""" Hellfire Chain """ #############################PyNN Initialisation############################################ from pyNN.utility import get_script_args from pyNN.hardware.stage1 import * #Uncomment this line for Spiky Chip import numpy #from pyNN.nest import * #Uncomment this line for Nest #This line sets up the simulation, and also resets hardware if it is used, and turns off #the scope setup(timestep=0.1,min_delay=0.1,max_delay=4.0,useScope=False,assertSilence=True, calibVthresh=False) #Length of Simulation in milliseconds (ms) Duration = 10000. # 1s ##############################INFORMATION################################################### #Excitatory weights range from 0.0003 to 0.005 # #Inhibitory weights range from 0.001 to 0.015 # #delay is ignored by hardware, but 0.1 is a good reference for next # ############################################################################################ ##############################Topography Control Variables################################## #Currently 12 layers deep, 12 wide excitatory, 4 wide inhibitory #Each inhibitory inhibits 1/4 of the chain ChainLength = 5 ExciteWidthE = 7 InhibiWidthE = 7 ExciteWidthI = 7 InhibiWidthI = 7 ##############################Configure Neurons############################################# NeuronParams = { 'v_thresh' : -55.2, #-55 to -80 'v_rest' : -60.0, #-55 to -80 'tau_syn_E' : 5.0, 'tau_syn_I' : 5.0, 'v_reset' : -65.0, #-55 to -80 'g_leak' : 20.0, 'e_rev_I' : -80.0} ##############################Instantiation################################################# #Create Cells Chain_ExciteE = create(IF_facets_hardware1, NeuronParams, n = ExciteWidthE*ChainLength ) Chain_InhibiE = create(IF_facets_hardware1, NeuronParams, n = InhibiWidthE*ChainLength ) Chain_ExciteI = create(IF_facets_hardware1, NeuronParams, n = ExciteWidthI*ChainLength ) Chain_InhibiI = create(IF_facets_hardware1, NeuronParams, n = InhibiWidthI*ChainLength ) #Noise sources ExciteSynapticInput = create(SpikeSourceArray, {'spike_times': numpy.arange(50., 53.1, 0.6)}, ExciteWidthE) InhibSynapticInput = create(SpikeSourceArray, {'spike_times': numpy.arange(0.,200., 5.3)}, ExciteWidthI) #############################Excitatory Connections######################################### #Connect the excitatory layers to the next layer and to the inhibitory layer for n in range(0,ChainLength,1): # E: e-e for p in range((n*ExciteWidthE),((n+1)*ExciteWidthE),1): for q in range((n*ExciteWidthE),((n+1)*ExciteWidthE),1): if p!=q: # avoid self-connections connect(Chain_ExciteE[p], Chain_ExciteE[q], weight = 0.003333, synapse_type='excitatory') # E: e-i for p in range((n*ExciteWidthE),((n+1)*ExciteWidthE),1): for q in range((n*InhibiWidthE),((n+1)*InhibiWidthE),1): connect(Chain_ExciteE[p], Chain_InhibiE[q], weight = 0.001, synapse_type='excitatory') # E: i-e for p in range((n*InhibiWidthE),((n+1)*InhibiWidthE),1): for q in range((n*ExciteWidthE),((n+1)*ExciteWidthE),1): connect(Chain_InhibiE[p], Chain_ExciteE[q], weight = 0.001, synapse_type='inhibitory') # I: e-e for p in range((n*ExciteWidthI),((n+1)*ExciteWidthI),1): for q in range((n*ExciteWidthI),((n+1)*ExciteWidthI),1): if p!=q: # avoid self-connections connect(Chain_ExciteI[p], Chain_ExciteI[q], weight = 0.002, synapse_type='excitatory') # I: e-i for p in range((n*ExciteWidthI),((n+1)*ExciteWidthI),1): for q in range((n*InhibiWidthI),((n+1)*InhibiWidthI),1): connect(Chain_ExciteI[p], Chain_InhibiI[q], weight = 0.005, synapse_type='excitatory') # I: i-e for p in range((n*InhibiWidthI),((n+1)*InhibiWidthI),1): for q in range((n*ExciteWidthI),((n+1)*ExciteWidthI),1): connect(Chain_InhibiI[p], Chain_ExciteI[q], weight = 0.000333, synapse_type='inhibitory') # forward excitation k = (n+1)%ChainLength if True:#k!=0: #print "connecting",n,k connect(Chain_ExciteE[(n*ExciteWidthE):((n+1)*ExciteWidthE)], Chain_ExciteE[(k*ExciteWidthE):((k+1)*ExciteWidthE)], weight = 0.001, synapse_type='excitatory') #connect(Chain_ExciteE[(n*ExciteWidthE):((n+1)*ExciteWidthE)], Chain_InhibiE[(k*InhibiWidthE):((k+1)*InhibiWidthE)], weight = 0.000666, synapse_type='excitatory') # inhibitory neighbor connect(Chain_ExciteE[(n*ExciteWidthE):((n+1)*ExciteWidthE)], Chain_ExciteI[(n*ExciteWidthI):((n+1)*ExciteWidthI)], weight = 0.001, synapse_type='excitatory') connect(Chain_ExciteE[(n*ExciteWidthE):((n+1)*ExciteWidthE)], Chain_InhibiI[(n*InhibiWidthI):((n+1)*InhibiWidthI)], weight = 0.001, synapse_type='excitatory') ############################# Blocking Inhibitory Connections ######################################### for n in range(0,ChainLength,1): for m in range(0,ChainLength,1): k = (n+1)%ChainLength x = (n-1)%ChainLength y = (n+2)%ChainLength if m==x: # backwards connect(Chain_InhibiI[n*InhibiWidthI:(n+1)*InhibiWidthI], Chain_ExciteE[m*ExciteWidthE:(m+1)*ExciteWidthE], weight = 0.015, synapse_type='inhibitory') connect(Chain_ExciteI[n*ExciteWidthI:(n+1)*ExciteWidthI], Chain_InhibiE[m*InhibiWidthE:(m+1)*InhibiWidthE], weight = 0.005, synapse_type='excitatory') if m==y: # 2 forward connect(Chain_InhibiI[n*InhibiWidthI:(n+1)*InhibiWidthI], Chain_ExciteE[m*ExciteWidthE:(m+1)*ExciteWidthE], weight = 0.015, synapse_type='inhibitory') connect(Chain_ExciteI[n*ExciteWidthI:(n+1)*ExciteWidthI], Chain_InhibiE[m*InhibiWidthE:(m+1)*InhibiWidthE], weight = 0.005, synapse_type='excitatory') k = (m-2)%ChainLength if n==k: #print "connecting",n,m connect(Chain_InhibiI[n*InhibiWidthI:(n+1)*InhibiWidthI], Chain_ExciteI[k*ExciteWidthI:(k+1)*ExciteWidthI], weight = 0.015, synapse_type='inhibitory') connect(Chain_ExciteI[n*ExciteWidthI:(n+1)*ExciteWidthI], Chain_InhibiI[k*InhibiWidthI:(k+1)*InhibiWidthI], weight = 0.005, synapse_type='excitatory') ############################# Input Synaptic Noise ########################################### #Connect the excitatory noise too the first excitatory cell layer SynToExciteCell = connect(ExciteSynapticInput, Chain_ExciteE[0:ExciteWidthE], weight = 0.005, synapse_type='excitatory') SynToInhibCell1 = connect(InhibSynapticInput, Chain_ExciteI[(ChainLength-1)*InhibiWidthI:ChainLength*InhibiWidthI], weight = 0.005, synapse_type='excitatory'); SynToInhibCell2 = connect(InhibSynapticInput, Chain_InhibiI[(ChainLength-1)*InhibiWidthI:ChainLength*InhibiWidthI], weight = 0.005, synapse_type='excitatory'); ############################# Setup Output Recording ######################################### #record_v(Chain_InhibiI[1*InhibiWidthI], "") #record_v(Chain_ExciteE[5*ExciteWidthE], "membraneE.dat") #record(Chain_ExciteE[ExciteWidthE*0:ExciteWidthE*1], "Spikes_E0.data") #record(Chain_ExciteE[ExciteWidthE*1:ExciteWidthE*2], "Spikes_E1.data") #record(Chain_ExciteE[ExciteWidthE*2:ExciteWidthE*3], "Spikes_E2.data") #record(Chain_ExciteE[ExciteWidthE*3:ExciteWidthE*4], "Spikes_E3.data") #record(Chain_ExciteE[ExciteWidthE*4:ExciteWidthE*5], "Spikes_E4.data") #record(Chain_ExciteE[ExciteWidthE*5:ExciteWidthE*6], "Spikes_E5.data") #record(Chain_InhibiI[InhibiWidthI*0:InhibiWidthI*1], "Spikes_I0.data") #record(Chain_InhibiI[InhibiWidthI*1:InhibiWidthI*2], "Spikes_I1.data") #record(Chain_InhibiI[InhibiWidthI*2:InhibiWidthI*3], "Spikes_I2.data") #record(Chain_InhibiI[InhibiWidthI*3:InhibiWidthI*4], "Spikes_I3.data") #record(Chain_InhibiI[InhibiWidthI*3:InhibiWidthI*5], "Spikes_I4.data") #record(Chain_InhibiI[InhibiWidthI*3:InhibiWidthI*6], "Spikes_I5.data") record(Chain_ExciteE, "spikesExE.dat") record(Chain_InhibiE, "spikesInE.dat") record(Chain_InhibiI, "spikesInI.dat") record(Chain_ExciteI, "spikesExI.dat") #run it for a duration run(Duration) end()