I_drive = I_drive_list[ii]
if num_jjs == 2:
directory = 'wrspice_data/constant_drive/from_saeed/2jj/'
file_name = 'dend_cnst_drv_1jj_Llft{:05.2f}pH_Lrgt{:05.2f}pH_Ide{:05.2f}uA_Idrv{:05.2f}uA_Ldi0077.50nH_taudi0775ms_dt00.1ps.dat'.format(
L_left_list[pp], L_right_list[pp], I_de_list[qq],
I_drive)
j_di_str = 'v(2)'
I_di_str = 'i(L2)'
elif num_jjs == 4:
directory = 'wrspice_data/constant_drive/from_saeed/4jj/'
file_name = 'dend_cnst_drv_Llft{:05.2f}pH_Lrgt{:05.2f}pH_Ide{:05.2f}uA_Idrv{:05.2f}uA_Ldi0077.50nH_taudi0775ms_dt00.1ps.dat'.format(
L_left_list[pp], L_right_list[pp], I_de_list[qq],
I_drive)
j_di_str = 'v(4)'
I_di_str = 'i(L4)'
data_dict = read_wr_data(directory + '/' + file_name)
# find peaks for each jj
time_vec = data_dict['time']
j_di = data_dict[j_di_str] # j_di = data_dict['v(5)']
# j_di_peaks, _ = find_peaks(j_di, height = jph[pp][qq][ii])
j_di_peaks, _ = find_peaks(j_di,
height=min_peak_height,
distance=min_peak_distance)
initial_ind = (np.abs(time_vec - 2.0e-9)).argmin()
final_ind = j_di_peaks[-1] * 2 * downsample_factor
time_vec = time_vec[initial_ind:final_ind]
j_di = j_di[initial_ind:final_ind]
tau_di_vec = np.asarray([10,25,50,250,1250])*1e-9 # 10,25,50,250,1250
#%%
error_drive_mat = np.zeros([len(I_de_vec),len(L_di_vec),len(tau_di_vec),len(dt_vec)])
error_mat = np.zeros([len(I_de_vec),len(L_di_vec),len(tau_di_vec),len(dt_vec)])
for ii in range(len(I_de_vec)):
I_de = I_de_vec[ii]
for jj in range(len(L_di_vec)):
L_di = L_di_vec[jj]
for kk in range(len(tau_di_vec)):
tau_di = tau_di_vec[kk]
# load WR data
directory_name = 'wrspice_data/{:d}jj/'.format(num_jjs)
file_name = 'syn_one_pls_{:d}jj_Ispd{:05.2f}uA_Ide{:05.2f}uA_Ldr20.0pH20.0pH_Ldi{:07.2f}nH_taudi{:07.2f}ns_dt01.0ps.dat'.format(num_jjs,I_spd*1e6,I_de*1e6,L_di*1e9,tau_di*1e9)
data_dict = read_wr_data('{}{}'.format(directory_name,file_name))
if num_jjs == 2:
I_di_str = 'L0#branch'
I_drive_str = 'L2#branch'
elif num_jjs == 4:
I_di_str = 'L3#branch'
I_drive_str = 'L0#branch'
target_data = np.vstack((data_dict['time'],data_dict[I_di_str]))
tf = np.round(data_dict['time'][-1]/1e-9)*1e-9
target_data__drive = np.vstack((data_dict['time'],data_dict[I_drive_str]))
print('calculating chi^2 norm ...')
dt_norm = target_data[0,1]-target_data[0,0]
norm = 0
norm__drive = 0
for rr in range(len(target_data[0,:])):
calculate_chi_squared = True
plot_each = False
#%%
num_files = len(I_sy_vec)
t_tot = time.time()
for ii in range(num_files): # range(1): #
print('\nii = {} of {}\n'.format(ii + 1, num_files))
#load WR data
file_name = 'syn_2jj_Ispd20.00uA_trep50ns_Isy{:04.2f}uA_Isc35.00uA_Lsi{:07.2f}nH_tausi{:04.0f}ns_dt10.0ps_tsim1000ns.dat'.format(
I_sy_vec[ii] * 1e6, L_si_vec[ii] * 1e9, tau_si_vec[ii] * 1e9)
data_dict = read_wr_data('wrspice_data/test_data/2jj/' + file_name)
target_drive = np.vstack((data_dict['time'], data_dict['L0#branch']))
target_drive_array.append(target_drive)
target_data = np.vstack((data_dict['time'], data_dict['L2#branch']))
target_data_array.append(target_data)
# initialize input signal
input_1 = input_signal('in',
input_temporal_form='arbitrary_spike_train',
spike_times=spike_times)
# initialize synapse
synapse_1 = synapse('sy',
num_jjs=2,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_si_vec[ii],
def dendrite_model__parameter_sweep(data_file_list, L_di_vec, tau_di_vec,
dt_vec, tf_vec, drive_info, amp_vec,
mu1_vec, mu2_vec, mu3_vec, mu4_vec,
master_error_plot_name):
master_error_plot_name = 'mstr_err__' + master_error_plot_name
best_params = dict()
best_params['amp_mu12'] = []
best_params['amp_mu34'] = []
best_params['mu1'] = []
best_params['mu2'] = []
best_params['mu3'] = []
best_params['mu4'] = []
data_array = dict()
data_array['amp_vec'] = amp_vec
data_array['mu1_vec'] = mu1_vec
data_array['mu2_vec'] = mu2_vec
data_array['mu3_vec'] = mu3_vec
data_array['mu4_vec'] = mu4_vec
num_sims = len(data_file_list)
num_amps = len(amp_vec)
num_mu1 = len(mu1_vec)
num_mu2 = len(mu2_vec)
num_mu3 = len(mu3_vec)
num_mu4 = len(mu4_vec)
mu3_initial = 0 #1
mu4_initial = 0 #0.5
print(
'\n\nrunning dendrite_model__parameter_sweep\n\nnum_files = {:d}\nnum_amps = {:d}\nnum_mu1 = {:d}\nnum_mu2 = {:d}\nnum_mu3 = {:d}\nnum_mu4 = {:d}'
.format(num_sims, num_amps, num_mu1, num_mu2, num_mu3, num_mu4))
error_mat_master__mu1_mu2 = np.zeros([num_amps, num_mu1, num_mu2])
error_mat_master__mu3_mu4 = np.zeros([num_amps, num_mu3, num_mu4])
if drive_info['drive_type'] == 'piecewise_linear':
pwl_drive = drive_info['pwl_drive']
directory_string = 'constant_drive'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'linear_ramp':
pwl_drive = drive_info['pwl_drive']
directory_string = 'linear_ramp'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'sq_pls_trn':
sq_pls_trn_params = drive_info['sq_pls_trn_params']
directory_string = 'square_pulse_sequence'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'exp_pls_trn':
exp_pls_trn_params = drive_info['exp_pls_trn_params']
directory_string = 'exponential_pulse_sequence'
wr_drive_string = 'L5#branch'
wr_target_string = 'L10#branch'
for ii in range(num_sims):
print('\ndata_file {} of {}\n'.format(ii + 1, num_sims))
plt.close('all')
dt = dt_vec[ii]
tf = tf_vec[ii]
# WR data
print('reading wr data ...\n')
file_name = data_file_list[ii] + '.dat'
data_dict = read_wr_data('wrspice_data/' + directory_string + '/' +
file_name)
target_data = np.vstack(
(data_dict['time'], data_dict[wr_target_string]))
#----------------------
# compare drive signals
#----------------------
target_data__drive = np.vstack(
(data_dict['time'], data_dict[wr_drive_string]))
# initialize input signal
if drive_info['drive_type'] == 'piecewise_linear' or drive_info[
'drive_type'] == 'linear_ramp':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
piecewise_linear=pwl_drive)
dendritic_drive = dendritic_drive__piecewise_linear(
input_1.time_vec, pwl_drive)
if drive_info['drive_type'] == 'sq_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
square_pulse_train=sq_pls_trn_params)
dendritic_drive = dendritic_drive__square_pulse_train(
input_1.time_vec, sq_pls_trn_params)
if drive_info['drive_type'] == 'exp_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
exponential_pulse_train=exp_pls_trn_params)
dendritic_drive = dendritic_drive__exp_pls_train__LR(
input_1.time_vec, exp_pls_trn_params)
actual_data__drive = np.vstack(
(input_1.time_vec[:], dendritic_drive[:, 0]))
print('comparing drive signals ...\n')
error__drive = chi_squared_error(target_data__drive,
actual_data__drive)
#------------------------
# find best amp, mu1, mu2
#------------------------
mu3 = [mu3_initial] #np.linspace([0.5,2.5,10])
mu4 = [mu4_initial] #np.linspace([0.25,1.5,10])
error_mat_1 = np.zeros([num_amps, num_mu1, num_mu2])
print('seeking amp, mu1, mu2 ...')
for aa in range(num_amps):
for bb in range(num_mu1):
for cc in range(num_mu2):
print('aa = {} of {}, bb = {} of {}, cc = {} of {}'.format(
aa + 1, num_amps, bb + 1, num_mu1, cc + 1, num_mu2))
# create sim_params dictionary
sim_params = dict()
sim_params['amp'] = amp_vec[aa]
sim_params['mu1'] = mu1_vec[bb]
sim_params['mu2'] = mu2_vec[cc]
sim_params['mu3'] = mu3
sim_params['mu4'] = mu4
sim_params['dt'] = dt
sim_params['tf'] = tf
# initialize dendrite
dendrite_1 = dendrite(
'dendrite_under_test',
inhibitory_or_excitatory='excitatory',
circuit_inductances=[
10e-12, 26e-12, 200e-12, 77.5e-12
],
input_synaptic_connections=[],
input_synaptic_inductances=[[]],
input_dendritic_connections=[],
input_dendritic_inductances=[[]],
input_direct_connections=['input_dendritic_drive'],
input_direct_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[72e-6, 29e-6, 35e-6],
integration_loop_self_inductance=L_di_vec[ii],
integration_loop_output_inductance=0e-12,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di_vec[ii],
integration_loop_saturation_current=11.75e-6,
dendrite_model_params=sim_params)
dendrite_1.run_sim()
actual_data = np.vstack(
(input_1.time_vec[:], dendrite_1.I_di[:, 0]))
error_mat_1[aa, bb, cc] = chi_squared_error(
target_data, actual_data)
error_mat_master__mu1_mu2 += error_mat_1
ind_best = np.where(
error_mat_1 == np.amin(error_mat_1)) #error_mat.argmin()
amp_best_mu12 = amp_vec[ind_best[0]][0]
mu1_best = mu1_vec[ind_best[1]][0]
mu2_best = mu2_vec[ind_best[2]][0]
print('\n\namp_best_mu12 = {}'.format(amp_best_mu12))
print('mu1_best = {}'.format(mu1_best))
print('mu2_best = {}\n\n'.format(mu2_best))
best_params['amp_mu12'].append(amp_best_mu12)
best_params['mu1'].append(mu1_best)
best_params['mu2'].append(mu2_best)
data_array['error_mat__amp_mu1_mu2'] = error_mat_1
#plot errors
title_string = '{}\namp_best_mu12 = {:2.2f}, mu1_best = {:1.2f}, mu2_best = {:1.2f}'.format(
data_file_list[ii], amp_best_mu12, mu1_best, mu2_best)
save_str = '{}__error__mu1_mu2'.format(data_file_list[ii])
for aa in range(num_amps):
plot_error_mat(error_mat_1[aa, :, :], mu1_vec, mu2_vec, 'mu1',
'mu2', 'amp = {}'.format(amp_vec[aa]), title_string,
save_str)
#repeat best one and plot
if drive_info['drive_type'] == 'piecewise_linear' or drive_info[
'drive_type'] == 'linear_ramp':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
piecewise_linear=pwl_drive)
dendritic_drive = dendritic_drive__piecewise_linear(
input_1.time_vec, pwl_drive)
if drive_info['drive_type'] == 'sq_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
square_pulse_train=sq_pls_trn_params)
dendritic_drive = dendritic_drive__square_pulse_train(
input_1.time_vec, sq_pls_trn_params)
if drive_info['drive_type'] == 'exp_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
exponential_pulse_train=exp_pls_trn_params)
dendritic_drive = dendritic_drive__exp_pls_train__LR(
input_1.time_vec, exp_pls_trn_params)
sim_params = dict()
sim_params['amp'] = amp_best_mu12
sim_params['mu1'] = mu1_best
sim_params['mu2'] = mu2_best
sim_params['mu3'] = mu3
sim_params['mu4'] = mu4
sim_params['dt'] = dt
sim_params['tf'] = tf
dendrite_1 = dendrite(
'dendrite_under_test',
inhibitory_or_excitatory='excitatory',
circuit_inductances=[10e-12, 26e-12, 200e-12, 77.5e-12],
input_synaptic_connections=[],
input_synaptic_inductances=[[]],
input_dendritic_connections=[],
input_dendritic_inductances=[[]],
input_direct_connections=['input_dendritic_drive'],
input_direct_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[72e-6, 29e-6, 35e-6],
integration_loop_self_inductance=L_di_vec[ii],
integration_loop_output_inductance=0e-12,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di_vec[ii],
integration_loop_saturation_current=11.75e-6,
dendrite_model_params=sim_params)
dendrite_1.run_sim()
# plot_dendritic_integration_loop_current(dendrite_1)
actual_data = np.vstack((input_1.time_vec[:], dendrite_1.I_di[:, 0]))
main_title = '{}\namp_best_{:2.2f}_mu1_best_{:1.4f}_mu2_best_{:1.4f}'.format(
data_file_list[ii], amp_best_mu12, mu1_best, mu2_best)
error__signal = np.amin(error_mat_1)
plot_wr_comparison__drive_and_response(main_title, target_data__drive,
actual_data__drive, target_data,
actual_data, data_file_list[ii],
error__drive, error__signal)
#-----------------------------
# find best amp_mu34, mu3, mu4
#-----------------------------
error_mat_2 = np.zeros([num_amps, num_mu3, num_mu4])
print('seeking amp, mu3, mu4 ...')
for aa in range(num_amps):
for bb in range(num_mu1):
for cc in range(num_mu2):
print('aa = {} of {}, bb = {} of {}, cc = {} of {}'.format(
aa + 1, num_amps, bb + 1, num_mu3, cc + 1, num_mu4))
# initialize input signal
if drive_info[
'drive_type'] == 'piecewise_linear' or drive_info[
'drive_type'] == 'linear_ramp':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
piecewise_linear=pwl_drive)
dendritic_drive = dendritic_drive__piecewise_linear(
input_1.time_vec, pwl_drive)
if drive_info['drive_type'] == 'sq_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
square_pulse_train=sq_pls_trn_params)
dendritic_drive = dendritic_drive__square_pulse_train(
input_1.time_vec, sq_pls_trn_params)
if drive_info['drive_type'] == 'exp_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
exponential_pulse_train=exp_pls_trn_params)
dendritic_drive = dendritic_drive__exp_pls_train__LR(
input_1.time_vec, exp_pls_trn_params)
# create sim_params dictionary
sim_params = dict()
sim_params['amp'] = amp_vec[aa]
sim_params['mu1'] = mu1_best
sim_params['mu2'] = mu2_best
sim_params['mu3'] = mu3_vec[bb]
sim_params['mu4'] = mu4_vec[cc]
sim_params['dt'] = dt
sim_params['tf'] = tf
# initialize dendrite
dendrite_1 = dendrite(
'dendrite_under_test',
inhibitory_or_excitatory='excitatory',
circuit_inductances=[
10e-12, 26e-12, 200e-12, 77.5e-12
],
input_synaptic_connections=[],
input_synaptic_inductances=[[]],
input_dendritic_connections=[],
input_dendritic_inductances=[[]],
input_direct_connections=['input_dendritic_drive'],
input_direct_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[72e-6, 29e-6, 35e-6],
integration_loop_self_inductance=L_di_vec[ii],
integration_loop_output_inductance=0e-12,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di_vec[ii],
integration_loop_saturation_current=11.75e-6,
dendrite_model_params=sim_params)
dendrite_1.run_sim()
actual_data = np.vstack(
(input_1.time_vec[:], dendrite_1.I_di[:, 0]))
error_mat_2[aa, bb, cc] = chi_squared_error(
target_data, actual_data)
error_mat_master__mu3_mu4 += error_mat_2
ind_best = np.where(
error_mat_2 == np.amin(error_mat_2)) #error_mat.argmin()
amp_best_mu34 = amp_vec[ind_best[0]][0]
mu3_best = mu3_vec[ind_best[1]][0]
mu4_best = mu4_vec[ind_best[2]][0]
print('\n\namp_best_mu34 = {}'.format(amp_best_mu34))
print('mu3_best = {}'.format(mu3_best))
print('mu4_best = {}'.format(mu4_best))
best_params['amp_mu34'].append(amp_best_mu34)
best_params['mu3'].append(mu3_best)
best_params['mu4'].append(mu4_best)
data_array['error_mat__mu3_mu4'] = error_mat_2
#plot errors
title_string = '{}\namp_best_mu12 = {:2.2f}, amp_best_mu34 = {:2.2f}, mu1_best = {:1.2f}, mu2_best = {:1.2f}, mu3_best = {:1.2f}, mu4_best = {:1.2f}'.format(
data_file_list[ii], amp_best_mu12, amp_best_mu34, mu1_best,
mu2_best, mu3_best, mu4_best)
save_str = '{}__error__mu3_mu4'.format(data_file_list[ii])
for aa in range(num_amps):
plot_error_mat(error_mat_2[aa, :, :], mu3_vec, mu4_vec, 'mu3',
'mu4', 'amp = {}'.format(amp_vec[aa]), title_string,
save_str)
#repeat best one and plot
if drive_info['drive_type'] == 'piecewise_linear' or drive_info[
'drive_type'] == 'linear_ramp':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
piecewise_linear=pwl_drive)
dendritic_drive = dendritic_drive__piecewise_linear(
input_1.time_vec, pwl_drive)
if drive_info['drive_type'] == 'sq_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
square_pulse_train=sq_pls_trn_params)
dendritic_drive = dendritic_drive__square_pulse_train(
input_1.time_vec, sq_pls_trn_params)
if drive_info['drive_type'] == 'exp_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
exponential_pulse_train=exp_pls_trn_params)
dendritic_drive = dendritic_drive__exp_pls_train__LR(
input_1.time_vec, exp_pls_trn_params)
sim_params = dict()
sim_params['amp'] = amp_best_mu34
sim_params['mu1'] = mu1_best
sim_params['mu2'] = mu2_best
sim_params['mu3'] = mu3_best
sim_params['mu4'] = mu4_best
sim_params['dt'] = dt
sim_params['tf'] = tf
dendrite_1 = dendrite(
'dendrite_under_test',
inhibitory_or_excitatory='excitatory',
circuit_inductances=[10e-12, 26e-12, 200e-12, 77.5e-12],
input_synaptic_connections=[],
input_synaptic_inductances=[[]],
input_dendritic_connections=[],
input_dendritic_inductances=[[]],
input_direct_connections=['input_dendritic_drive'],
input_direct_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[72e-6, 29e-6, 35e-6],
integration_loop_self_inductance=L_di_vec[ii],
integration_loop_output_inductance=0e-12,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di_vec[ii],
integration_loop_saturation_current=11.75e-6,
dendrite_model_params=sim_params)
dendrite_1.run_sim()
main_title = '{}\namp_best_mu12_{:2.2f}_amp_best_mu34_{:2.2f}_mu1_best_{:1.2f}_mu2_best_{:1.2f}_mu3_best_{:1.2f}_mu4_best_{:1.2f}'.format(
data_file_list[ii], amp_best_mu12, amp_best_mu34, mu1_best,
mu2_best, mu3_best, mu4_best)
actual_data = np.vstack((input_1.time_vec[:], dendrite_1.I_di[:, 0]))
error__signal = np.amin(error_mat_2)
plot_wr_comparison__drive_and_response(main_title, target_data__drive,
actual_data__drive, target_data,
actual_data, data_file_list[ii],
error__drive, error__signal)
# save data
save_string = 'wr_fits__finding_amp_mu1_mu2+mu3_mu4'
data_array['wr_spice_data_file_list'] = data_file_list
data_array['best_params'] = best_params
data_array['error_mat_master__mu1_mu2'] = error_mat_master__mu1_mu2
data_array['error_mat_master__mu3_mu4'] = error_mat_master__mu3_mu4
print('\n\nsaving session data ...')
save_session_data(data_array, save_string)
#plot errors
title_string = '{}\namp_best_mu12 = {:2.2f}, amp_best_mu34 = {:2.2f}, mu1_best = {:1.2f}, mu2_best = {:1.2f}, mu3_best = {:1.2f}, mu4_best = {:1.2f}'.format(
master_error_plot_name, amp_best_mu12, amp_best_mu34, mu1_best,
mu2_best, mu3_best, mu4_best)
for aa in range(num_amps):
save_str_1 = '{}__master_error__mu1_mu2__amp_{:2.2f}'.format(
master_error_plot_name, amp_vec[aa])
save_str_2 = '{}__master_error__mu3_mu4__amp_{:2.2f}'.format(
master_error_plot_name, amp_vec[aa])
plot_error_mat(error_mat_master__mu1_mu2[aa, :, :], mu1_vec, mu2_vec,
'mu1', 'mu2', 'amp = {}'.format(amp_vec[aa]),
title_string, save_str_1)
plot_error_mat(error_mat_master__mu3_mu4[aa, :, :], mu3_vec, mu4_vec,
'mu3', 'mu4', 'amp = {}'.format(amp_vec[aa]),
title_string, save_str_2)
return best_params, error_mat_master__mu1_mu2, error_mat_master__mu3_mu4
error_drive_mat = np.zeros([num_files, len(dt_vec)])
for qq in range(len(dt_vec)):
sim_params = dict()
sim_params['dt'] = dt_vec[qq]
sim_params['tf'] = tf
sim_params['synapse_model'] = 'lookup_table'
for ii in range(num_files):
print('\nqq = {} of {}, ii = {} of {}'.format(qq + 1, len(dt_vec),
ii + 1, num_files))
#load WR data
data_dict = read_wr_data('wrspice_data/test_data/2jj/' +
data_file_list[ii])
target_drive = np.vstack((data_dict['time'], data_dict['L0#branch']))
target_data = np.vstack((data_dict['time'], data_dict['L2#branch']))
# initialize input signal
input_1 = input_signal('in',
input_temporal_form='arbitrary_spike_train',
spike_times=spike_times)
# initialize synapse
synapse_1 = synapse('sy',
num_jjs=2,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_si_vec[ii],
integration_loop_self_inductance=L_si_vec[ii],
integration_loop_output_inductance=0e-12,
def dendrite_model__parameter_sweep(data_file_list, L_di_vec, tau_di_vec,
dt_vec, tf_vec, drive_info, gamma1_vec,
gamma2_vec, master_error_plot_name):
master_error_plot_name = 'mstr_err__' + master_error_plot_name
best_params = dict()
best_params['gamma1'] = []
best_params['gamma2'] = []
data_array = dict()
data_array['gamma1_vec'] = gamma1_vec
data_array['gamma2_vec'] = gamma2_vec
num_sims = len(data_file_list)
num_gamma1 = len(gamma1_vec)
num_gamma2 = len(gamma2_vec)
print(
'\n\nrunning dendrite_model__parameter_sweep\n\nnum_files = {:d}\nnum_gamma1 = {:d}\nnum_gamma2 = {:d}'
.format(num_sims, num_gamma1, num_gamma2))
error_mat_master__gamma1_gamma2 = np.zeros([num_gamma1, num_gamma2])
if drive_info['drive_type'] == 'piecewise_linear':
pwl_drive = drive_info['pwl_drive']
directory_string = 'constant_drive'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'linear_ramp':
pwl_drive = drive_info['pwl_drive']
directory_string = 'linear_ramp'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'sq_pls_trn':
sq_pls_trn_params = drive_info['sq_pls_trn_params']
directory_string = 'square_pulse_sequence'
wr_drive_string = '@I0[c]'
wr_target_string = 'L9#branch'
if drive_info['drive_type'] == 'exp_pls_trn':
exp_pls_trn_params = drive_info['exp_pls_trn_params']
directory_string = 'exponential_pulse_sequence'
wr_drive_string = 'L5#branch'
wr_target_string = 'L10#branch'
for ii in range(num_sims):
print('\ndata_file {} of {}\n'.format(ii + 1, num_sims))
plt.close('all')
dt = dt_vec[ii]
tf = tf_vec[ii]
# WR data
print('reading wr data ...\n')
file_name = data_file_list[ii] + '.dat'
data_dict = read_wr_data('wrspice_data/' + directory_string + '/' +
file_name)
target_data = np.vstack(
(data_dict['time'], data_dict[wr_target_string]))
#----------------------
# compare drive signals
#----------------------
target_data__drive = np.vstack(
(data_dict['time'], data_dict[wr_drive_string]))
# initialize input signal
if drive_info['drive_type'] == 'piecewise_linear' or drive_info[
'drive_type'] == 'linear_ramp':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
piecewise_linear=pwl_drive)
dendritic_drive = dendritic_drive__piecewise_linear(
input_1.time_vec, pwl_drive)
if drive_info['drive_type'] == 'sq_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
square_pulse_train=sq_pls_trn_params)
dendritic_drive = dendritic_drive__square_pulse_train(
input_1.time_vec, sq_pls_trn_params)
if drive_info['drive_type'] == 'exp_pls_trn':
input_1 = input_signal(
'input_dendritic_drive',
input_temporal_form='analog_dendritic_drive',
output_inductance=200e-12,
time_vec=np.arange(0, tf + dt, dt),
exponential_pulse_train=exp_pls_trn_params)
dendritic_drive = dendritic_drive__exp_pls_train__LR(
input_1.time_vec, exp_pls_trn_params)
actual_data__drive = np.vstack(
(input_1.time_vec[:], dendritic_drive[:, 0]))
print('comparing drive signals ...\n')
error__drive = chi_squared_error(target_data__drive,
actual_data__drive)
#------------------------
# find best amp, gamma1, gamma2
#------------------------
error_mat_1 = np.zeros([num_gamma1, num_gamma2])
print('seeking gamma1, gamma2 ...')
for aa in range(num_gamma1):
for bb in range(num_gamma2):
print('aa = {} of {}, bb = {} of {}'.format(
aa + 1, num_gamma1, bb + 1, num_gamma2))
# create sim_params dictionary
sim_params = dict()
sim_params['gamma1'] = gamma1_vec[aa]
sim_params['gamma2'] = gamma2_vec[bb]
sim_params['dt'] = dt
sim_params['tf'] = tf
# initialize dendrite
dendrite_1 = dendrite(
'dendrite_under_test',
inhibitory_or_excitatory='excitatory',
circuit_inductances=[10e-12, 26e-12, 200e-12, 77.5e-12],
input_synaptic_connections=[],
input_synaptic_inductances=[[]],
input_dendritic_connections=[],
input_dendritic_inductances=[[]],
input_direct_connections=['input_dendritic_drive'],
input_direct_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[72e-6, 29e-6, 35e-6],
integration_loop_self_inductance=L_di_vec[ii],
integration_loop_output_inductance=0e-12,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di_vec[ii],
integration_loop_saturation_current=11.75e-6,
dendrite_model_params=sim_params)
dendrite_1.run_sim()
actual_data = np.vstack(
(input_1.time_vec[:], dendrite_1.I_di[:, 0]))
error_mat_1[aa,
bb] = chi_squared_error(target_data, actual_data)
error_mat_master__gamma1_gamma2 += error_mat_1
ind_best = np.where(
error_mat_1 == np.amin(error_mat_1)) #error_mat.argmin()
gamma1_best = gamma1_vec[ind_best[0]][0]
gamma2_best = gamma2_vec[ind_best[1]][0]
print('gamma1_best = {}'.format(gamma1_best))
print('gamma2_best = {}\n\n'.format(gamma2_best))
best_params['gamma1'].append(gamma1_best)
best_params['gamma2'].append(gamma2_best)
data_array['error_mat__amp_gamma1_gamma2'] = error_mat_1
#plot errors
title_string = '{}\ngamma1_best = {:1.2f}, gamma2_best = {:1.2f}'.format(
data_file_list[ii], gamma1_best, gamma2_best)
save_str = '{}__error__gamma1_gamma2'.format(data_file_list[ii])
# plot_error_mat(error_mat_1[:,:],gamma1_vec,gamma2_vec,'gamma1','gamma2',title_string,save_str)
# #repeat best one and plot
# if drive_info['drive_type'] == 'piecewise_linear' or drive_info['drive_type'] == 'linear_ramp':
# input_1 = input_signal('input_dendritic_drive', input_temporal_form = 'analog_dendritic_drive', output_inductance = 200e-12,
# time_vec = np.arange(0,tf+dt,dt), piecewise_linear = pwl_drive)
# dendritic_drive = dendritic_drive__piecewise_linear(input_1.time_vec,pwl_drive)
# if drive_info['drive_type'] == 'sq_pls_trn':
# input_1 = input_signal('input_dendritic_drive', input_temporal_form = 'analog_dendritic_drive', output_inductance = 200e-12,
# time_vec = np.arange(0,tf+dt,dt), square_pulse_train = sq_pls_trn_params)
# dendritic_drive = dendritic_drive__square_pulse_train(input_1.time_vec,sq_pls_trn_params)
# if drive_info['drive_type'] == 'exp_pls_trn':
# input_1 = input_signal('input_dendritic_drive', input_temporal_form = 'analog_dendritic_drive', output_inductance = 200e-12,
# time_vec = np.arange(0,tf+dt,dt), exponential_pulse_train = exp_pls_trn_params)
# dendritic_drive = dendritic_drive__exp_pls_train__LR(input_1.time_vec,exp_pls_trn_params)
# sim_params = dict()
# sim_params['gamma1'] = gamma1_best
# sim_params['gamma2'] = gamma2_best
# sim_params['dt'] = dt
# sim_params['tf'] = tf
# dendrite_1 = dendrite('dendrite_under_test', inhibitory_or_excitatory = 'excitatory', circuit_inductances = [10e-12,26e-12,200e-12,77.5e-12],
# input_synaptic_connections = [], input_synaptic_inductances = [[]],
# input_dendritic_connections = [], input_dendritic_inductances = [[]],
# input_direct_connections = ['input_dendritic_drive'], input_direct_inductances = [[10e-12,1]],
# thresholding_junction_critical_current = 40e-6, bias_currents = [72e-6,29e-6,35e-6],
# integration_loop_self_inductance = L_di_vec[ii], integration_loop_output_inductance = 0e-12,
# integration_loop_temporal_form = 'exponential', integration_loop_time_constant = tau_di_vec[ii],
# integration_loop_saturation_current = 11.75e-6, dendrite_model_params = sim_params)
# dendrite_1.run_sim()
# # plot_dendritic_integration_loop_current(dendrite_1)
# actual_data = np.vstack((input_1.time_vec[:],dendrite_1.I_di[:,0]))
main_title = '{}\ngamma1_best_{:1.4f}_gamma2_best_{:1.4f}'.format(
data_file_list[ii], gamma1_best, gamma2_best)
error__signal = np.amin(error_mat_1)
plot_wr_comparison__dend_drive_and_response(
main_title, target_data__drive, actual_data__drive, target_data,
actual_data, data_file_list[ii], error__drive, error__signal)
# # save data
# save_string = 'wr_fits__finding_gamma1_gamma2'
# data_array['wr_spice_data_file_list'] = data_file_list
# data_array['best_params'] = best_params
# data_array['error_mat_master__gamma1_gamma2'] = error_mat_master__gamma1_gamma2
# print('\n\nsaving session data ...')
# save_session_data(data_array,save_string)
# #plot errors
# title_string = '{}; gamma1_best = {:1.2f}, gamma2_best = {:1.2f}'.format(master_error_plot_name,gamma1_best,gamma2_best)
# save_str_1 = '{}__master_error__gamma1_gamma2'.format(master_error_plot_name)
# plot_error_mat(error_mat_master__gamma1_gamma2[:,:],gamma1_vec,gamma2_vec,'gamma1','gamma2',title_string,save_str_1)
return best_params, error_mat_master__gamma1_gamma2
def synapse_model__parameter_sweep(data_file_list, I_sy_vec, L_si_vec,
tau_si_vec, dt, tf, spike_times, gamma1_vec,
gamma2_vec, gamma3_vec,
master_error_plot_name):
gamma3_init = 1
master_error_plot_name = 'mstr_err__' + master_error_plot_name
best_params = dict()
best_params['gamma1'] = []
best_params['gamma2'] = []
best_params['gamma3'] = []
data_array = dict()
data_array['gamma1_vec'] = gamma1_vec
data_array['gamma2_vec'] = gamma2_vec
data_array['gamma3_vec'] = gamma3_vec
num_sims = len(data_file_list)
num_gamma1 = len(gamma1_vec)
num_gamma2 = len(gamma2_vec)
num_gamma3 = len(gamma3_vec)
print(
'\n\nrunning synapse_model__parameter_sweep\n\nnum_files = {:d}\nnum_gamma1 = {:d}\nnum_gamma2 = {:d}'
.format(num_sims, num_gamma1, num_gamma2))
#------------------------
# find best gamma1, gamma2
#------------------------
error_mat_master__gamma1_gamma2 = np.zeros([num_gamma1, num_gamma2])
for ii in range(num_sims):
print('\ndata_file {} of {}\n'.format(ii + 1, num_sims))
# plt.close('all')
# WR data
directory = 'wrspice_data/fitting_data'
file_name = data_file_list[ii]
data_dict = read_wr_data(directory + '/' + file_name)
target_data = np.vstack((data_dict['time'], data_dict['L3#branch']))
wr_drive = np.vstack((data_dict['time'], data_dict['L0#branch']))
# initialize input signal
name__i = 'in'
input_1 = input_signal(name__i,
input_temporal_form='arbitrary_spike_train',
spike_times=spike_times)
error_mat_1 = np.zeros([num_gamma1, num_gamma2])
print('\nseeking gamma1, gamma2 ...\n')
for aa in range(num_gamma1):
for bb in range(num_gamma2):
print('aa = {} of {}, bb = {} of {}'.format(
aa + 1, num_gamma1, bb + 1, num_gamma2))
# create sim_params dictionary
sim_params = dict()
sim_params['gamma1'] = gamma1_vec[aa]
sim_params['gamma2'] = gamma2_vec[bb]
sim_params['gamma3'] = gamma3_init
sim_params['dt'] = dt
sim_params['tf'] = tf
# initialize synapse
name_s = 'sy'
synapse_1 = synapse(
name_s,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_si_vec[ii],
integration_loop_self_inductance=L_si_vec[ii],
integration_loop_output_inductance=0e-12,
synaptic_bias_current=I_sy_vec[ii],
integration_loop_bias_current=35e-6,
input_signal_name='in',
synapse_model_params=sim_params)
synapse_1.run_sim()
actual_data = np.vstack(
(synapse_1.time_vec[:], synapse_1.I_si[:, 0]))
error_mat_1[aa,
bb] = chi_squared_error(target_data, actual_data)
plot_wr_comparison__synapse(
file_name + '; gamma1 = {:f}, gamma2 = {:f}'.format(
gamma1_vec[aa], gamma2_vec[bb]), spike_times, wr_drive,
target_data, actual_data, file_name, error_mat_1[aa, bb])
error_mat_master__gamma1_gamma2 += error_mat_1
ind_best = np.where(
error_mat_1 == np.amin(error_mat_1)) #error_mat.argmin()
gamma1_best = gamma1_vec[ind_best[0]][0]
gamma2_best = gamma2_vec[ind_best[1]][0]
print('gamma1_best = {}'.format(gamma1_best))
print('gamma2_best = {}\n\n'.format(gamma2_best))
best_params['gamma1'].append(gamma1_best)
best_params['gamma2'].append(gamma2_best)
data_array['error_mat__gamma1_gamma2'] = error_mat_1
#plot errors
# title_string = '{}\ngamma1_best = {:1.2f}, gamma2_best = {:1.2f}'.format(data_file_list[ii],gamma1_best,gamma2_best)
# save_str = '{}__error__gamma1_gamma2'.format(data_file_list[ii])
# plot_error_mat(error_mat_1[:,:],gamma1_vec,gamma2_vec,'gamma1','gamma2',title_string,save_str)
# #repeat best one and plot
sim_params = dict()
sim_params['gamma1'] = gamma1_best
sim_params['gamma2'] = gamma2_best
sim_params['gamma3'] = gamma3_init
sim_params['dt'] = dt
sim_params['tf'] = tf
# initialize synapse
name_s = 'sy'
synapse_1 = synapse(name_s,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_si_vec[ii],
integration_loop_self_inductance=L_si_vec[ii],
integration_loop_output_inductance=0e-12,
synaptic_bias_current=I_sy_vec[ii],
integration_loop_bias_current=35e-6,
input_signal_name='in',
synapse_model_params=sim_params)
synapse_1.run_sim()
actual_data = np.vstack((synapse_1.time_vec[:], synapse_1.I_si[:, 0]))
error__si = chi_squared_error(target_data, actual_data)
main_title = '{}\ngamma1_best_{:6.4f}_gamma2_best_{:6.4f}'.format(
data_file_list[ii], gamma1_best, gamma2_best)
plot_wr_comparison__synapse(main_title, spike_times, wr_drive,
target_data, actual_data, file_name,
error__si)
# # save data
# save_string = 'wr_fits__finding_gamma1_gamma2'
# data_array['wr_spice_data_file_list'] = data_file_list
# data_array['best_params'] = best_params
# data_array['error_mat_master__gamma1_gamma2'] = error_mat_master__gamma1_gamma2
# print('\n\nsaving session data ...')
# save_session_data(data_array,save_string)
#plot errors
title_string = '{}; gamma1_best = {:6.4f}, gamma2_best = {:6.4f}'.format(
master_error_plot_name, gamma1_best, gamma2_best)
save_str_1 = '{}__master_error__gamma1_gamma2'.format(
master_error_plot_name)
plot_error_mat(error_mat_master__gamma1_gamma2[:, :], gamma1_vec,
gamma2_vec, 'gamma1', 'gamma2', title_string, save_str_1)
#-----------------
# find best gamma3
#-----------------
# error_mat_master__gamma3 = np.zeros([num_gamma3])
# for ii in range(num_sims):
# print('\ndata_file {} of {}\n'.format(ii+1,num_sims))
# # plt.close('all')
# # WR data
# directory = 'wrspice_data/fitting_data'
# file_name = data_file_list[ii]
# data_dict = read_wr_data(directory+'/'+file_name)
# target_data = np.vstack((data_dict['time'],data_dict['L3#branch']))
# wr_drive = np.vstack((data_dict['time'],data_dict['L0#branch']))
# # initialize input signal
# name__i = 'in'
# input_1 = input_signal(name__i, input_temporal_form = 'arbitrary_spike_train', spike_times = spike_times)
# error_mat_2 = np.zeros([num_gamma3])
# print('\nseeking gamma3 ...\n')
# for aa in range(num_gamma3):
# print('aa = {} of {}'.format(aa+1,num_gamma3))
# # create sim_params dictionary
# sim_params = dict()
# sim_params['gamma1'] = gamma1_best
# sim_params['gamma2'] = gamma2_best
# sim_params['gamma3'] = gamma3_vec[aa]
# sim_params['dt'] = dt
# sim_params['tf'] = tf
# # initialize synapse
# name_s = 'sy'
# synapse_1 = synapse(name_s, integration_loop_temporal_form = 'exponential', integration_loop_time_constant = tau_si_vec[ii],
# integration_loop_self_inductance = L_si_vec[ii], integration_loop_output_inductance = 0e-12,
# synaptic_bias_current = I_sy_vec[ii], integration_loop_bias_current = 35e-6,
# input_signal_name = 'in', synapse_model_params = sim_params)
# synapse_1.run_sim()
# actual_data = np.vstack((synapse_1.time_vec[:],synapse_1.I_si[:,0]))
# error_mat_2[aa] = chi_squared_error(target_data,actual_data)
# # plot_wr_comparison__synapse(file_name+'gamma1 = {:f}, gamma2 = {:f}'.format(gamma1_vec[aa],gamma2_vec[bb]),spike_times,wr_drive,target_data,actual_data,file_name,error_mat_1[aa,bb])
# error_mat_master__gamma3 += error_mat_2
# ind_best = np.where(error_mat_2 == np.amin(error_mat_2))#error_mat.argmin()
# gamma3_best = gamma3_vec[ind_best[0]][0]
# print('gamma3_best = {}'.format(gamma3_best))
# best_params['gamma3'].append(gamma3_best)
# data_array['error_mat__gamma3'] = error_mat_2
# #plot errors
# # title_string = '{}\ngamma1_best = {:1.2f}, gamma2_best = {:1.2f}'.format(data_file_list[ii],gamma1_best,gamma2_best)
# # save_str = '{}__error__gamma1_gamma2'.format(data_file_list[ii])
# # plot_error_mat(error_mat_1[:,:],gamma1_vec,gamma2_vec,'gamma1','gamma2',title_string,save_str)
# # repeat best one and plot
# sim_params = dict()
# sim_params['gamma1'] = gamma1_best
# sim_params['gamma2'] = gamma2_best
# sim_params['gamma3'] = gamma3_best
# sim_params['dt'] = dt
# sim_params['tf'] = tf
# # initialize synapse
# name_s = 'sy'
# synapse_1 = synapse(name_s, integration_loop_temporal_form = 'exponential', integration_loop_time_constant = tau_si_vec[ii],
# integration_loop_self_inductance = L_si_vec[ii], integration_loop_output_inductance = 0e-12,
# synaptic_bias_current = I_sy_vec[ii], integration_loop_bias_current = 35e-6,
# input_signal_name = 'in', synapse_model_params = sim_params)
# synapse_1.run_sim()
# actual_data = np.vstack((synapse_1.time_vec[:],synapse_1.I_si[:,0]))
# error__si = chi_squared_error(target_data,actual_data)
# main_title = '{}\ngamma1_best = {:6.4f}, gamma2_best = {:6.4f}\ngamma3_best_{:6.4f}'.format(data_file_list[ii],gamma1_best,gamma2_best,gamma3_best)
# plot_wr_comparison__synapse(main_title,spike_times,wr_drive,target_data,actual_data,file_name,error__si)
# # save data
# save_string = 'wr_fits__finding_gamma1_gamma2'
# data_array['wr_spice_data_file_list'] = data_file_list
# data_array['best_params'] = best_params
# data_array['error_mat_master__gamma1_gamma2'] = error_mat_master__gamma1_gamma2
# print('\n\nsaving session data ...')
# save_session_data(data_array,save_string)
#plot errors
# title_string = '{}; gamma3_best = {:6.4f}'.format(master_error_plot_name,gamma3_best)
# save_str_1 = '{}__master_error__gamma3'.format(master_error_plot_name)
# plot_error_vec(error_mat_master__gamma1_gamma2[:,:],gamma1_vec,gamma2_vec,'gamma1','gamma2',title_string,save_str_1)
return best_params, error_mat_master__gamma1_gamma2, error_mat_master__gamma3
plt.close('all')
#%% temporal
tf = 50e-9
dt = 0.1e-9
I_b1 = 72e-6
tau_di = 1000e-9
mu1_vec = np.linspace(0.75,1.25,5)
mu2_vec = np.linspace(0.75,1.25,5)
#%% get WRSpice data
file_name = '_dat__sweep_Iflux__01'
data_dict = read_wr_data('dendrite_testing/'+file_name)
#%% run it
error_mat = np.zeros([len(mu1_vec),len(mu2_vec)])
for ii in range(len(mu1_vec)):
print('\n\nii = {:d} of {:d}\n'.format(ii+1,len(mu1_vec)))
for jj in range(len(mu1_vec)):
print('jj = {:d} of {:d}'.format(jj+1,len(mu2_vec)))
input_2 = input_signal('input_dendritic_drive', input_temporal_form = 'analog_dendritic_drive', output_inductance = 200e-12,
time_vec = np.arange(0,tf+dt,dt), slope = 30e-6/48e-9, time_on = 2e-9)
# initialize dendrite
dendrite_1 = dendrite('intermediate_dendrite', inhibitory_or_excitatory = 'excitatory', circuit_inductances = [10e-12,26e-12,200e-12,77.5e-12],
input_synaptic_connections = [], input_synaptic_inductances = [[]],
import pickle
import numpy.matlib
# from soen_sim import input_signal, synapse, dendrite, neuron
from _plotting import plot_fq_peaks_and_average_voltage__isolated_JJ
from _functions import read_wr_data, syn_isolatedjj_voltage_fit
from util import physical_constants
p = physical_constants()
# plt.close('all')
#%% load wr data
Ic = 40 #uA
data_dict = read_wr_data('wrspice_data/fitting_data/jj_rate_vs_bias.dat')
#%% find peaks, find rates
time_vec = data_dict['time']
I_bias = data_dict['@I0[c]']
V_fq = data_dict['v(1)']
j_peaks, _ = find_peaks(V_fq, height=100e-6)
ind_max = V_fq[j_peaks].argmax()
j_peaks = j_peaks[ind_max:]
# find inter-fluxon intervals and fluxon generation rates versus bias current
j_ifi = np.diff(time_vec[j_peaks])
j_rate = 1 / j_ifi
