp radius and larger than the distance between dipole and sphere center: |rz| < r_el <= radii[3].

radii: list, dtype=float

Len 4 list with the outer radii in units of (µm) for the 4 concentric shells in the four-sphere model: brain, csf, skull and scalp, respectively.

sigmas: list, dtype=float

Len 4 list with the electrical conductivity in units of (S/m) of the four shells in the four-sphere model: brain, csf, skull and scalp, respectively.

iter_factor: float

iteration-stop factor

See also

InfiniteVolumeConductor

MEG

References

1

Næss S, Chintaluri C, Ness TV, Dale AM, Einevoll GT and Wójcik DK (2017) Corrected Four-sphere Head Model for EEG Signals. Front. Hum. Neurosci. 11:490. doi: 10.3389/fnhum.2017.00490

2

Hagen E, Næss S, Ness TV and Einevoll GT (2018) Multimodal Modeling of Neural Network Activity: Computing LFP, ECoG, EEG, and MEG Signals With LFPy 2.0. Front. Neuroinform. 12:92. doi: 10.3389/fninf.2018.00092

Examples

Compute extracellular potential from current dipole moment in four-sphere head model:

>>> from lfpykit.eegmegcalc import FourSphereVolumeConductor
>>> import numpy as np
>>> radii = [79000., 80000., 85000., 90000.]  # (µm)
>>> sigmas = [0.3, 1.5, 0.015, 0.3]  # (S/m)
>>> r_electrodes = np.array([[0., 0., 90000.], [0., 85000., 0.]]) # (µm)
>>> sphere_model = FourSphereVolumeConductor(r_electrodes, radii,
>>>                                          sigmas)
>>> # current dipole moment
>>> p = np.array([[10.]*10, [10.]*10, [10.]*10]) # 10 timesteps (nA µm)
>>> dipole_location = np.array([0., 0., 78000.])  # (µm)
>>> # compute potential
>>> sphere_model.get_dipole_potential(p, dipole_location)  # (mV)
array([[1.06247669e-08, 1.06247669e-08, 1.06247669e-08, 1.06247669e-08,
        1.06247669e-08, 1.06247669e-08, 1.06247669e-08, 1.06247669e-08,
        1.06247669e-08, 1.06247669e-08],
       [2.39290752e-10, 2.39290752e-10, 2.39290752e-10, 2.39290752e-10,
        2.39290752e-10, 2.39290752e-10, 2.39290752e-10, 2.39290752e-10,
        2.39290752e-10, 2.39290752e-10]])

get_dipole_potential(p, dipole_location)

Return electric potential from current dipole moment p in location dipole_location in locations r_electrodes

Parameters

p: ndarray, dtype=float

Shape (3, n_timesteps) array containing the x,y,z components of the current dipole moment in units of (nA*µm) for all timesteps.

dipole_location: ndarray, dtype=float

Shape (3, ) array containing the position of the current dipole in cartesian coordinates. Units of (µm).

Returns

potential: ndarray, dtype=float

Shape (n_contacts, n_timesteps) array containing the electric potential at contact point(s) FourSphereVolumeConductor.rxyz in units of (mV) for all timesteps of current dipole moment p.

get_dipole_potential_from_multi_dipoles(cell, timepoints=None)

Return electric potential from multiple current dipoles from cell.

By multiple current dipoles we mean the dipoles computed from all axial currents in a neuron simulation, typically two axial currents per segment, except for the root segment.

Parameters

cell: LFPy Cell object, LFPy.Cell

timepoints: ndarray, dtype=int

array of timepoints at which you want to compute the electric potential. Defaults to None. If not given, all simulation timesteps will be included.

Returns

potential: ndarray, dtype=float

Shape (n_contacts, n_timesteps) array containing the electric potential at contact point(s) electrode_locs in units of [mV] for all timesteps of neuron simulation.

Examples

Compute extracellular potential from neuron simulation in four-sphere head model. Instead of simplifying the neural activity to a single dipole, we compute the contribution from every multi dipole from all axial currents in neuron simulation:

>>> import os
>>> import LFPy
>>> from LFPy import FourSphereVolumeConductor
>>> import numpy as np
>>> cell = LFPy.Cell(os.path.join(LFPy.__path__[0], 'test',
>>>                               'ball_and_sticks.hoc'),
>>>                  v_init=-65, cm=1., Ra=150,
>>>                  passive=True,
>>>                  passive_parameters=dict(g_pas=1/1E4, e_pas=-65))
>>> syn = LFPy.Synapse(cell, idx=cell.get_closest_idx(0,0,100),
>>>                    syntype='ExpSyn', e=0., tau=1., weight=0.001)
>>> syn.set_spike_times(np.mgrid[20:100:20])
>>> cell.simulate(rec_vmem=True, rec_imem=False)
>>> cell.set_pos(0, 0, 78800)
>>> radii = [79000., 80000., 85000., 90000.]
>>> sigmas = [0.3, 1.5, 0.015, 0.3]
>>> r_electrodes = np.array([[0., 0., 90000.]])
>>> MD_4s = FourSphereVolumeConductor(r_electrodes=r_electrodes,
>>>                                   radii=radii,
>>>                                   sigmas=sigmas)
>>> phi = MD_4s.get_dipole_potential_from_multi_dipoles(cell)

get_transformation_matrix(dipole_location)

Get linear response matrix mapping current dipole moment in (nA µm) located in location rz to extracellular potential in (mV) at recording sites FourSphereVolumeConductor.rxyz (µm)

Parameters

dipole_location: ndarray, dtype=float

Shape (3, ) array containing the position of the current dipole in cartesian coordinates. Units of (µm).

Returns

response_matrix: ndarray

shape (n_contacts, 3) ndarray

class NYHeadModel

class LFPy.NYHeadModel(nyhead_file=None)

Bases: Model

Main class for computing EEG signals from current dipole moment \(\mathbf{P}\) in New York Head Model [1], [2]

Assumes units of nA * um for current dipole moment, and mV for the EEG

Parameters

nyhead_file: str [optional]

Location of file containing New York Head Model. If empty (or None), it will be looked for in the present working directory. If not present the user is asked if it should be downloaded from https://www.parralab.org/nyhead/sa_nyhead.mat

See also

FourSphereVolumeConductor

MEG

Notes

The original unit of the New York model current dipole moment is (probably?) mA*m, and the EEG output unit is V. LFPykit’s current dipole moments have units nA*um, and EEGs from the NYhead model is here recomputed in units of mV.

References

1

Huang, Parra, Haufe (2016) The New York Head—A precise standardized volume conductor model for EEG source localization and tES targeting. Neuroimage 140:150–162. doi: 10.1016/j.neuroimage.2015.12.019

2

Naess et al. (2020) Biophysical modeling of the neural origin of EEG and MEG signals. bioRxiv 2020.07.01.181875. doi: 10.1101/2020.07.01.181875

Examples

Computing EEG from dipole moment.

>>> from lfpykit.eegmegcalc import NYHeadModel

>>> nyhead = NYHeadModel()

>>> nyhead.set_dipole_pos('parietal_lobe') # predefined example location
>>> M = nyhead.get_transformation_matrix()

>>> # Rotate to be along normal vector of cortex
>>> p = nyhead.rotate_dipole_to_surface_normal([[0.], [0.], [1.]])
>>> eeg = M @ p  # (mV)

find_closest_electrode()

Returns minimal distance (mm) and closest electrode idx to dipole location specified in self.dipole_pos.

get_transformation_matrix()

Get linear response matrix mapping from current dipole moment (nA µm) to EEG signal (mV) at EEG electrodes (n=231)

Returns

response_matrix: ndarray

shape (231, 3) ndarray

return_closest_idx(pos)

Returns the index of the closest vertex in the brain to a given position (in mm).

Parameters

posarray of length (3)

[x, y, z] of a location in the brain, given in mm, and not in um which is the default position unit in LFPy

Returns

——-

idxint

Index of the vertex in the brain that is closest to the given location

rotate_dipole_to_surface_normal(p, orig_ax_vec=[0, 0, 1])

Returns rotated dipole moment, p_rot, oriented along the normal vector of the cortex at the dipole location

Parameters

pnp.ndarray of size (3, num_timesteps)

Current dipole moment from neural simulation, [p_x(t), p_y(t), p_z(t)]. If z-axis is the depth axis of cortex in the original neural simulation p_x(t) and p_y(t) will typically be small, and orig_ax_vec = [0, 0, 1].

orig_ax_vecnp.ndarray or list of length (3)

Original surface vector of cortex in the neural simulation. If depth axis of cortex is the z-axis, orig_ax_vec = [0, 0, 1].

Returns

p_rotnp.ndarray of size (3, num_timesteps)

Rotated current dipole moment, oriented along cortex normal vector at the dipole location

References

See: https://en.wikipedia.org/wiki/Rotation_matrix under “Rotation matrix from axis and angle”

set_dipole_pos(dipole_pos=None)

Sets the dipole location in the brain

Parameters

dipole_pos: None, str or array of length (3) [x, y, z) (mm)

Location of the dipole. If no argument is given (or dipole_pos=None), a location, ‘motorsensory_cortex’, from self.dipole_pos_dict is used. If dipole_pos is an array of length 3, the closest vertex in the brain will be set as the dipole location.

class InfiniteHomogeneousVolCondMEG

class LFPy.InfiniteHomogeneousVolCondMEG(sensor_locations, mu=1.2566370614359173e-06)

Bases: InfiniteHomogeneousVolCondMEG

Basic class for computing magnetic field from current dipole moment. For this purpose we use the Biot-Savart law derived from Maxwell’s equations under the assumption of negligible magnetic induction effects [1]:

\[\mathbf{H} = \frac{\mathbf{p} \times \mathbf{R}}{4 \pi R^3}\]

where \(\mathbf{p}\) is the current dipole moment, \(\mathbf{R}\) the vector between dipole source location and measurement location, and \(R=|\mathbf{R}|\)

Note that the magnetic field \(\mathbf{H}\) is related to the magnetic field \(\mathbf{B}\) as

\[\mu_0 \mathbf{H} = \mathbf{B}-\mathbf{M}\]

where \(\mu_0\) is the permeability of free space (very close to permebility of biological tissues). \(\mathbf{M}\) denotes material magnetization (also ignored)

Parameters

sensor_locations: ndarray, dtype=float

shape (n_locations x 3) array with x,y,z-locations of measurement devices where magnetic field of current dipole moments is calculated. In unit of [µm]

mu: float

Permeability. Default is permeability of vacuum (\(\mu_0 = 4*\pi*10^{-7}\) T*m/A)

Raises

AssertionError

If dimensionality of sensor_locations is wrong

See also

FourSphereVolumeConductor

InfiniteVolumeConductor

References

1

Nunez and Srinivasan, Oxford University Press, 2006

Examples

Define cell object, create synapse, compute current dipole moment:

>>> import LFPy, os, numpy as np, matplotlib.pyplot as plt
>>> from LFPy import InfiniteHomogeneousVolCondMEG as MEG
>>> from LFPy import CurrentDipoleMoment
>>> # create LFPy.Cell object
>>> cell = LFPy.Cell(morphology=os.path.join(LFPy.__path__[0], 'test',
>>>                                          'ball_and_sticks.hoc'),
>>>                  passive=True)
>>> cell.set_pos(0., 0., 0.)
>>> # create single synaptic stimuli at soma (idx=0)
>>> syn = LFPy.Synapse(cell, idx=0, syntype='ExpSyn', weight=0.01, tau=5,
>>>                    record_current=True)
>>> syn.set_spike_times_w_netstim()
>>> # simulate, record current dipole moment
>>> cdm = CurrentDipoleMoment(cell=cell)
>>> cell.simulate(probes=[cdm])
>>> # Compute the dipole location as an average of segment locations
>>> # weighted by membrane area:
>>> dipole_location = (cell.area * np.c_[cell.x.mean(axis=1),
>>>                                      cell.y.mean(axis=1),
>>>                                      cell.z.mean(axis=1)].T
>>>                    / cell.area.sum()).sum(axis=1)
>>> # Define sensor site, instantiate MEG object, get transformation matrix
>>> sensor_locations = np.array([[1E4, 0, 0]])
>>> meg = MEG(sensor_locations)
>>> M = meg.get_transformation_matrix(dipole_location)
>>> # compute the magnetic signal in a single sensor location:
>>> H = M @ cdm.data
>>> # plot output
>>> plt.figure(figsize=(12, 8), dpi=120)
>>> plt.subplot(311)
>>> plt.plot(cell.tvec, cell.somav)
>>> plt.ylabel(r'$V_{soma}$ (mV)')
>>> plt.subplot(312)
>>> plt.plot(cell.tvec, syn.i)
>>> plt.ylabel(r'$I_{syn}$ (nA)')
>>> plt.subplot(313)
>>> plt.plot(cell.tvec, H[0].T)
>>> plt.ylabel(r'$H$ (nA/um)')
>>> plt.xlabel('$t$ (ms)')
>>> plt.legend(['$H_x$', '$H_y$', '$H_z$'])
>>> plt.show()

calculate_B(p, r_p)

Compute magnetic field B from single current dipole p localized somewhere in space at r_p.

This function returns the magnetic field \(\mathbf{B}=µ\mathbf{H}\).

Parameters

p: ndarray, dtype=float

shape (3, n_timesteps) array with x,y,z-components of current- dipole moment time series data in units of (nA µm)

r_p: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{B}\) in units of (nA/µm)

calculate_H(current_dipole_moment, dipole_location)

Compute magnetic field H from single current-dipole moment localized in an infinite homogeneous volume conductor.

Parameters

current_dipole_moment: ndarray, dtype=float

shape (3, n_timesteps) array with x,y,z-components of current- dipole moment time series data in units of (nA µm)

dipole_location: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{H}\) in units of (nA/µm)

Raises

AssertionError

If dimensionality of current_dipole_moment and/or dipole_location is wrong

calculate_H_from_iaxial(cell)

Computes the magnetic field in space from axial currents computed from membrane potential values and axial resistances of multicompartment cells.

See [1] for details on the biophysics governing magnetic fields from axial currents.

Parameters

cell: object

LFPy.Cell-like object. Must have attribute vmem containing recorded membrane potentials in units of mV

Returns

H: ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{H}\) in units of (nA/µm)

References

1

Blagoev et al. (2007) Modelling the magnetic signature of neuronal tissue. NeuroImage 37 (2007) 137–148 DOI: 10.1016/j.neuroimage.2007.04.033

Examples

Define cell object, create synapse, compute current dipole moment:

>>> import LFPy, os, numpy as np, matplotlib.pyplot as plt
>>> from LFPy import InfiniteHomogeneousVolCondMEG as MEG
>>> cell = LFPy.Cell(morphology=os.path.join(LFPy.__path__[0], 'test',
>>>                                          'ball_and_sticks.hoc'),
>>>                  passive=True)
>>> cell.set_pos(0., 0., 0.)
>>> syn = LFPy.Synapse(cell, idx=0, syntype='ExpSyn', weight=0.01,
>>>                    record_current=True)
>>> syn.set_spike_times_w_netstim()
>>> cell.simulate(rec_vmem=True)
>>> # Instantiate the MEG object, compute and plot the magnetic
>>> # signal in a sensor location:
>>> sensor_locations = np.array([[1E4, 0, 0]])
>>> meg = MEG(sensor_locations)
>>> H = meg.calculate_H_from_iaxial(cell)
>>> plt.subplot(311)
>>> plt.plot(cell.tvec, cell.somav)
>>> plt.subplot(312)
>>> plt.plot(cell.tvec, syn.i)
>>> plt.subplot(313)
>>> plt.plot(cell.tvec, H[0, 1, :])  # y-component
>>> plt.show()

get_transformation_matrix(dipole_location)

Get linear response matrix mapping current dipole moment in (nA µm) located in location dipole_location to magnetic field \(\mathbf{H}\) in units of (nA/µm) at sensor_locations

Parameters

dipole_location: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

response_matrix: ndarray

shape (n_contacts, 3, 3) ndarray

class SphericallySymmetricVolCondMEG

class LFPy.SphericallySymmetricVolCondMEG(r, mu=1.2566370614359173e-06)

Bases: SphericallySymmetricVolCondMEG

Computes magnetic fields from current dipole in spherically-symmetric volume conductor models.

This class facilitates calculations according to eq. (34) from [1] (see also [2]) defined as:

\[ \begin{align}\begin{aligned}\mathbf{H} = \frac{1}{4 \pi} \frac{F \mathbf{p} \times \mathbf{r}_p - (\mathbf{p} \times \mathbf{r}_p \cdot \mathbf{r}) \nabla F}{F^2}, \text{ where}\\F = a(ra + r^2 - \mathbf{r}_p \cdot \mathbf{r}),\\\nabla F = (r^{-1}a^2 + a^{-1}\mathbf{a} \cdot \mathbf{r} + 2a + 2r)\mathbf{r} -(a + 2r + a^{-1}\mathbf{a} \cdot \mathbf{r})\mathbf{r}_p,\\\mathbf{a} = \mathbf{r} - \mathbf{r}_p,\\a = |\mathbf{a}|,\\r = |\mathbf{r}| .\end{aligned}\end{align} \]

Here, \(\mathbf{p}\) is the current dipole moment, \(\mathbf{r}\) the measurement location(s) and \(\mathbf{r}_p\) the current dipole location.

Note that the magnetic field \(\mathbf{H}\) is related to the magnetic field \(\mathbf{B}\) as

\[\mu_0 \mathbf{H} = \mathbf{B}-\mathbf{M} ,\]

where \(\mu_0\) denotes the permeability of free space (very close to permebility of biological tissues). \(\mathbf{M}\) denotes material magnetization (which is ignored).

Parameters

r: ndarray

sensor locations, shape (n, 3) where n denotes number of locations, unit [µm]

mu: float

Permeability. Default is permeability of vacuum (\(\mu_0 = 4\pi 10^{-7}\) Tm/A)

Raises

AssertionError

If dimensionality of sensor locations r is wrong

See also

InfiniteHomogeneousVolCondMEG

References

1

Hämäläinen M., et al., Reviews of Modern Physics, Vol. 65, No. 2, April 1993.

2

Sarvas J., Phys.Med. Biol., 1987, Vol. 32, No 1, 11-22.

Examples

Compute the magnetic field from a current dipole located

>>> import numpy as np
>>> from LFPy import SphericallySymmetricVolCondMEG
>>> p = np.array([[0, 1, 0]]).T  # tangential current dipole (nAµm)
>>> r_p = np.array([0, 0, 90000])  # dipole location (µm)
>>> r = np.array([[0, 0, 92000]])  # measurement location (µm)
>>> m = SphericallySymmetricVolCondMEG(r=r)
>>> M = m.get_transformation_matrix(r_p=r_p)
>>> H = M @ p
>>> H  # magnetic field (nA/µm)
array([[[9.73094081e-09],
        [0.00000000e+00],
        [0.00000000e+00]]])

calculate_B(p, r_p)

Compute magnetic field B from single current dipole p localized somewhere in space at r_p.

This function returns the magnetic field \(\mathbf{B}=µ\mathbf{H}\).

Parameters

p: ndarray, dtype=float

shape (3, n_timesteps) array with x,y,z-components of current- dipole moment time series data in units of (nA µm)

r_p: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{B}\) in units of (nA/µm)

calculate_H(p, r_p)

Compute magnetic field \(\mathbf{H}\) from single current dipole p localized somewhere in space at r_p

Parameters

p: ndarray, dtype=float

shape (3, n_timesteps) array with x,y,z-components of current- dipole moment time series data in units of (nA µm)

r_p: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{H}\) in units of (nA/µm)

Raises

AssertionError

If dimensionality of current_dipole_moment p and/or dipole_location r_p is wrong

calculate_H_from_iaxial(cell)

Computes the magnetic field in space from axial currents computed from membrane potential values and axial resistances of multicompartment cells.

See [1] for details on the biophysics governing magnetic fields from axial currents.

Parameters

cell: object

LFPy.Cell-like object. Must have attribute vmem containing recorded membrane potentials in units of mV

Returns

H: ndarray, dtype=float

shape (n_locations x 3 x n_timesteps) array with x,y,z-components of the magnetic field \(\mathbf{H}\) in units of (nA/µm)

References

1

Blagoev et al. (2007) Modelling the magnetic signature of neuronal tissue. NeuroImage 37 (2007) 137–148 DOI: 10.1016/j.neuroimage.2007.04.033

Examples

Define cell object, create synapse, compute current dipole moment:

>>> import LFPy, os, numpy as np, matplotlib.pyplot as plt
>>> from LFPy import SphericallySymmetricVolCondMEG as MEG
>>> cell = LFPy.Cell(morphology=os.path.join(LFPy.__path__[0], 'test',
>>>                                          'ball_and_sticks.hoc'),
>>>                  passive=True)
>>> cell.set_pos(0., 0., 0.)
>>> syn = LFPy.Synapse(cell, idx=0, syntype='ExpSyn', weight=0.01,
>>>                    record_current=True)
>>> syn.set_spike_times_w_netstim()
>>> cell.simulate(rec_vmem=True)
>>> # Instantiate the MEG object, compute and plot the magnetic
>>> # signal in a sensor location:
>>> r = np.array([[1E4, 0, 0]])
>>> meg = MEG(r)
>>> H = meg.calculate_H_from_iaxial(cell)
>>> plt.subplot(311)
>>> plt.plot(cell.tvec, cell.somav)
>>> plt.subplot(312)
>>> plt.plot(cell.tvec, syn.i)
>>> plt.subplot(313)
>>> plt.plot(cell.tvec, H[0, 1, :])  # y-component
>>> plt.show()

get_transformation_matrix(r_p)

Get linear response matrix mapping current dipole moment in (nA µm) located in location r_p to magnetic field \(\mathbf{H}\) in units of (nA/µm) at sensor locations r

Parameters

r_p: ndarray, dtype=float

shape (3, ) array with x,y,z-location of dipole in units of (µm)

Returns

response_matrix: ndarray

shape (n_sensors, 3, 3) ndarray

Raises

AssertionError

If dipole location r_p has the wrong shape or if its radius is greater than radius to any sensor location in <object>.r

Current Source Density (CSD)

class LaminarCurrentSourceDensity

class LFPy.LaminarCurrentSourceDensity(cell, z, r)

Bases: LinearModel

Facilitates calculations of the ground truth Current Source Density (CSD) in cylindrical volumes aligned with the z-axis based on [1] and [2].

The implementation assumes piecewise linear current sources similar to LineSourcePotential, and accounts for the fraction of each segment’s length within each volume, see Eq. 11 in [2].

This class is a LinearModel subclass that defines a 2D linear response matrix \(\mathbf{M}\) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding CSD \(\mathbf{C}\) (nA/µm^3) as

\[\mathbf{C} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{C}\) represents the CSD in each volume for every time step as the sum of currents divided by the volume.

Parameters

cell: object or None

CellGeometry instance or similar.

z: ndarray, dtype=float

shape (n_volumes, 2) array of lower and upper edges of each volume along the z-axis in units of (µm). The lower edge value must be below the upper edge value.

r: ndarray, dtype=float

shape (n_volumes, ) array with assumed radius of each cylindrical volume. Each radius must be greater than zero, and in units of (µm)

Raises

AttributeError

inputs z and r must be ndarrays of correct shape etc.

See also

LinearModel

VolumetricCurrentSourceDensity

References

1

Pettersen KH, Hagen E, Einevoll GT (2008) Estimation of population firing rates and current source densities from laminar electrode recordings. J Comput Neurosci (2008) 24:291–313. DOI 10.1007/s10827-007-0056-4

2

Hagen E, Fossum JC, Pettersen KH, Alonso JM, Swadlow HA, Einevoll GT (2017) Journal of Neuroscience, 37(20):5123-5143. DOI: https://doi.org/10.1523/JNEUROSCI.2715-16.2017

Examples

Mock cell geometry and transmembrane currents:

>>> import numpy as np
>>> from lfpykit import CellGeometry, LaminarCurrentSourceDensity
>>> # cell geometry with three segments (µm)
>>> cell = CellGeometry(x=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     y=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     z=np.array([[0, 10], [10, 20], [20, 30]]),
>>>                     d=np.array([1, 1, 1]))
>>> # transmembrane currents, three time steps (nA)
>>> I_m = np.array([[0., -1., 1.], [-1., 1., 0.], [1., 0., -1.]])
>>> # define geometry (z - upper and lower boundary;  r - radius)
>>> # of cylindrical volumes aligned with the z-axis (µm)
>>> z = np.array([[-10., 0.], [0., 10.], [10., 20.],
>>>               [20., 30.], [30., 40.]])
>>> r = np.array([100., 100., 100., 100., 100.])
>>> # instantiate electrode, get linear response matrix
>>> csd = LaminarCurrentSourceDensity(cell=cell, z=z, r=r)
>>> M = csd.get_transformation_matrix()
>>> # compute current source density [nA/µm3]
>>> M @ I_m
array([[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00],
       [ 0.00000000e+00, -3.18309886e-06,  3.18309886e-06],
       [-3.18309886e-06,  3.18309886e-06,  0.00000000e+00],
       [ 3.18309886e-06,  0.00000000e+00, -3.18309886e-06],
       [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00]])

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (n_volumes, n_seg) ndarray

Raises

AttributeError

if cell is None

class VolumetricCurrentSourceDensity

class LFPy.VolumetricCurrentSourceDensity(cell, x=None, y=None, z=None, dl=1.0)

Bases: LinearModel

Facilitates calculations of the ground truth Current Source Density (CSD) across 3D volumetric grid with bin edges defined by parameters x, y and z.

The implementation assumes piecewise constant current sources similar to LineSourcePotential, and accounts for the fraction of each segment’s length within each volume by counting the number of points representing partial segments with max length dl divided by the number of partial segments.

This class is a LinearModel subclass that defines a 4D linear response matrix \(\mathbf{M}\) of shape (x.size-1, y.size-1, z.size-1, n_seg) between transmembrane current array \(\mathbf{I}\) (nA) of a multicompartment neuron model and the corresponding CSD \(\mathbf{C}\) (nA/µm^3) as

\[\mathbf{C} = \mathbf{M} \mathbf{I}\]

The current \(\mathbf{I}\) is an ndarray of shape (n_seg, n_tsteps) with unit (nA), and each row indexed by \(j\) of \(\mathbf{C}\) represents the CSD in each bin for every time step as the sum of currents divided by the volume.

Parameters

cell: object or None

CellGeometry instance or similar.

x, y, z: ndarray, dtype=float

shape (n, ) array of bin edges of each volume along each axis in units of (µm). Must be monotonously increasing.

dl: float

discretization length of compartments before binning (µm). Default=1. Lower values will result in more accurate estimates as each line source gets split into more points.

Raises

See also

LinearModel

LaminarCurrentSourceDensity

Notes

The resulting mapping M may be very sparse (i.e, mostly made up by zeros) and can be converted into a sparse array for more efficient multiplication for the same result:

>>> import scipy.sparse as ss
>>> M_csc = ss.csc_matrix(M.reshape((-1, M.shape[-1])))
>>> C = M_csc @ I_m
>>> np.all(C.reshape((M.shape[:-1] + (-1,))) == (M @ I_m))
True

Examples

Mock cell geometry and transmembrane currents:

>>> import numpy as np
>>> from lfpykit import CellGeometry, VolumetricCurrentSourceDensity
>>> # cell geometry with three segments (µm)
>>> cell = CellGeometry(x=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     y=np.array([[0, 0], [0, 0], [0, 0]]),
>>>                     z=np.array([[0, 10], [10, 20], [20, 30]]),
>>>                     d=np.array([1, 1, 1]))
>>> # transmembrane currents, three time steps (nA)
>>> I_m = np.array([[0., -1., 1.], [-1., 1., 0.], [1., 0., -1.]])
>>> # instantiate probe, get linear response matrix
>>> csd = VolumetricCurrentSourceDensity(cell=cell,
>>>                                      x=np.linspace(-20, 20, 5),
>>>                                      y=np.linspace(-20, 20, 5),
>>>                                      z=np.linspace(-20, 20, 5), dl=1.)
>>> M = csd.get_transformation_matrix()
>>> # compute current source density [nA/µm3]
>>> M @ I_m
array([[[[ 0.,  0.,  0.],
         [ 0.,  0.,  0.],
         [ 0.,  0.,  0.],
         [ 0.,  0.,  0.]],
         ...

get_transformation_matrix()

Get linear response matrix

Returns

response_matrix: ndarray

shape (x.size-1, y.size-1, z.size-1, n_seg) ndarray

Raises

AttributeError

if cell is None

Misc.

submodule lfpcalc

Copyright (C) 2012 Computational Neuroscience Group, NMBU.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

submodule tools

Copyright (C) 2012 Computational Neuroscience Group, NMBU.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

LFPy.tools.load(filename)

Generic loading of cPickled objects from file

Parameters

filename: str

path to pickle file

LFPy.tools.noise_brown(ncols, nrows=1, weight=1.0, filter=None, filterargs=None)

Return 1/f^2 noise of shape(nrows, ncols obtained by taking the cumulative sum of gaussian white noise, with rms weight.

If filter is not None, this function will apply the filter coefficients obtained by:

>>> b, a = filter(**filterargs)
>>> signal = scipy.signal.lfilter(b, a, signal)

Parameters

ncols: int

nrows: int

weight: float

filter: None or function

filterargs: **dict

parameters passed to filter

submodule alias_method

LFPy.alias_method.alias_method(idx, probs, nsyn)

Alias method for drawing random numbers from a discrete probability distribution. See http://www.keithschwarz.com/darts-dice-coins/

Parameters

idx: np.ndarray

compartment indices as array of ints

probs: np.ndarray

compartment areas as array of floats

nsyn: int

number of randomized compartment indices

Returns

out: np.ndarray

integer array of randomly drawn compartment indices

LFPy.alias_method.alias_setup(probs)

Set up function for alias method. See http://www.keithschwarz.com/darts-dice-coins/

Parameters

probs: np.ndarray

float array of compartment areas

Returns

J: np.ndarray

array of ints

q: np.ndarray

array of floats

submodule inputgenerators

Copyright (C) 2012 Computational Neuroscience Group, NMBU.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

LFPy.inputgenerators.get_activation_times_from_distribution(n, tstart=0.0, tstop=1000000.0, distribution=<scipy.stats._continuous_distns.expon_gen object>, rvs_args={'loc': 0, 'scale': 1}, maxiter=1000000.0)

Construct a length n list of ndarrays containing continously increasing random numbers on the interval [tstart, tstop], with intervals drawn from a chosen continuous random variable distribution subclassed from scipy.stats.rv_continous, e.g., scipy.stats.expon or scipy.stats.gamma.

The most likely initial first entry is tstart + method.rvs(size=inf, **rvs_args).mean()

Parameters

n: int

number of ndarrays in list

tstart: float

minimum allowed value in ndarrays

tstop: float

maximum allowed value in ndarrays

distribution: object

subclass of scipy.stats.rv_continous. Distributions producing negative values should be avoided if continously increasing values should be obtained, i.e., the probability density function (distribution.pdf(**rvs_args)) should be 0 for x < 0, which is not explicitly tested for.

rvs_args: dict

parameters for method.rvs method. If “size” is in dict, then tstop will be ignored, and each ndarray in output list will be distribution.rvs(**rvs_args).cumsum() + tstart. If size is not given in dict, then values up to tstop will be included

maxiter: int

maximum number of iterations

Returns

list of ndarrays

length n list of arrays containing data

Raises

AssertionError

if distribution does not have the ‘rvs’ attribute

StopIteration

if number of while-loop iterations reaches maxiter

Examples

Create n sets of activation times with intervals drawn from the exponential distribution, with rate expectation lambda 10 s^-1 (thus scale=1000 / lambda). Here we assume output in units of ms

>>> from LFPy.inputgenerators import get_activation_times_from_distribution
>>> import scipy.stats as st
>>> import matplotlib.pyplot as plt
>>> times = get_activation_times_from_distribution(n=10, tstart=0.,
>>>                                                tstop=1000.,
>>>                                                distribution=st.expon,
>>>                                                rvs_args=dict(loc=0.,
>>>                                                scale=100.))