# initialize synapses
name__s = 'input_synapse__{:d}_{:d}'.format(num_id,pp)
synapse_1 = synapse(name__s, integration_loop_temporal_form = 'exponential', integration_loop_time_constant = tau_si_vec[ii],
integration_loop_self_inductance = 50e-9, integration_loop_output_inductance = 200e-12,
synaptic_bias_current = I_sy_vec[kk], integration_loop_bias_current = 31e-6,
input_signal_name = name__i)
input_synapses.append(name__s)
coupling_factor = -0.0375*num_synapses+0.5375
input_inductances.append([10e-12,coupling_factor])
#initialize neuron
input_inductances.append([10e-12,0.5])
name__n = 'rate_encoding_neuron__{:d}'.format(num_id)
neuron_1 = neuron(name__n, input_synaptic_connections = input_synapses, input_synaptic_inductances = input_inductances,
thresholding_junction_critical_current = 40e-6, thresholding_junction_bias_current = 35e-6,
refractory_temporal_form = 'exponential', refractory_time_constant = tau_ref_vec[qq],
refractory_loop_self_inductance = 10e-9, refractory_loop_output_inductance = 200e-12)
# propagate in time
sim_params = dict()
sim_params['dt'] = dt
sim_params['pre_observation_duration'] = num_tau_sim*synapse_1.time_constant
sim_params['observation_duration'] = observation_duration
sim_params['num_tau_sim'] = num_tau_sim
neuron_1.sim_params = sim_params
neuron_1.run_sim()
# plot temporal response
# plot_save_string = 'I_sy={:2.2f}uA__tau_si={:04.2f}ns__tau_ref={:04.2f}ns__rate_in={:04.2f}MHz__dt={:04.2f}ns__obs={:04.2f}us__jitter={}ns'.format(1e6*I_sy_vec[kk],1e9*tau_si_vec[ii],1e9*tau_ref_vec[qq],1e-6*rate_vec[jj],1e9*dt,observation_duration*1e6,jitter_params[1])
# neuron_1.plot_receiving_loop_current(plot_save_string)
# neuron_1.plot_spike_train(plot_save_string)
synaptic_dendrite_circuit_inductances=[0, 20, 200, 77.5],
synaptic_dendrite_input_synaptic_inductance=[20, 1],
junction_critical_current=40,
bias_currents=[I_de, 36, 35],
integration_loop_self_inductance=L_di,
integration_loop_output_inductance=0,
integration_loop_time_constant=tau_di)
ne = neuron('ne',
input_synaptic_connections=['sy'],
input_synaptic_inductances=[[20, 1]],
junction_critical_current=40,
circuit_inductances=[0, 0, 200, 77.5],
refractory_loop_circuit_inductances=[0, 20, 200, 77.5],
refractory_time_constant=50,
refractory_thresholding_junction_critical_current=40,
refractory_loop_self_inductance=775,
refractory_loop_output_inductance=100,
refractory_bias_currents=[74, 36, 35],
refractory_receiving_input_inductance=[20, 1],
neuronal_receiving_input_refractory_inductance=[20, 1],
integration_loop_time_constant=25,
time_params=dict([['dt', dt], ['tf', tf]]))
ne.run_sim()
actual_data__drive = np.vstack(
(ne.time_vec[:], ne.synapses['sy'].I_spd2_vec[:]))
if ii == 0 and jj == 0 and kk == 0:
error__drive = chi_squared_error(target_data__drive,
actual_data__drive)
thresholding_junction_critical_current=40,
bias_currents=[72, 36, 35],
integration_loop_self_inductance=L_di,
integration_loop_output_inductance=0,
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_di)
time_params = dict()
time_params['dt'] = dt
time_params['tf'] = tf
neuron_1 = neuron(
name='dummy_neuron',
input_dendritic_connections=['dendrite_under_test'],
circuit_inductances=[0, 0, 200, 77.5],
input_dendritic_inductances=[[20, 1]],
refractory_loop_circuit_inductances=[0, 2, 200, 77.5],
refractory_time_constant=50,
refractory_thresholding_junction_critical_current=40,
refractory_loop_self_inductance=775,
refractory_loop_output_inductance=100,
refractory_bias_currents=[74, 36, 35],
refractory_receiving_input_inductance=[20, 1],
neuronal_receiving_input_refractory_inductance=[20, 1],
time_params=time_params)
neuron_1.run_sim()
dendrite_1.time_vec = neuron_1.time_vec
dendrite_1.I_di = neuron_1.dendrites['dendrite_under_test'].I_di_vec
plot_dendritic_integration_loop_current(dendrite_1)
neuron_1 = neuron(
'ne',
num_jjs=2,
circuit_inductances=[10e-12, 0e-12, 200e-12, 77.5e-12],
input_synaptic_connections=['sy'],
input_synaptic_inductances=[[10e-12, 1]],
thresholding_junction_critical_current=40e-6,
bias_currents=[76e-6, 36e-6, 35e-6],
integration_loop_self_inductance=775e-12,
integration_loop_output_inductances=[
[400e-12, 1], [300e-12, 1]
], # first is to drive latching JJ, second is to drive refractory dendrite; both are of the form [L_self,k]
integration_loop_temporal_form='exponential',
integration_loop_time_constant=5e-9,
refractory_temporal_form='exponential',
refractory_time_constant=10e-9,
refractory_thresholding_junction_critical_current=40e-6,
refractory_loop_self_inductance=775e-12,
refractory_loop_output_inductance=200e-12,
refractory_loop_circuit_inductances=[0e-12, 20e-12, 200e-12, 77.5e-12],
refractory_bias_currents=[72e-6, 36e-6, 35e-6],
neuronal_receiving_input_refractory_inductance=[20e-12, 1],
homeostatic_temporal_form='exponential',
homeostatic_time_constant=10e-9,
homeostatic_thresholding_junction_critical_current=40e-6,
homeostatic_loop_self_inductance=775e-12,
homeostatic_loop_output_inductance=200e-12,
homeostatic_loop_circuit_inductances=[20e-12, 20e-12, 200e-12, 77.5e-12],
homeostatic_bias_currents=[71.5e-6, 36e-6, 35e-6],
neuronal_receiving_input_homeostatic_inductance=[20e-12, 1],
time_params=time_params)
integration_loop_time_constant=tau_si)
#%% neuron
neuron_1 = neuron(
name='ne',
num_jjs=4,
input_synaptic_connections=['sy'],
input_synaptic_inductances=[[20e-12, 1]],
junction_critical_current=40e-6,
bias_currents=[76e-6, 36e-6, 35e-6],
circuit_inductances=[0e-12, 0e-12, 200e-12, 77.5e-12],
refractory_dendrite_num_jjs=4,
refractory_loop_circuit_inductances=[0e-12, 20e-12, 200e-12, 77.5e-12],
refractory_time_constant=25e-9,
refractory_junction_critical_current=40e-6,
refractory_loop_self_inductance=200e-12,
refractory_loop_output_inductance=100e-12,
refractory_bias_currents=[76e-6, 36e-6, 35e-6],
refractory_receiving_input_inductance=[20e-12, 1],
neuronal_receiving_input_refractory_inductance=[20e-12, 1],
integration_loop_self_inductance=0,
integration_loop_time_constant=25e-9,
integration_loop_output_inductances=[
[0e-12, 1], [100e-12, 1]
], # first is to drive latching JJ, second is to drive refractory dendrite; both are of the form [L_self,k]
time_params=dict([['dt', dt], ['tf', tf]]))
neuron_1.run_sim()
#%% plot
integration_loop_self_inductance = L_si,
integration_loop_output_inductance = L_msi,
integration_loop_time_constant = tau_si)
threshold_circuit = 'hTron'
if threshold_circuit == 'JJ':
ne = neuron(name = 'ne', num_jjs = num_jjs__ne,
input_synaptic_connections = ['sy'],
input_synaptic_inductances = [[20,1]], # pH
junction_critical_current = 40, # uA
bias_currents = [I_ne,36,35], # uA
circuit_inductances = [0,0,200,77.5], # pH
integration_loop_self_inductance = L_ni,
integration_loop_time_constant = tau_nf,
integration_loop_output_inductance = [400,1], # pH # inductor going into MI feeding threshold circuit [L,k]
neuronal_receiving_input_refractory_inductance = [20,1], # pH
threshold_circuit_inductances = [10,0,20], # pH
threshold_circuit_resistance = 0.8, # mOhm
threshold_circuit_bias_current = 35, # uA
threshold_junction_critical_current = 40, # uA
time_params = dict([['dt',dt],['tf',time_vec__wr[-1]]]))
ne.run_sim()
elif threshold_circuit == 'hTron':
ne = neuron(name = 'ne', num_jjs = num_jjs__ne,
input_synaptic_connections = ['sy'],
input_synaptic_inductances = [[20,1]], # pH
junction_critical_current = 40, # uA
synapse_1 = synapse(name__s,
loop_temporal_form='exponential',
time_constant=tau_si_vec[ii],
integration_loop_self_inductance=50e-9,
integration_loop_output_inductance=200e-12,
synaptic_bias_current=37e-6,
loop_bias_current=31e-6)
# print('synapse time constant = {}'.format(synapse_1.time_constant))
#initialize neuron
name__n = 'rate_encoding_neuron__{}'.format(ii * len(rate_vec) + jj)
neuron_1 = neuron(name__n,
input_connections={name__s},
input_inductances=[[10e-12, 0.5], [10e-12, 0.5]],
thresholding_junction_critical_current=40e-6,
threshold_bias_current=35e-6,
refractory_temporal_form='exponential',
refractory_time_constant=50e-9,
refractory_loop_self_inductance=10e-9,
refractory_loop_output_inductance=200e-12)
#propagate in time
t1 = t0 + num_tau_sim * synapse_1.time_constant
tf = t1 + observation_duration
time_vec = np.arange(t0, tf + dt, dt)
synapse_1.input_spike_times = np.arange(5e-9, tf + dt,
isi_input_vec[jj])
neuron_1.run_sim(time_vec)
# neuron_1.plot_receiving_loop_current(time_vec)
#calculate output rate in various ways by looking at spikes in observation_duration
# synapse_1.synaptic_bias_current = 35e-6
# synapse_1.run_sim(time_vec)
# synapse_1.plot_integration_loop_current(time_vec)
# synapse_1.synaptic_bias_current = 39e-6
# synapse_1.run_sim(time_vec)
# synapse_1.plot_integration_loop_current(time_vec)
#%% set up neuron
neuron_1 = neuron('test_neuron',
input_connections={'s0', 's1'},
input_inductances=[[10e-12, 0.5], [10e-12, 0.5],
[10e-12, 0.5]],
thresholding_junction_critical_current=40e-6,
threshold_bias_current=35e-6,
refractory_temporal_form='exponential',
refractory_loop_self_inductance=1e-9,
refractory_loop_output_inductance=200e-12)
# for obj in neuron.get_instances():
# print(obj.name)
# for obj in synapse.get_instances():
# # print(obj.name)
# if obj.unique_label in neuron_1.input_connections:
# print(obj.unique_label+'; '+obj.name)
#%% propagate in time
t0 = 0
integration_loop_temporal_form='exponential',
integration_loop_time_constant=tau_si_vec[jj],
integration_loop_self_inductance=10e-9,
integration_loop_output_inductance=200e-12,
synaptic_bias_current=I_sy,
integration_loop_bias_current=31e-6,
input_signal_name=name__i)
#initialize neuron
name__n = 'bursting_neuron__{:d}_{:d}'.format(ii, jj)
neuron_1 = neuron(name__n,
input_synaptic_connections=[name__s],
input_synaptic_inductances=[[10e-12, 0.5],
[10e-12, 0.5]],
thresholding_junction_critical_current=40e-6,
thresholding_junction_bias_current=I_th,
refractory_temporal_form='exponential',
refractory_time_constant=tau_ref_vec[ii],
refractory_loop_self_inductance=2e-9,
refractory_loop_output_inductance=200e-12,
refractory_loop_synaptic_bias_current=39e-6,
refractory_loop_saturation_current=50e-6)
# propagate in time
sim_params = dict()
sim_params['dt'] = dt
sim_params['pre_observation_duration'] = 0
sim_params['observation_duration'] = observation_duration
neuron_1.sim_params = sim_params
neuron_1.run_sim()
# plot temporal response
