def setupLayerAN(params, settings, neuronModel, cell_params, popClassActivation, popPoissionNoiseSource, populationsPN, populationsAN,learning,projectionsPNAN):
#create an Association Neuron AN cluster population per class
#this will be fed by:
#1) PN clusters via plastic synapses
#2) Class activation to innervate the correct AN cluster for a given input
#3) laterally inhibit between AN clusters
numClasses = params['NUM_CLASSES']
anClusterSize = int(params['CLUSTER_SIZE']) #* params['NETWORK_SCALE']
for an in range(numClasses):
popName = 'popClusterAN_' + str(an) ;
popClusterAN = spynnaker.Population(anClusterSize, neuronModel, cell_params, label=popName)
populationsAN.append(popClusterAN)
#connect neurons in every PN popn to x% (e.g 50%) neurons in this AN cluster
for pn in range(len(populationsPN)):
if learning:
projLabel = 'Proj_PN' + str(pn) + '_AN' + str(an)
projClusterPNToClusterAN = connectClusterPNtoAN(params,populationsPN[pn],popClusterAN,float(settings['OBSERVATION_EXPOSURE_TIME_MS']),projLabel)
projectionsPNAN.append(projClusterPNToClusterAN) #keep handle to use later for saving off weights at end of learning
else:
#Without plasticity, create PNAN FromList connectors using weights saved during learning stage
connections = utils.loadListFromFile(getWeightsFilename(settings,'PNAN',pn,an))
#print 'Loaded weightsList[',pn,',',an,']',connections
tupleList = utils.createListOfTuples(connections) #new version only accepts list of tuples not list of lists
#print 'tupleList[',pn,',',an,']',tupleList
conn = spynnaker.FromListConnector(tupleList)
projClusterPNToClusterAN = spynnaker.Projection(populationsPN[pn], popClusterAN,conn, target='excitatory')
if learning:
#use the class activity input neurons to create correlated activity during learining in the corresponding class cluster
weight = params['WEIGHT_CLASS_EXCITATION_TO_CLUSTER_AN']
connections = utils.fromList_SpecificNeuronToAll(an,anClusterSize,weight,params['MIN_DELAY_CLASS_ACTIVITY_TO_CLUSTER_AN'],params['MAX_DELAY_CLASS_ACTIVITY_TO_CLUSTER_AN'])
projClassActivityToClusterAN = spynnaker.Projection(popClassActivation, popClusterAN, spynnaker.FromListConnector(connections), target='excitatory')
else: #testing
#send spikes on these outputs back to correct host port , these will be used to determine winner etc
anHostReceivePort = int(settings['AN_HOST_RECEIVE_PORT'])
ExternalDevices.activate_live_output_for(popClusterAN,port=anHostReceivePort)
#connect each AN cluster to inhibit every other AN cluster
utils.createInterPopulationWTA(populationsAN,params['WEIGHT_WTA_AN_AN'],params['DELAY_WTA_AN_AN'],float(params['CONNECTIVITY_WTA_AN_AN']))
#inhibit other non-corresponding class clusters
if learning:
weight = params['WEIGHT_CLASS_INHIBITION_TO_CLUSTER_AN']
for activeCls in range(numClasses):
connections = utils.fromList_SpecificNeuronToAll(activeCls,anClusterSize,weight,params['MIN_DELAY_CLASS_ACTIVITY_TO_CLUSTER_AN'],params['MAX_DELAY_CLASS_ACTIVITY_TO_CLUSTER_AN'])
for an in range(numClasses):
if an != activeCls:
projClassActivityToClusterAN = spynnaker.Projection(popClassActivation, populationsAN[an], spynnaker.FromListConnector(connections), target='inhibitory')
