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agent_timestep.pyx
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import numpy as np
def multitimestep_mutator(agent,popdata,numsteps):
for i in range(numsteps):
timestep_mutator(agent,popdata)
def timestep_mutator(a,popdata):
Npop = len(popdata)
newspikes = []
for i in range(Npop):
newspikes.append([])
# 1337-1345
for popid in range(len(popdata)):
a.dpmn_XPRE[popid] *= 0
a.dpmn_XPOST[popid] *= 0
for popid in range(len(popdata)):
a.ExtMuS_AMPA[popid] = a.MeanExtEff_AMPA[popid] * a.FreqExt_AMPA[popid] * .001 * a.MeanExtCon_AMPA[popid] * a.Tau_AMPA[popid]
a.ExtSigmaS_AMPA[popid] = a.MeanExtEff_AMPA[popid] * np.sqrt(a.Tau_AMPA[popid] * .5 * a.FreqExt_AMPA[popid] * .001 * a.MeanExtCon_AMPA[popid])
a.ExtS_AMPA[popid] += a.dt / a.Tau_AMPA[popid] * (-a.ExtS_AMPA[popid] + a.ExtMuS_AMPA[popid]) + a.ExtSigmaS_AMPA[popid] * np.sqrt(a.dt * 2. / a.Tau_AMPA[popid]) * np.random.normal(size=len(a.Tau_AMPA[popid]))
a.LS_AMPA[popid] *= np.exp(-a.dt / a.Tau_AMPA[popid])
for src_popid in range(len(popdata)):
for dest_popid in range(len(popdata)):
if a.AMPA_con[src_popid][dest_popid] is not None:
for src_neuron in a.spikes[src_popid]:
a.LS_AMPA[dest_popid] += a.AMPA_eff[src_popid][dest_popid][src_neuron] * a.AMPA_con[src_popid][dest_popid][src_neuron]
a.dpmn_XPRE[dest_popid] = np.maximum(a.dpmn_XPRE[dest_popid], a.dpmn_cortex[src_popid][src_neuron] * a.AMPA_con[src_popid][dest_popid][src_neuron] * np.sign(a.dpmn_type[dest_popid]))
for popid in range(len(popdata)):
a.ExtMuS_GABA[popid] = a.MeanExtEff_GABA[popid] * a.FreqExt_GABA[popid] * .001 * a.MeanExtCon_GABA[popid] * a.Tau_GABA[popid]
a.ExtSigmaS_GABA[popid] = a.MeanExtEff_GABA[popid] * np.sqrt(a.Tau_GABA[popid] * .5 * a.FreqExt_GABA[popid] * .001 * a.MeanExtCon_GABA[popid])
a.ExtS_GABA[popid] += a.dt / a.Tau_GABA[popid] * (-a.ExtS_GABA[popid] + a.ExtMuS_GABA[popid]) + a.ExtSigmaS_GABA[popid] * np.sqrt(a.dt * 2. / a.Tau_GABA[popid]) * np.random.normal(size=len(a.Tau_AMPA[popid]))
a.LS_GABA[popid] *= np.exp(-a.dt / a.Tau_GABA[popid])
for src_popid in range(len(popdata)):
for dest_popid in range(len(popdata)):
if a.GABA_con[src_popid][dest_popid] is not None:
for src_neuron in a.spikes[src_popid]:
a.LS_GABA[dest_popid] += a.GABA_eff[src_popid][dest_popid][src_neuron] * a.GABA_con[src_popid][dest_popid][src_neuron]
for popid in range(len(popdata)):
a.ExtMuS_NMDA[popid] = a.MeanExtEff_NMDA[popid] * a.FreqExt_NMDA[popid] * .001 * a.MeanExtCon_NMDA[popid] * a.Tau_NMDA[popid]
a.ExtSigmaS_NMDA[popid] = a.MeanExtEff_NMDA[popid] * np.sqrt(a.Tau_NMDA[popid] * .5 * a.FreqExt_NMDA[popid] * .001 * a.MeanExtCon_NMDA[popid])
a.ExtS_NMDA[popid] += a.dt / a.Tau_NMDA[popid] * (-a.ExtS_NMDA[popid] + a.ExtMuS_NMDA[popid]) + a.ExtSigmaS_NMDA[popid] * np.sqrt(a.dt * 2. / a.Tau_NMDA[popid]) * np.random.normal(size=len(a.Tau_AMPA[popid]))
a.LS_NMDA[popid] *= np.exp(-a.dt / a.Tau_NMDA[popid])
a.timesincelastspike[popid] += a.dt
a.Ptimesincelastspike[popid] += a.dt
for src_popid in range(len(popdata)):
for dest_popid in range(len(popdata)):
if a.NMDA_con[src_popid][dest_popid] is not None:
for src_neuron in a.spikes[src_popid]:
ALPHA = 0.6332
a.LastConductanceNMDA[src_popid][dest_popid][src_neuron] *= np.exp(-a.Ptimesincelastspike[src_popid][src_neuron]/a.Tau_NMDA[dest_popid])
a.LS_NMDA[dest_popid] += a.NMDA_eff[src_popid][dest_popid][src_neuron] * a.NMDA_con[src_popid][dest_popid][src_neuron] * ALPHA * (1 - a.LastConductanceNMDA[src_popid][dest_popid][src_neuron])
a.LastConductanceNMDA[src_popid][dest_popid][src_neuron] += ALPHA * (1 - a.LastConductanceNMDA[src_popid][dest_popid][src_neuron])
# # 1347-1392
# # Compute the decay of the total conductances and add external input
# for p in range(Npop):
# selected = np.random.randint(len(a.N[p]))
# for i in range(len(a.N[p])):
# # AMPA
# freq = a.FreqExt_AMPA[p][i]
# efficacy = a.MeanExtEff_AMPA[p][i]
# a.ExtMuS_AMPA[p][i] = freq * .001 * efficacy * a.MeanExtCon_AMPA[p][i] * a.Tau_AMPA[p][i]
# a.ExtSigmaS_AMPA[p][i] = np.sqrt(a.Tau_AMPA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_AMPA[p][i])
# s = a.ExtSigmaS_AMPA[p][i]
# if s > 0:
# a.ExtS_AMPA[p][i] += a.dt / a.Tau_AMPA[p][i] * (-a.ExtS_AMPA[p][i] + a.ExtMuS_AMPA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_AMPA[p][i]) * np.random.normal()
# else:
# a.ExtS_AMPA[p][i] += a.dt / a.Tau_AMPA[p][i] * (-a.ExtS_AMPA[p][i] + a.ExtMuS_AMPA[p][i])
# a.LS_AMPA[p][i] *= np.exp(-a.dt / a.Tau_AMPA[p][i]) # decay
#
# # GABA
# freq = a.FreqExt_GABA[p][i]
# efficacy = a.MeanExtEff_GABA[p][i]
# a.ExtMuS_GABA[p][i] = freq * .001 * efficacy * a.MeanExtCon_GABA[p][i] * a.Tau_GABA[p][i]
# a.ExtSigmaS_GABA[p][i] = np.sqrt(a.Tau_GABA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_GABA[p][i])
# s = a.ExtSigmaS_GABA[p][i]
# if s > 0:
# a.ExtS_GABA[p][i] += a.dt / a.Tau_GABA[p][i] * (-a.ExtS_GABA[p][i] + a.ExtMuS_GABA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_GABA[p][i]) * np.random.normal()
# else:
# a.ExtS_GABA[p][i] += a.dt / a.Tau_GABA[p][i] * (-a.ExtS_GABA[p][i] + a.ExtMuS_GABA[p][i])
# a.LS_GABA[p][i] *= np.exp(-a.dt / a.Tau_GABA[p][i]) # decay
#
# # NMDA
# freq = a.FreqExt_NMDA[p][i]
# efficacy = a.MeanExtEff_NMDA[p][i]
# a.ExtMuS_NMDA[p][i] = freq * .001 * efficacy * a.MeanExtCon_NMDA[p][i] * a.Tau_NMDA[p][i]
# a.ExtSigmaS_NMDA[p][i] = np.sqrt(a.Tau_NMDA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_NMDA[p][i])
# s = a.ExtSigmaS_NMDA[p][i]
# if s > 0:
# a.ExtS_NMDA[p][i] += a.dt / a.Tau_NMDA[p][i] * (-a.ExtS_NMDA[p][i] + a.ExtMuS_NMDA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_NMDA[p][i]) * np.random.normal()
# else:
# a.ExtS_NMDA[p][i] += a.dt / a.Tau_NMDA[p][i] * (-a.ExtS_NMDA[p][i] + a.ExtMuS_NMDA[p][i])
# a.LS_NMDA[p][i] *= np.exp(-a.dt / a.Tau_NMDA[p][i]) # decay
#
# a.timesincelastspike[p][i] += a.dt
#
# # 1396-1449
# # Update the total conductances (changes provoked by the spikes)
# for p in range(Npop):
# for sourceneuron in a.spikes[p]:
# for tp in range(Npop):
# # AMPA
# if a.AMPA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.AMPA_eff[p][tp][sourceneuron,tn]
# a.LS_AMPA[tp][tn] += pathway_strength
#
# # GABA
# if a.GABA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.GABA_eff[p][tp][sourceneuron,tn]
# a.LS_GABA[tp][tn] += pathway_strength
#
# # NMDA
# if a.NMDA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.NMDA_eff[p][tp][sourceneuron,tn]
# ALPHA = 0.6332
# a.LastConductanceNMDA[p][tp][sourceneuron,tn] *= np.exp(-a.timesincelastspike[p][sourceneuron] / a.Tau_NMDA[tp][tn])
# a.LS_NMDA[tp][tn] += pathway_strength * ALPHA * (1. - a.LastConductanceNMDA[p][tp][sourceneuron,tn])
# a.LastConductanceNMDA[p][tp][sourceneuron,tn] += ALPHA * (1. - a.LastConductanceNMDA[p][tp][sourceneuron,tn])
#
# # dopaminergic learning
# #if a.dpmn_cortex[p][sourceneuron] > 0 and a.dpmn_type[tp][tn] > 0:
# # a.dpmn_XPRE[tp][tn] = 1
for popid in range(len(popdata)):
a.cond[popid] = (a.V[popid] < a.V_h[popid]).astype(int)
# true (cond = 1)
a.h[popid] = a.h[popid] + a.cond[popid] * a.dt * (1 - a.h[popid]) / a.tauhp[popid]
# false (cond = 0)
a.h[popid] = a.h[popid] + (1 - a.cond[popid]) * a.dt * (-a.h[popid]) / a.tauhm[popid]
# mix
a.g_rb[popid] = a.g_T[popid] * a.h[popid] * (1 - a.cond[popid])
for popid in range(len(popdata)):
# 0 = 1st continue, 1 = proceed
a.cond[popid] = (a.V[popid] <= a.Threshold[popid]).astype(int)
a.V[popid] -= (a.V[popid] - a.ResetPot[popid]) * (1 - a.cond[popid])
# 0 = 1st or 2nd continue, 1 = proceed
a.cond[popid] = a.cond[popid] * (a.RefrState[popid] == 0).astype(int)
a.RefrState[popid] -= np.sign(a.RefrState[popid]) * (1 - a.cond[popid])
a.g_adr[popid] = a.g_adr_max[popid] / (1 + np.exp((a.V[popid]-a.Vadr_h[popid]) / a.Vadr_s[popid]))
a.dv[popid] = a.V[popid] + 55
a.tau_n[popid] = a.tau_k_max[popid] / (np.exp(-1 * a.dv[popid] / 30) + np.exp(a.dv[popid] / 30))
a.n_inif[popid] = 1 / (1 + np.exp(-(a.V[popid] - a.Vk_h[popid]) / a.Vk_s[popid]))
a.n_k[popid] = a.n_k[popid] + a.cond[popid] * -a.dt / a.tau_n[popid] * (a.n_k[popid] - a.n_inif[popid])
a.g_k[popid] = a.g_k_max[popid] * a.n_k[popid]
a.V[popid] = a.V[popid] + a.cond[popid] * -a.dt * (1 / a.Taum[popid] * (a.V[popid] - a.RestPot[popid]) + a.Ca[popid] * a.g_ahp[popid] / a.C[popid] * 0.001 * (a.V[popid] - a.Vk[popid]) + a.g_adr[popid] / a.C[popid] * (a.V[popid] - a.ADRRevPot[popid]) + a.g_k[popid] / a.C[popid] * (a.V[popid] - a.ADRRevPot[popid]) + a.g_rb[popid] / a.C[popid] * (a.V[popid] - a.V_T[popid]))
a.Ca[popid] = a.Ca[popid] - a.cond[popid] * a.Ca[popid] * a.dt / a.Tau_ca[popid]
a.Vaux[popid] = np.minimum(a.V[popid],a.Threshold[popid])
a.V[popid] = a.V[popid] + a.cond[popid] * a.dt * (a.RevPot_NMDA[popid] - a.Vaux[popid]) * .001 * (a.LS_NMDA[popid] + a.ExtS_NMDA[popid]) / a.C[popid] / (1. + np.exp(-0.062 * a.Vaux[popid] / 3.57))
a.V[popid] = a.V[popid] + a.cond[popid] * a.dt * (a.RevPot_AMPA[popid] - a.Vaux[popid]) * .001 * (a.LS_AMPA[popid] + a.ExtS_AMPA[popid]) / a.C[popid]
a.V[popid] = a.V[popid] + a.cond[popid] * a.dt * (a.RevPot_GABA[popid] - a.Vaux[popid]) * .001 * (a.LS_GABA[popid] + a.ExtS_GABA[popid]) / a.C[popid]
for popid in range(len(popdata)):
newspikes[popid] = list(np.nonzero(a.V[popid] > a.Threshold[popid])[0])
for neuron in newspikes[popid]:
a.V[popid][neuron] = 0
a.Ca[popid][neuron] += a.alpha_ca[popid][neuron]
a.RefrState[popid][neuron] = 10
a.Ptimesincelastspike[popid][neuron] = a.timesincelastspike[popid][neuron]
a.timesincelastspike[popid][neuron] = 0
a.dpmn_XPOST[popid][neuron] = 1
a.spikes = newspikes
# for p in range(Npop):
# for i in range(len(a.N[p])):
# g_rb = 0
# if a.V[p][i] < a.V_h[p][i]:
# a.h[p][i] += (1.0 - a.h[p][i]) * a.dt / a.tauhp[p][i]
# else:
# a.h[p][i] += -a.h[p][i] * a.dt / a.tauhm[p][i]
# g_rb = a.g_T[p][i] * a.h[p][i]
#
# if a.V[p][i] > a.Threshold[p][i]:
# a.V[p][i] = a.ResetPot[p][i]
# a.RefrState[p][i] -= 1
# continue
#
# if a.RefrState[p][i] > 0:
# a.RefrState[p][i] -= 1
# continue
#
# g_adr = 0
# if a.g_adr_max[p][i] != 0:
# g_adr = a.g_adr_max[p][i] / (1 + np.exp((a.V[p][i] - a.Vadr_h[p][i]) / a.Vadr_s[p][i]))
#
# g_k = 0
# if a.g_k_max[p][i] != 0:
# tau_max = a.tau_k_max[p][i]
# dv = a.V[p][i] + 55.0
# tau_n = tau_max / (np.exp(-1 * dv / 30) + np.exp(dv / 30))
# n_inif = 1 / (1 + np.exp(-(a.V[p][i] - a.Vk_h[p][i]) / a.Vk_s[p][i]))
# a.n_k[p][i] += -a.dt / tau_n * (a.n_k[p][i] - n_inif)
# g_k = a.g_k_max[p][i] * a.n_k[p][i]
#
# if a.g_ahp[p][i] == 0:
# a.V[p][i] += -a.dt * (1 / a.Taum[p][i] * (a.V[p][i] - a.RestPot[p][i]) + g_adr / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_k / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_rb / a.C[p][i] * (a.V[p][i] - a.V_T[p][i]))
# else:
# a.V[p][i] += -a.dt * (1 / a.Taum[p][i] * (a.V[p][i] - a.RestPot[p][i]) + a.Ca[p][i] * a.g_ahp[p][i] / a.C[p][i] * 0.001 * (a.V[p][i] - a.Vk[p][i]) + g_adr / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_k / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_rb / a.C[p][i] * (a.V[p][i] - a.V_T[p][i]))
#
# a.Ca[p][i] += -a.Ca[p][i] * a.dt / a.Tau_ca[p][i]
#
# Vaux = a.V[p][i]
# if Vaux > a.Threshold[p][i]:
# Vaux = a.Threshold[p][i]
#
# # AMPA
# a.V[p][i] += a.dt * (a.RevPot_AMPA[p][i] - Vaux) * .001 * (a.LS_AMPA[p][i] + a.ExtS_AMPA[p][i]) / a.C[p][i]
# # GABA
# a.V[p][i] += a.dt * (a.RevPot_GABA[p][i] - Vaux) * .001 * (a.LS_GABA[p][i] + a.ExtS_GABA[p][i]) / a.C[p][i]
# # NMDA
# a.V[p][i] += a.dt * (a.RevPot_NMDA[p][i] - Vaux) * .001 * (a.LS_NMDA[p][i] + a.ExtS_NMDA[p][i]) / a.C[p][i] / (1. + np.exp(-0.062 * Vaux / 3.57))
#
# if a.V[p][i] > a.Threshold[p][i]:
# #print("a spike occured!")
# newspikes[p].append(i)
#
# a.V[p][i] = a.ResetPot[p][i]
# a.timesincelastspike[p][i] = 0
#
# a.V[p][i] = 0
# a.RefrState[p][i] = 10 # 2/dt
#
# a.Ca[p][i] += a.alpha_ca[p][i]
#
# a.spikes = newspikes
for popid in range(len(popdata)):
if a.dpmn_type[popid][0] > 0:
a.dpmn_DAp[popid] -= a.dt * a.dpmn_DAp[popid] * a.dpmn_tauDOP[popid]
a.dpmn_APRE[popid] += a.dt * (a.dpmn_dPRE[popid] * a.dpmn_XPRE[popid] - a.dpmn_APRE[popid]) / a.dpmn_tauPRE[popid]
a.dpmn_APOST[popid] += a.dt * (a.dpmn_dPOST[popid] * a.dpmn_XPOST[popid] - a.dpmn_APOST[popid]) / a.dpmn_tauPOST[popid]
a.dpmn_E[popid] += a.dt * (a.dpmn_XPOST[popid] * a.dpmn_APRE[popid] - a.dpmn_XPRE[popid] * a.dpmn_APOST[popid] - a.dpmn_E[popid]) / a.dpmn_tauE[popid]
DA = a.dpmn_m[popid] * (a.dpmn_DAp[popid] + a.dpmn_DAt[popid])
# ignore motivational decay for now? 1645-1647 are excluded
fDA = DA
if a.dpmn_type[popid][0] > 1.5:
fDA = DA / (2.5 + abs(DA))
for src_popid in range(len(popdata)):
if a.dpmn_cortex[src_popid][0] > 0:
if a.AMPA_con[src_popid][popid] is not None:
a.AMPA_eff[src_popid][popid] += a.dt * a.AMPA_con[src_popid][popid] * (a.dpmn_wmax[popid] - a.AMPA_eff[src_popid][popid]) * a.dpmn_alphaw[popid] * fDA * a.dpmn_E[popid]
for popid in range(len(popdata)):
a.rollingbuffer[popid][a.bufferpointer] = len(a.spikes[popid])
a.bufferpointer += 1
if a.bufferpointer >= a.bufferlength:
a.bufferpointer = 0
#####################################################
#
# def timestep_mutator_2(a,popdata):
# Npop = len(popdata)
#
# newspikes = []
# for i in range(Npop):
# newspikes.append([])
#
# # 1337-1345
# for p in range(Npop):
# if a.dpmn_type[p][0] > 0:
# pass
# #a.dpmn_XPRE[p] *= 0
# #a.dpmn_XPOST[p] *= 0
#
# # 1347-1392
# # Compute the decay of the total conductances and add external input
# for p in range(Npop):
# selected = np.random.randint(len(a.N[p]))
# for i in range(len(a.N[p])):
# # AMPA
# freq = a.FreqExt_AMPA[p][i]
# efficacy = a.MeanExtEff_AMPA[p][i]
# a.ExtMuS_AMPA[p][i] = freq * .001 * efficacy * a.MeanExtCon_AMPA[p][i] * a.Tau_AMPA[p][i]
# a.ExtSigmaS_AMPA[p][i] = np.sqrt(a.Tau_AMPA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_AMPA[p][i])
# s = a.ExtSigmaS_AMPA[p][i]
# if s > 0:
# a.ExtS_AMPA[p][i] += a.dt / a.Tau_AMPA[p][i] * (-a.ExtS_AMPA[p][i] + a.ExtMuS_AMPA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_AMPA[p][i]) * np.random.normal()
# else:
# a.ExtS_AMPA[p][i] += a.dt / a.Tau_AMPA[p][i] * (-a.ExtS_AMPA[p][i] + a.ExtMuS_AMPA[p][i])
# a.LS_AMPA[p][i] *= np.exp(-a.dt / a.Tau_AMPA[p][i]) # decay
#
# # GABA
# freq = a.FreqExt_GABA[p][i]
# efficacy = a.MeanExtEff_GABA[p][i]
# a.ExtMuS_GABA[p][i] = freq * .001 * efficacy * a.MeanExtCon_GABA[p][i] * a.Tau_GABA[p][i]
# a.ExtSigmaS_GABA[p][i] = np.sqrt(a.Tau_GABA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_GABA[p][i])
# s = a.ExtSigmaS_GABA[p][i]
# if s > 0:
# a.ExtS_GABA[p][i] += a.dt / a.Tau_GABA[p][i] * (-a.ExtS_GABA[p][i] + a.ExtMuS_GABA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_GABA[p][i]) * np.random.normal()
# else:
# a.ExtS_GABA[p][i] += a.dt / a.Tau_GABA[p][i] * (-a.ExtS_GABA[p][i] + a.ExtMuS_GABA[p][i])
# a.LS_GABA[p][i] *= np.exp(-a.dt / a.Tau_GABA[p][i]) # decay
#
# # NMDA
# freq = a.FreqExt_NMDA[p][i]
# efficacy = a.MeanExtEff_NMDA[p][i]
# a.ExtMuS_NMDA[p][i] = freq * .001 * efficacy * a.MeanExtCon_NMDA[p][i] * a.Tau_NMDA[p][i]
# a.ExtSigmaS_NMDA[p][i] = np.sqrt(a.Tau_NMDA[p][i] * .5 * freq * .001 * efficacy * efficacy * a.MeanExtCon_NMDA[p][i])
# s = a.ExtSigmaS_NMDA[p][i]
# if s > 0:
# a.ExtS_NMDA[p][i] += a.dt / a.Tau_NMDA[p][i] * (-a.ExtS_NMDA[p][i] + a.ExtMuS_NMDA[p][i]) + s * np.sqrt(a.dt * 2. / a.Tau_NMDA[p][i]) * np.random.normal()
# else:
# a.ExtS_NMDA[p][i] += a.dt / a.Tau_NMDA[p][i] * (-a.ExtS_NMDA[p][i] + a.ExtMuS_NMDA[p][i])
# a.LS_NMDA[p][i] *= np.exp(-a.dt / a.Tau_NMDA[p][i]) # decay
#
# a.timesincelastspike[p][i] += a.dt
#
# # 1396-1449
# # Update the total conductances (changes provoked by the spikes)
# for p in range(Npop):
# for sourceneuron in a.spikes[p]:
# for tp in range(Npop):
# # AMPA
# if a.AMPA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.AMPA_eff[p][tp][sourceneuron,tn]
# a.LS_AMPA[tp][tn] += pathway_strength
#
# # GABA
# if a.GABA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.GABA_eff[p][tp][sourceneuron,tn]
# a.LS_GABA[tp][tn] += pathway_strength
#
# # NMDA
# if a.NMDA_con[p][tp] is not None:
# for tn in range(len(a.N[tp])):
# pathway_strength = a.NMDA_eff[p][tp][sourceneuron,tn]
# ALPHA = 0.6332
# a.LastConductanceNMDA[p][tp][sourceneuron,tn] *= np.exp(-a.timesincelastspike[p][sourceneuron] / a.Tau_NMDA[tp][tn])
# a.LS_NMDA[tp][tn] += pathway_strength * ALPHA * (1. - a.LastConductanceNMDA[p][tp][sourceneuron,tn])
# a.LastConductanceNMDA[p][tp][sourceneuron,tn] += ALPHA * (1. - a.LastConductanceNMDA[p][tp][sourceneuron,tn])
#
# # dopaminergic learning
# #if a.dpmn_cortex[p][sourceneuron] > 0 and a.dpmn_type[tp][tn] > 0:
# # a.dpmn_XPRE[tp][tn] = 1
#
# for p in range(Npop):
# for i in range(len(a.N[p])):
# g_rb = 0
# if a.V[p][i] < a.V_h[p][i]:
# a.h[p][i] += (1.0 - a.h[p][i]) * a.dt / a.tauhp[p][i]
# else:
# a.h[p][i] += -a.h[p][i] * a.dt / a.tauhm[p][i]
# g_rb = a.g_T[p][i] * a.h[p][i]
#
# if a.V[p][i] > a.Threshold[p][i]:
# a.V[p][i] = a.ResetPot[p][i]
# a.RefrState[p][i] -= 1
# continue
#
# if a.RefrState[p][i] > 0:
# a.RefrState[p][i] -= 1
# continue
#
# g_adr = 0
# if a.g_adr_max[p][i] != 0:
# g_adr = a.g_adr_max[p][i] / (1 + np.exp((a.V[p][i] - a.Vadr_h[p][i]) / a.Vadr_s[p][i]))
#
# g_k = 0
# if a.g_k_max[p][i] != 0:
# tau_max = a.tau_k_max[p][i]
# dv = a.V[p][i] + 55.0
# tau_n = tau_max / (np.exp(-1 * dv / 30) + np.exp(dv / 30))
# n_inif = 1 / (1 + np.exp(-(a.V[p][i] - a.Vk_h[p][i]) / a.Vk_s[p][i]))
# a.n_k[p][i] += -a.dt / tau_n * (a.n_k[p][i] - n_inif)
# g_k = a.g_k_max[p][i] * a.n_k[p][i]
#
# if a.g_ahp[p][i] == 0:
# a.V[p][i] += -a.dt * (1 / a.Taum[p][i] * (a.V[p][i] - a.RestPot[p][i]) + g_adr / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_k / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_rb / a.C[p][i] * (a.V[p][i] - a.V_T[p][i]))
# else:
# a.V[p][i] += -a.dt * (1 / a.Taum[p][i] * (a.V[p][i] - a.RestPot[p][i]) + a.Ca[p][i] * a.g_ahp[p][i] / a.C[p][i] * 0.001 * (a.V[p][i] - a.Vk[p][i]) + g_adr / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_k / a.C[p][i] * (a.V[p][i] - a.ADRRevPot[p][i]) + g_rb / a.C[p][i] * (a.V[p][i] - a.V_T[p][i]))
#
# a.Ca[p][i] += -a.Ca[p][i] * a.dt / a.Tau_ca[p][i]
#
# Vaux = a.V[p][i]
# if Vaux > a.Threshold[p][i]:
# Vaux = a.Threshold[p][i]
#
# # AMPA
# a.V[p][i] += a.dt * (a.RevPot_AMPA[p][i] - Vaux) * .001 * (a.LS_AMPA[p][i] + a.ExtS_AMPA[p][i]) / a.C[p][i]
# # GABA
# a.V[p][i] += a.dt * (a.RevPot_GABA[p][i] - Vaux) * .001 * (a.LS_GABA[p][i] + a.ExtS_GABA[p][i]) / a.C[p][i]
# # NMDA
# a.V[p][i] += a.dt * (a.RevPot_NMDA[p][i] - Vaux) * .001 * (a.LS_NMDA[p][i] + a.ExtS_NMDA[p][i]) / a.C[p][i] / (1. + np.exp(-0.062 * Vaux / 3.57))
#
# if a.V[p][i] > a.Threshold[p][i]:
# #print("a spike occured!")
# newspikes[p].append(i)
#
# a.V[p][i] = a.ResetPot[p][i]
# a.timesincelastspike[p][i] = 0
#
# a.V[p][i] = 0
# a.RefrState[p][i] = 10 # 2/dt
#
# a.Ca[p][i] += a.alpha_ca[p][i]
#
# a.spikes = newspikes
#
# for p in range(Npop):
# a.rollingbuffer[p][a.bufferpointer] = len(a.spikes[p])
# a.bufferpointer += 1
# if a.bufferpointer >= a.bufferlength:
# a.bufferpointer = 0