def setupLayerRN(params, neuronModel, cell_params, injectionPopulations, popPoissionNoiseSource, populationsRN):
#create a single RN population divided into virtual clusters one per VR
#this will be fed by the noise population and modulated by the relevant ratecoded neuron
#to create a rate coded population
numVR = params['NUM_VR']
rnClusterSize = int(params['CLUSTER_SIZE']) #* params['NETWORK_SCALE']
rnPopSize = rnClusterSize * numVR
popName = 'popRN'
popRN = spynnaker.Population(rnPopSize, neuronModel, cell_params, label=popName)
populationsRN.append(popRN)
#connect one random poisson neuron to each RN neuron
weight = params['WEIGHT_POISSON_TO_CLUSTER_RN']
delay = params['DELAY_POISSON_TO_CLUSTER_RN']
connections = utils.fromList_OneRandomSrcForEachTarget(popPoissionNoiseSource._size,popRN._size,weight,delay)
projPoissonToClusterRN = spynnaker.Projection(popPoissionNoiseSource, popRN, spynnaker.FromListConnector(connections), target='excitatory')
vr = 0
for injectionPopn in injectionPopulations:
connections = list()
for fromNeuronIdx in range(injectionPopn._size):
#connect the correct VR ratecode neuron in popRateCodeSpikes to corresponding subsection (cluster) of the RN population
weight = params['WEIGHT_RATECODE_TO_CLUSTER_RN']
firstIndex = vr * rnClusterSize
lastIndex = firstIndex + rnClusterSize - 1
connections += utils.fromList_SpecificNeuronToRange(fromNeuronIdx,firstIndex,lastIndex,weight,params['MIN_DELAY_RATECODE_TO_CLUSTER_RN'],params['MAX_DELAY_RATECODE_TO_CLUSTER_RN'])
vr = vr + 1
#after the last neuron in the current injection pop, create a projection to the RN
projRateToClusterRN = spynnaker.Projection(injectionPopn, popRN, spynnaker.FromListConnector(connections), target='excitatory')
print 'Added projection to RN of ', len(connections), " connections from injection pop ", injectionPopn.label, "(size ", injectionPopn._size,")"
def setupLayerRN(params, neuronModel, cell_params, popRateCodeSpikes, popPoissionNoiseSource, populationsRN):
# create a single RN population divided into virtual clusters one per VR
# this will be fed by the noise population and modulated by the relevant
# ratecoded neuron
# to create a rate coded population
numVR = params["NUM_VR"]
rnClusterSize = params["CLUSTER_SIZE"] * params["NETWORK_SCALE"]
rnPopSize = rnClusterSize * numVR
popName = "popRN"
popRN = spynnaker.Population(rnPopSize, neuronModel, cell_params, label=popName)
populationsRN.append(popRN)
# connect one random poisson neuron to each RN neuron
weight = params["WEIGHT_POISSON_TO_CLUSTER_RN"]
delay = params["DELAY_POISSON_TO_CLUSTER_RN"]
connections = utils.fromList_OneRandomSrcForEachTarget(popPoissionNoiseSource._size, popRN._size, weight, delay)
projPoissonToClusterRN = spynnaker.Projection(
popPoissionNoiseSource, popRN, spynnaker.FromListConnector(connections), target="excitatory"
)
connections = list()
for vr in range(numVR):
# connect the correct VR ratecode neuron in popRateCodeSpikes to
# corresponding subsection (cluster) of the RN population
weight = params["WEIGHT_RATECODE_TO_CLUSTER_RN"]
firstIndex = vr * rnClusterSize
lastIndex = firstIndex + rnClusterSize - 1
connections += utils.fromList_SpecificNeuronToRange(
vr,
firstIndex,
lastIndex,
weight,
params["MIN_DELAY_RATECODE_TO_CLUSTER_RN"],
params["MAX_DELAY_RATECODE_TO_CLUSTER_RN"],
)
projRateToClusterRN = spynnaker.Projection(
popRateCodeSpikes, popRN, spynnaker.FromListConnector(connections), target="excitatory"
)
