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%matplotlib inline
This is an example scripts using LFPy with a passive cell model adapted from Mainen and Sejnowski, Nature 1996, for the original files, see http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=2488
Here, excitatory and inhibitory neurons are distributed on different parts of
the morphology, with stochastic spike times produced by the
LFPy.inputgenerators.stationary_gamma() function.
Same as LFPy-example-7.ipynb, just without the active conductances
Copyright (C) 2017 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.
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# importing some modules, setting some matplotlib values for pl.plot.
import LFPy
import numpy as np
import scipy.stats
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size' : 12,
'figure.facecolor' : '1',
'figure.subplot.wspace' : 0.5,
'figure.subplot.hspace' : 0.5})
#seed for random generation
np.random.seed(1234)
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def insert_synapses(synparams, section, n, spTimesFun, args):
'''find n compartments to insert synapses onto'''
idx = cell.get_rand_idx_area_norm(section=section, nidx=n)
#Insert synapses in an iterative fashion
for i in idx:
synparams.update({'idx' : int(i)})
# Some input spike train using the function call
[spiketimes] = spTimesFun(**args)
# Create synapse(s) and setting times using the Synapse class in LFPy
s = LFPy.Synapse(cell, **synparams)
s.set_spike_times(spiketimes)
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# define cell parameters used as input to cell-class
cellParameters = {
'morphology' : 'morphologies/L5_Mainen96_wAxon_LFPy.hoc',
'cm' : 1.0, # membrane capacitance
'Ra' : 150, # axial resistance
'v_init' : -65, # initial crossmembrane potential
'passive' : True, # switch on passive mechs
'passive_parameters' : {'g_pas' : 1./30000, 'e_pas' : -65}, # passive params
'nsegs_method' : 'lambda_f',# method for setting number of segments,
'lambda_f' : 100, # segments are isopotential at this frequency
'dt' : 2**-4, # dt of LFP and NEURON simulation.
'tstart' : -100, #start time, recorders start at t=0
'tstop' : 200, #stop time of simulation
#'custom_code' : ['active_declarations_example3.hoc'], # will run this file
}
# Synaptic parameters taken from Hendrickson et al 2011
# Excitatory synapse parameters:
synapseParameters_AMPA = {
'e' : 0, #reversal potential
'syntype' : 'Exp2Syn', #conductance based exponential synapse
'tau1' : 1., #Time constant, rise
'tau2' : 3., #Time constant, decay
'weight' : 0.005, #Synaptic weight
'record_current' : True, #record synaptic currents
}
# Excitatory synapse parameters
synapseParameters_NMDA = {
'e' : 0,
'syntype' : 'Exp2Syn',
'tau1' : 10.,
'tau2' : 30.,
'weight' : 0.005,
'record_current' : True,
}
# Inhibitory synapse parameters
synapseParameters_GABA_A = {
'e' : -80,
'syntype' : 'Exp2Syn',
'tau1' : 1.,
'tau2' : 12.,
'weight' : 0.005,
'record_current' : True
}
# where to insert, how many, and which input statistics
insert_synapses_AMPA_args = {
'section' : 'apic',
'n' : 100,
'spTimesFun' : LFPy.inputgenerators.get_activation_times_from_distribution,
'args' : dict(n=1, tstart=0, tstop=cellParameters['tstop'],
distribution=scipy.stats.gamma,
rvs_args=dict(a=0.5, loc=0., scale=40)
)
}
insert_synapses_NMDA_args = {
'section' : ['dend', 'apic'],
'n' : 15,
'spTimesFun' : LFPy.inputgenerators.get_activation_times_from_distribution,
'args' : dict(n=1, tstart=0, tstop=cellParameters['tstop'],
distribution=scipy.stats.gamma,
rvs_args=dict(a=2, loc=0, scale=50)
)
}
insert_synapses_GABA_A_args = {
'section' : 'dend',
'n' : 100,
'spTimesFun' : LFPy.inputgenerators.get_activation_times_from_distribution,
'args' : dict(n=1, tstart=0, tstop=cellParameters['tstop'],
distribution=scipy.stats.gamma,
rvs_args=dict(a=0.5, loc=0., scale=40)
)
}
# Define electrode geometry corresponding to a laminar electrode, where contact
# points have a radius r, surface normal vectors N, and LFP calculated as the
# average LFP in n random points on each contact:
N = np.empty((16, 3))
for i in range(N.shape[0]): N[i,] = [1, 0, 0] #normal unit vec. to contacts
# put parameters in dictionary
electrodeParameters = {
'sigma' : 0.3, # Extracellular potential
'x' : np.zeros(16) + 25, # x,y,z-coordinates of electrode contacts
'y' : np.zeros(16),
'z' : np.linspace(-500, 1000, 16),
'n' : 20,
'r' : 10,
'N' : N,
}
# Parameters for the cell.simulate() call, recording membrane- and syn.-currents
simulationParameters = {
'rec_imem' : True, # Record Membrane currents during simulation
}
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# Initialize cell instance, using the LFPy.Cell class
cell = LFPy.Cell(**cellParameters)
# Insert synapses using the function defined earlier
insert_synapses(synapseParameters_AMPA, **insert_synapses_AMPA_args)
insert_synapses(synapseParameters_NMDA, **insert_synapses_NMDA_args)
insert_synapses(synapseParameters_GABA_A, **insert_synapses_GABA_A_args)
# perform NEURON simulation, results saved as attributes in the cell instance
cell.simulate(**simulationParameters)
# Initialize electrode geometry, then calculate the LFP, using the
# LFPy.RecExtElectrode class. Note that now cell is given as input to electrode
# and created after the NEURON simulations are finished
electrode = LFPy.RecExtElectrode(cell, **electrodeParameters)
print('simulating LFPs....')
electrode.calc_lfp()
print('done')
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from example_suppl import plot_ex3
fig = plot_ex3(cell, electrode)
# fig.savefig('LFPy-example-8.pdf', dpi=300)
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