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')