def inputs_calc(self,g_tt_vec,time_step_sim,numb_syn,w_vec_tt,syn_func,tau_syn,tt,delta_t,spike_trains_complete):
from Euler_Method import Euler # import Euler to perform the Euler integration method
for syn in range(1,numb_syn+1): # loop through synapses
for sp in range(1,len(spike_trains_complete[syn-1])): # loop through spikes
if spike_trains_complete[syn-1][sp-1]>=tt-time_step_sim and spike_trains_complete[syn-1][sp-1]<tt+time_step_sim:
g_tt_vec[syn-1]=g_tt_vec[syn-1]+w_vec_tt[syn-1]
# call function from Euler_Method to integrate the conductance
g_1=Euler()
g_tt_vec[syn-1]=g_1.euler_integration(syn_func, tau_syn, g_tt_vec[syn-1], tt, time_step_sim, delta_t) # gives us the value of the differential equation at certain time
return [g_tt_vec]
def inputs(self,g_tt_vec,time_step_sim,spikes,spiketrue,numb_syn,weight,syn_func,tau_syn,tt,counter,delta_t):
from Euler_Method import Euler
for syn in range(1,numb_syn+1): # loop over synapses
# get spike times:
if np.sum(spiketrue[np.where(np.tile(tt,len(spikes))==spikes)])>0:
# input is delivered
counter += 1
g_tt_vec[syn-1]=g_tt_vec[syn-1]+weight[syn-1]
# call function from Euler_Method to integrate the conductance
g_1=Euler()
g_tt_vec[syn-1]=g_1.euler_integration(syn_func, tau_syn, g_tt_vec[syn-1], tt, time_step_sim, delta_t) # gives us the value of the differential equation at certain time
return [g_tt_vec,counter]
def inputs_pois(self, g_tt_vec, time_step_sim, firing_rate, numb_syn,
weight, syn_func, tau_syn, tt, counter, delta_t):
# g_tt_vec contains all the conductances from the last timestep
# syn_func contains the function of the the conductances, of the synapse type (needed for Euler Integration)
# weight is the synaptic strength
# counter counts the number of inputs
from Euler_Method import Euler
for syn in range(1, numb_syn + 1):
if random.uniform(0, 1) <= (firing_rate * time_step_sim / 1000):
counter += 1
g_tt_vec[syn - 1] = g_tt_vec[syn - 1] + weight[syn - 1]
# call function from Euler_Method to integrate the conductance
g_1 = Euler()
g_tt_vec[syn - 1] = g_1.euler_integration(
syn_func, tau_syn, g_tt_vec[syn - 1], tt, time_step_sim,
delta_t
) # gives us the value of the differential equation at certain time
return [g_tt_vec, counter]
[g_i_tt_vec] = synapse_i.inputs_calc(g_i_tt_vec, time_step_sim,
numb_inh_syn, w_i_vec_tt, inhib_syn,
tau_i, tt, delta_t,
spike_trains_complete_i)
total_inh_input_tt = sum([ww * (E_i - V_tt) for ww in g_i_tt_vec])
# Include STDP in excitatory synapses. (1)
# Check if there was LTD by comparing all new pre-spikes in this timepoint with the latest post-spike.
w_e_vec_tt = exc_STDP.STDP_post_pre(last_post_spike - time_step_sim / 10,
spike_trains_complete_e, tt,
time_step_sim, w_e_vec_tt, tau_LTD,
A_LTD, w_max)
# update the postsynaptic neuron.
total_input_tt = total_exc_input_tt + total_inh_input_tt
V_1 = Euler()
V = V_1.euler_integration(memb_volt, arg_for_V, V_tt, tt, time_step_sim,
delta_t)
if V < V_thresh:
V_tt = V
else: # if threshold is reached -> spike & reset mem V
V_tt = V_reset
# log spike time (for figure)
V_vals.append(0)
t_vals.append(tt - time_step_sim / 10)
spike_times.append(tt)
number_spikes += 1
### include STDP in excitatory synapses. (2)
# check if there was LTP: compare this post-spike to all most recent pre-spikes.