def get_sensitivity_measure(calcAna, calcAnaAll, mode='gamma', eig_index=None):
print 'Calculate sensitivity measure.'
if calcAna or calcAnaAll:
if mode == 'gamma' or mode == 'low':
fmax = 100.0
elif mode == 'high_gamma':
fmax = 400.0
circ = circuit.Circuit('microcircuit',
circuit_params,
fmax=fmax,
from_file=not calcAnaAll)
freqs, eigs = circ.create_eigenvalue_spectra('MH')
if mode == 'gamma':
fmax = freqs[np.argmin(abs(eigs[eig_index] - 1))]
Z = circ.get_sensitivity_measure(fmax)
eigc = eigs[eig_index][np.argmin(abs(eigs[eig_index] - 1))]
elif mode == 'high_gamma':
eigs = eigs[eig_index][np.where(freqs > 150.)]
freqs = freqs[np.where(freqs > 150.)]
fmax = freqs[np.argmin(abs(eigs - 1.0))]
fmax_index = np.argmin(abs(freqs - fmax))
eigc = eigs[fmax_index]
Z = circ.get_sensitivity_measure(fmax, index=eig_index)
elif mode == 'low':
eigs = eigs[eig_index]
fmax = 0.0
eigc = eigs[0]
Z = circ.get_sensitivity_measure(fmax)
k = np.asarray([1, 0]) - np.asarray([eigc.real, eigc.imag])
k /= np.sqrt(np.dot(k, k))
k_per = np.asarray([-k[1], k[0]])
k_per /= np.sqrt(np.dot(k_per, k_per))
Z_amp = Z.real * k[0] + Z.imag * k[1]
Z_freq = Z.real * k_per[0] + Z.imag * k_per[1]
label = mode + str(eig_index)
h5.add_to_h5('results.h5', {
'sensitivity_measure': {
label: {
'f_peak': fmax,
'Z': Z,
'Z_amp': Z_amp,
'Z_freq': Z_freq,
'k': k,
'k_per': k_per,
'eigc': eigc
}
}
},
'a',
overwrite_dataset=True)
else:
path_base = 'sensitivity_measure/' + mode + str(eig_index)
fmax = h5.load_h5('results.h5', path_base + '/f_peak')
Z = h5.load_h5('results.h5', path_base + '/Z')
Z_amp = h5.load_h5('results.h5', path_base + '/Z_amp')
Z_freq = h5.load_h5('results.h5', path_base + '/Z_freq')
k = h5.load_h5('results.h5', path_base + '/k')
k_per = h5.load_h5('results.h5', path_base + '/k_per')
eigc = h5.load_h5('results.h5', path_base + '/eigc')
return fmax, Z, Z_amp, Z_freq, k, k_per, eigc
def get_diffapprox_gain(rates0, params, Kstim, calc=False, read_old_results=False):
folder = get_folder(params)
label = 'plot_rates_gain' + str(Kstim) + folder
if read_old_results:
path = '/Users/bos/Documents/pittsburgh/disinhibitory_circuits/py/E-I-VIP/'
g = h5.load_h5(path + 'results.h5', label + '/g')
return g
circuit_params = set_circuit_params(params)
circuit_params['Next'] += np.array([Kstim,Kstim,0])
if calc:
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates = circ.th_rates
# divide by 8 since this is the external firing rate in simulation
g = (rates[0]-rates0[0])/(Kstim*8.)
M = circ.ana.I*circ.ana.W
dI = [M[0][i]*(rates[i]-rates0[i]) for i in range(3)]
h5.add_to_h5('results.h5',{label:{'g': g}},
'a', overwrite_dataset=True)
else:
g = h5.load_h5('results.h5', label + '/g')
return g
def get_diffapprox_Next_for_rE_rI_grid(params, rE_array, rI_array, rS,
calc_Next=False):
folder = get_folder(params)
label_Next = 'Next_betas_matrix_' + folder
circuit_params = set_circuit_params(params)
if calc_Next:
print 'calculate Next'
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates_calc = circ.th_rates
M = circ.I*circ.W
Next = np.zeros((len(rE_array), len(rI_array), 3))
betas = np.zeros((len(rE_array), len(rI_array), 3))
initial_Next = get_Next(params)
for i,re in enumerate(rE_array):
for j,ri in enumerate(rI_array):
target_rates = np.array([re, ri, rS])
circ.Next = initial_Next
Next[i][j], betas[i][j] = circ.get_external_input(target_rates)
h5.add_to_h5('results.h5', {label_Next:{'Next': Next, 'betas': betas,
'M': M}}, 'a', overwrite_dataset=True)
else:
Next = h5.load_h5('results.h5', label_Next + '/Next')
betas = h5.load_h5('results.h5', label_Next + '/betas')
M = h5.load_h5('results.h5', label_Next + '/M')
return Next, betas, M
def get_diffapprox_Next_for_pSE_array(params, rE, rI, rS, pSE_array,
calc_Next=False):
folder = get_folder(params)
label_Next = 'Next_betas_pSE_array_' + folder + str(rE) + str(rI)
circuit_params = set_circuit_params(params)
if calc_Next:
print 'calculate Next'
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates_calc = circ.th_rates
M = circ.I*circ.W
Next = np.zeros((len(pSE_array), 3))
betas = np.zeros((len(pSE_array), 3))
initial_Next = get_Next(params)
for i,pSE in enumerate(pSE_array):
target_rates = np.array([rE, rI, rS])
circ.Next = initial_Next
params['pSE'] = pSE
circ.alter_default_params({'p': get_connection_probabilities(params)})
Next[i], betas[i] = circ.get_external_input(target_rates)
h5.add_to_h5('results.h5', {label_Next:{'Next': Next, 'betas': betas,
'M': M}}, 'a', overwrite_dataset=True)
else:
Next = h5.load_h5('results.h5', label_Next + '/Next')
betas = h5.load_h5('results.h5', label_Next + '/betas')
M = h5.load_h5('results.h5', label_Next + '/M')
return Next, betas, M
def get_spec_ana(calcAna, calcAnaAll):
print 'Calculate power spectra.'
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit', circuit_params, fmax=500.0,
from_file=not calcAnaAll)
freq_ana, power_ana = circ.create_power_spectra()
h5.add_to_h5('results.h5',{'fig_microcircuit':{
'freq_ana': freq_ana,'power_ana': power_ana}},
'a',overwrite_dataset=True)
else:
freq_ana = h5.load_h5('results.h5','fig_microcircuit/freq_ana')
power_ana = h5.load_h5('results.h5','fig_microcircuit/power_ana')
return freq_ana, power_ana
def get_spectra(calcAna, calcAnaAll, a, conn):
print 'Calculate power spectra and eigenvalues.'
label = str(a) + str(conn[0]) + str(conn[1])
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit',
circuit_params,
analysis_type='stationary',
from_file=not calcAnaAll)
I_new = circ.I.copy()
Next_new = circ.Next.copy()
I_new[conn[0]][conn[1]] = np.round(circ.I[conn[0]][conn[1]] *
(1.0 + a))
# missing rate
r = circ.th_rates[conn[1]] * circ.I[conn[0]][conn[1]] * abs(a)
# source inhibitory
# a pos: more inh. input -> add ext. input
# a neg: less inh. input -> rem. ext. input
if conn[1] % 2 == 1:
Next_add = np.sign(a) * 4 * int(r / circ.v_ext)
# source excitatory
# a pos: more exc. input -> rem. ext. input
# a neg: less exc. input -> add ext. input
else:
Next_add = -np.sign(a) * int(r / circ.v_ext)
Next_new[conn[0]] = circ.Next[conn[0]] + Next_add
circ.alter_default_params({
'analysis_type': 'dynamical',
'I': I_new,
'Next': Next_new
})
freqs, power = circ.create_power_spectra()
freqs, eigs = circ.create_eigenvalue_spectra('MH')
rates = circ.th_rates
h5.add_to_h5('results.h5', {
'Z_validation': {
label: {
'freqs': freqs,
'power': power,
'eigs': eigs,
'rates': rates
}
}
},
'a',
overwrite_dataset=True)
else:
freqs = h5.load_h5('results.h5', 'Z_validation/' + label + '/freqs')
power = h5.load_h5('results.h5', 'Z_validation/' + label + '/power')
eigs = h5.load_h5('results.h5', 'Z_validation/' + label + '/eigs')
rates = h5.load_h5('results.h5', 'Z_validation/' + label + '/rates')
return freqs, power, eigs, rates
def get_eigenvalues(calcAna, calcAnaAll):
print('Calculate eigenvalues.')
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit', circuit_params,
fmax=400.0, from_file=not calcAnaAll)
freqs, eigs = circ.create_eigenvalue_spectra('MH')
eigs = np.transpose(eigs)
h5.add_to_h5('results.h5', {'eigenvalue_trajectories': {
'freqs': freqs, 'eigs': eigs}},
'a', overwrite_dataset=True)
else:
freqs = h5.load_h5('results.h5', 'eigenvalue_trajectories/freqs')
eigs = h5.load_h5('results.h5', 'eigenvalue_trajectories/eigs')
return freqs, eigs
def get_spectra(panel, calcAna, calcAnaAll):
print 'Calculate power spectra.'
eig_index = 4
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit',
circuit_params,
fmax=100.0,
from_file=not calcAnaAll)
freqs, power = circ.create_power_spectra()
h5.add_to_h5(
'results.h5',
{'oscillation_origin': {
panel: {
'freqs': freqs,
'power': power
}
}},
'a',
overwrite_dataset=True)
if panel == 'A':
Mred = np.zeros((8, 8))
Mred[0, 0:4] = 1
Mred[1, 0:2] = 1
Mred[2:4, 2:4] = 1
Mred[3, 0] = 1
elif panel == 'B':
Mred = np.ones((8, 8))
Mred[3, 0] = 0
circ.reduce_connectivity(Mred)
freqs_sub, power_sub = circ.create_power_spectra()
h5.add_to_h5('results.h5', {
'oscillation_origin': {
panel: {
'power_sub': power_sub,
'Mred': Mred
}
}
},
'a',
overwrite_dataset=True)
else:
path_base = 'oscillation_origin/' + panel
freqs = h5.load_h5('results.h5', path_base + '/freqs')
power = h5.load_h5('results.h5', path_base + '/power')
power_sub = h5.load_h5('results.h5', path_base + '/power_sub')
Mred = h5.load_h5('results.h5', path_base + '/Mred')
return freqs, power, power_sub, Mred
def get_parameter_microcircuit():
# factor for standard deviation of delay distribution
dsd = 1.0
circ = circuit.Circuit('microcircuit', analysis_type=None)
I_new = circ.I.copy()
# reduce indegrees from 4I to 4E
I_new[2][3] = np.round(circ.I[2][3]*(1.0-0.15))
Next_new = circ.Next.copy()
# adjust external input to 4E
Next_new[2] -= 300
Next_new[2] *= (1.0-0.011)
new_params = {'de_sd': circ.de*dsd, 'di_sd': circ.di*dsd,
'I': I_new, 'Next': Next_new,
'delay_dist': 'truncated_gaussian'}
params = circ.params
params.update(new_params)
return params
def get_diffapprox_rates_betas(params, calc=False):
folder = get_folder(params)
label = 'plot_rates_betas' + folder
circuit_params = set_circuit_params(params)
if calc:
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates = circ.th_rates
betas = circ.ana.create_H0_mu()[:,0]
h5.add_to_h5('results.h5',{label:{'rates_calc': rates, 'betas': betas}},
'a', overwrite_dataset=True)
else:
rates = h5.load_h5('results.h5', label + '/rates_calc')
betas = h5.load_h5('results.h5', label + '/betas')
return rates, betas
def get_spectra(calcAna, calcAnaAll):
print('Calculate spectra..')
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit',
circuit_params,
fmax=400.0,
from_file=not calcAnaAll)
freqs, power = circ.create_power_spectra()
h5.add_to_h5('results.h5',
{'fig_eigenmodes': {
'freqs': freqs,
'power': power
}},
'a',
overwrite_dataset=True)
else:
freqs = h5.load_h5('results.h5', 'fig_eigenmodes/freqs')
power = h5.load_h5('results.h5', 'fig_eigenmodes/power')
return freqs, power
def get_diffapprox_rates(params, calc=False, read_old_results=False):
folder = get_folder(params)
label = 'plot_rates_' + folder
if read_old_results:
path = '/Users/bos/Documents/pittsburgh/disinhibitory_circuits/py/E-I-VIP/'
rates = h5.load_h5(path + 'results.h5', label + '/rates_calc')
return rates
circuit_params = set_circuit_params(params)
if calc:
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates = circ.th_rates
h5.add_to_h5('results.h5',{label:{'rates_calc': rates}},
'a', overwrite_dataset=True)
else:
rates = h5.load_h5('results.h5', label + '/rates_calc')
return rates
def get_diffapprox_rates_Next_array(params, Next_array, calc=False):
folder = get_folder(params)
label = 'plot_rates_Next_array' + folder
label += str(Next_array[0]) + str(Next_array[1]-Next_array[0])
label += str(Next_array[-1])
circuit_params = set_circuit_params(params)
if calc:
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates = np.zeros_like(Next_array)
for i in range(Next_array.shape[0]):
circuit_params['Next']= Next_array[i]
circ.alter_default_params(circuit_params)
rates[i] = circ.th_rates
h5.add_to_h5('results.h5',{label:{'rates_calc': rates}},
'a', overwrite_dataset=True)
else:
rates = h5.load_h5('results.h5', label + '/rates_calc')
return rates
def get_rates(calcData, calcAna, calcAnaAll, folder=folder):
print 'Get data for firing rates.'
tmin = 300.0
if calcData:
rates_sim = []
for pop in range(8):
mu = rs.get_mu_rate(folder, pop, T, tmin)
rates_sim.append(mu)
h5.add_to_h5('results.h5',{'fig_microcircuit':{
'rates_sim': rates_sim}}, 'a', overwrite_dataset=True)
else:
rates_sim = h5.load_h5('results.h5', 'fig_microcircuit/rates_sim')
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit', circuit_params,
analysis_type='stationary',
from_file=not calcAnaAll)
rates_calc = circ.th_rates
h5.add_to_h5('results.h5',{'fig_microcircuit':{
'rates_calc': rates_calc}}, 'a', overwrite_dataset=True)
else:
rates_calc = h5.load_h5('results.h5','fig_microcircuit/rates_calc')
return np.asarray(rates_sim), rates_calc
def get_diffapprox_gain_coefficient(params, calc=False):
folder = get_folder(params)
label = 'plot_gain_coefficient' + folder
circuit_params = set_circuit_params(params)
if calc:
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates_calc = circ.th_rates
MH = circ.ana.create_MH0()
P = np.linalg.inv(np.eye(3)-MH)
betas = circ.ana.create_H0_mu()[:,0]
factor = np.dot(P[:,:2], betas[:2])
h5.add_to_h5('results.h5',{label:{
'rates_calc': rates_calc, 'factor': factor}},
'a', overwrite_dataset=True)
else:
rates_calc = h5.load_h5('results.h5', label + '/rates_calc')
factor = h5.load_h5('results.h5', label + '/factor')
return rates_calc, factor
def get_spectra(calcAna, calcAnaAll):
print('Calculate spectra.')
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit', circuit_params,
fmax=400.0, from_file=not calcAnaAll)
freqs, power = circ.create_power_spectra()
power_layer = []
for i, layer in enumerate(ph.layers):
M_red = np.zeros((8, 8))
M_red[2 * i: 2 * i + 2, 2 * i: 2 * i + 2] = np.ones((2, 2))
circ.reduce_connectivity(M_red)
freqs, power_red = circ.create_power_spectra()
power_layer.append([power_red[2 * i], power_red[2 * i + 1]])
circ.restore_full_connectivity()
h5.add_to_h5('results.h5', {'eigenvalue_trajectories': {
'freqs_spec': freqs, 'power_layer': power_layer,
'power': power}}, 'a', overwrite_dataset=True)
else:
freqs = h5.load_h5('results.h5', 'eigenvalue_trajectories/freqs_spec')
power_layer = h5.load_h5('results.h5',
'eigenvalue_trajectories/power_layer')
power = h5.load_h5('results.h5', 'eigenvalue_trajectories/power')
return freqs, power_layer, power
def get_eigenvalues_layers(calcAna, calcAnaAll):
print('Calculate eigenvalues.')
if calcAna or calcAnaAll:
circ = circuit.Circuit('microcircuit', circuit_params,
fmax=400.0, from_file=not calcAnaAll)
eigs_layer = []
for i, layer in enumerate(ph.layers):
M_red = np.zeros((8, 8))
M_red[2 * i: 2 * i + 2, 2 * i: 2 * i + 2] = np.ones((2, 2))
circ.reduce_connectivity(M_red)
freqs, eigs = circ.create_eigenvalue_spectra('MH')
eigs_layer.append(eigs[0])
eigs_layer.append(eigs[1])
circ.restore_full_connectivity()
eigs_layer = np.transpose(np.asarray(eigs_layer))
h5.add_to_h5('results.h5', {'eigenvalue_trajectories': {
'freq_layer': freqs, 'eigs_layer': eigs_layer}},
'a', overwrite_dataset=True)
else:
freqs = h5.load_h5('results.h5', 'eigenvalue_trajectories/freq_layer')
eigs_layer = h5.load_h5('results.h5',
'eigenvalue_trajectories/eigs_layer')
return freqs, eigs_layer
def get_diffapprox_Next(params, rates, calc=False):
folder = get_folder(params)
label = 'plot_rates_Next_' + folder + str(rates)
if rates[2]>0:
label += 'rS_' + str(rates[2])
circuit_params = set_circuit_params(params)
if calc:
print 'calculate Next'
circ = circuit.Circuit('3dcircuit',
circuit_params,
analysis_type='stationary',
from_file='False')
rates_calc = circ.th_rates
M = circ.I*circ.W
Next, betas = circ.get_external_input(rates)
h5.add_to_h5('results.h5', {label:{'Next': Next, 'betas': betas, 'M': M}},
'a', overwrite_dataset=True)
else:
Next = h5.load_h5('results.h5', label + '/Next')
betas = h5.load_h5('results.h5', label + '/betas')
M = h5.load_h5('results.h5', label + '/M')
return Next, betas, M
import plot_helpers
import meanfield.circuit as circuit
import numpy as np
import h5py_wrapper.wrapper as h5
fix_path = 'integration/data/'
if __name__ == '__main__':
filename = fix_path + 'Bos2016_data.h5'
# Original parameters extracted from the data + modification
circ_params_with_modifications = plot_helpers.get_parameter_microcircuit()
circ = circuit.Circuit('microcircuit',
circ_params_with_modifications,
analysis_type='dynamical',
fmax=500.0,
from_file=False)
power_spectra_freqs, power_spectra = circ.create_power_spectra()
exemplary_frequency_idx = 20
omega = circ.omegas[exemplary_frequency_idx]
print(omega)
H = circ.ana.create_H(omega)
MH = circ.ana.create_MH(omega)
delay_dist = circ.ana.create_delay_dist_matrix(omega)
transfer_function = circ.trans_func
tau_s = circ.params['tauf'] * 1e-3
transfer_function_with_synaptic_filter = np.copy(transfer_function)
"""
import plot_helpers
import meanfield.circuit as circuit
from docopt import docopt
import sys
if __name__ == '__main__':
arguments = docopt(__doc__, version='0.1')
filename = arguments['<filename>']
# New parameters used for Fig. 1
circ_params_new = plot_helpers.get_parameter_microcircuit()
# Original parameters extracted from the data
circ = circuit.Circuit('microcircuit', analysis_type=None)
circ_params_old = circ.params
file = open(filename, 'w')
sys.stdout = file
for attribute in circ_params_new.keys():
print(attribute)
print('old/original:')
print(circ_params_old[attribute])
print('new:')
print(circ_params_new[attribute])
print('\n')
attribute = 'I'
print('old/original:')