cbar.minorticks_on()
fig.suptitle('$r_{tot}$ versus $I_{drive}$ and $I_{di}$')
# plt.title(title_string)
ax.set_xlabel(r'$I_{drive}$ [$\mu$A]')
ax.set_ylabel(r'$I_{di}$ [$\mu$A]')
plt.show()
# fig.savefig('figures/'+save_str+'__log.png')
#%% save data
save_string = 'master_rate_matrix'
data_array = dict()
data_array['master_rate_matrix'] = master_rate_matrix
data_array['I_drive_vec'] = I_drive_vec * 1e-6
data_array['I_di_list'] = I_di_list
print('\n\nsaving session data ...')
save_session_data(data_array, save_string)
#%% load test
data_array_imported = load_session_data(
'session_data__master_rate_matrix__2020-04-17_13-11-03.dat')
I_di_list__imported = data_array_imported['I_di_list']
I_drive_vec__imported = data_array_imported['I_drive_vec']
master_rate_matrix__imported = data_array_imported['master_rate_matrix']
I_drive_sought = 23.45e-6
I_drive_sought_ind = (
np.abs(np.asarray(I_drive_vec__imported) - I_drive_sought)).argmin()
I_di_sought = 14.552e-6
I_di_sought_ind = (
np.abs(np.asarray(I_di_list__imported[I_drive_sought_ind]) -
I_di_sought)).argmin()
plot_dend_rate_array(I_di_array=I_di_array__scaled,
I_drive_list=I_drive_list,
influx_list=influx_list,
master_rate_array=master_rate_array)
# save data
save_string = 'master_dnd_rate_array_{:1d}jj_Llft{:05.2f}_Lrgt{:05.2f}_Ide{:05.2f}'.format(
num_jjs, L_left_list[pp] + 10, L_right_list[pp], I_de_list[qq])
data_array = dict()
data_array['rate_array'] = master_rate_array
data_array['I_drive_list'] = I_drive_list
data_array['influx_list'] = influx_list
data_array['I_di_array'] = I_di_array__scaled
print('\n\nsaving session data ...\n\n')
# save_session_data(data_array,save_string)
save_session_data(data_array, save_string + '.soen', False)
#%% just plot
if 1 == 1:
L_left = 10
L_right = 20
I_de = 70
# file_name = 'master_dnd_rate_array_{:1d}jj_Llft{:05.2f}_Lrgt{:05.2f}_Ide{:05.2f}.soen'.format(num_jjs,L_left+10,L_right,I_de)
# plot_dend_rate_array(file_name = file_name)
for pp in range(num_L):
for qq in range(num_I_de):
file_name = 'master_dnd_rate_array_{:1d}jj_Llft{:05.2f}_Lrgt{:05.2f}_Ide{:05.2f}.soen'.format(
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
