dtype = np.float32 # %% Figure 1 - Supplement Figure 3 folder = 'Fig_1_Supp_3' ### Define neuron and network parameters NeuPar = Neurons(FlagDendRec=np.int32(0)) VE_scale = 0.5 wPP, wPE, wDS = dtype(-0.5), dtype(2.5), dtype(-5.0) NetPar = Network(NeuPar, wPP=wPP, wPE=wPE, wDS=wDS) ### Define simulation parameters & recording options SimPar_test = Simulation() SimPar = Simulation(dt=0.2) SavePar = SaveData() ### Define plasticity parameter LearnPar = Learning(NeuPar) ##################### # Before plasticity # ##################### ### Define input parameters stim_max, SD = dtype(100), dtype(5) Xternal = dtype([400, 2, 2, 2, 40]) num_stim = np.int32(10)
wSV, wVS, wPP, wEP = -0.6, -0.5, -0.1, -40.0
elif Panel==3: # e
VE, VP, MP = 0, 1, 1
wSV, wVS, wPP, wEP = -0.6, -0.5, -1.5, -40.0
elif Panel==4: # f
VE, VP, MP = 0, 0, 1
wSV, wVS, wPP, wEP = -0.6, -0.5, -1.5, -40.0
wPV = -(VP + abs(wSV)*MP - (1-wPP)/(-0.07*wEP) * VE) # gain = 0.07
wPS = -(abs(wVS)*abs(wPV) + (1-wSV*wVS)*MP)
NeuPar = Neurons()
NetPar = Network(NeuPar, wPP=wPP, wPS=wPS, wPV=wPV, wEP=wEP, flag_hetero=0)
### Define input parameters
stim_max, SD = 50.0, 10.0
r0 = np.array([1,2,2,4])
StimPar = Stimulation(NeuPar, NetPar, SD, None, stim_max, r0 = r0, VE=VE, VP=VP, MP=MP, VS=VS, VV=VV)
### Define simulation parameters
SimPar = Simulation()
### Run simulation
RunStaticNetwork(NeuPar, NetPar, StimPar, SimPar, folder, fln)
SaveNetworkPara(NeuPar, NetPar, StimPar, None, folder, fln)
### Analyse & plot network
Bar_pathways(NeuPar, NetPar, VE, VP, MP, folder, fln, pPE_flag = True)
Plot_PopulationRate(NeuPar, folder, fln)