def setupLayerInput(params, settings, populationsInput):
numVR = params["NUM_VR"]
numRatecodeNeurons = numVR
spikeSourceVrResponsePath = settings["SPIKE_SOURCE_VR_RESPONSE_TRAIN"]
spikeSourceVrResponsePathTest = settings["SPIKE_SOURCE_VR_RESPONSE_TEST"]
spikeSourceActiveClassPath = settings["SPIKE_SOURCE_CLASS_ACTIVATIONS"]
learning = settings["LEARNING"]
if learning:
# Create a population, one neuron per VR,
# where each neuron wil be loaded with the rate code spikes for the
# VR response over the training set
spikeDataVR = utils.readSpikeSourceDataFile(spikeSourceVrResponsePath)
popRateCodeSpikes = spynnaker.Population(
numRatecodeNeurons, spynnaker.SpikeSourceArray, spikeDataVR, label="popRateCodeSpikes"
)
populationsInput.append(popRateCodeSpikes)
# Create a population, one neuron per class,
# During training the neuron representing the current class will be
# active with significant spikes, the others will be quiet.
#
# The purpose is to innervate the relevant output class cluster/population
# so that fire-together-wire-together hebbian learning (via STDP)
# stregthens synapses from active PN clusters
#
# During testing all these neurons will be silent, leaving
# the strengthened synapses to trigger activity direct from PN layer
# in the correct output cluster.
spikeDataClass = utils.readSpikeSourceDataFile(spikeSourceActiveClassPath)
numNeurons = params["NUM_CLASSES"]
popClassActivationSpikes = spynnaker.Population(
numNeurons, spynnaker.SpikeSourceArray, spikeDataClass, label="popClassActivationSpikes"
)
populationsInput.append(popClassActivationSpikes)
else:
# Create a population, one neuron per VR,
# where each neuron wil be loaded with the rate code spikes for
# the VR response over the test set
spikeDataVRTest = utils.readSpikeSourceDataFile(spikeSourceVrResponsePathTest)
popRateCodeSpikesTest = spynnaker.Population(
numRatecodeNeurons, spynnaker.SpikeSourceArray, spikeDataVRTest, label="popRateCodeSpikes"
)
populationsInput.append(popRateCodeSpikesTest)
# create an orphan dummy popn of 1 neuron to take the place of the now
# unused spike source pop used in learning
# This is to ensure that the freed up core does not get co-opted by the
# PN layer config routine
# as this would makae the learning and testing configurations different
# in PN which would likely make the saved PNAN weight arrays incorrect
popClassActivationSpikes = spynnaker.Population(
1, neuronModel, cell_params, label="dummy_popClassActivationSpikes"
)
populationsInput.append(popClassActivationSpikes)
def setupLayerInput(params, spikeSourceVrResponsePath, spikeSourceActiveClassPath, populationsInput,learning):
#Create a population, one neuron per VR,
#where each neuron wil be loaded with the rate code spikes for the VR response over the training and/or test set
spikeData = utils.readSpikeSourceDataFile(spikeSourceVrResponsePath)
numVR = params['NUM_VR']
numRatecodeNeurons = numVR
popRateCodeSpikes = spynnaker.Population(numRatecodeNeurons, spynnaker.SpikeSourceArray, spikeData, label='popRateCodeSpikes')
populationsInput.append(popRateCodeSpikes)
if learning:
#Create a population, one neuron per class,
#During training the neuron representing the current class will be active with significant spikes, the others will be quiet
#The purpose is to innervate the relevant ouptut class cluster/population so that fire-together-wire-together hebbian learning (via STDP) stregthens synapses from active PN clusters
#During testing all these neurons will be silent, leaving the strengthened synapses to trigger activity direct from PN layer in the correct ouptpu cluster
spikeData = utils.readSpikeSourceDataFile(spikeSourceActiveClassPath)
numNeurons = params['NUM_CLASSES']
popClassActivationSpikes = spynnaker.Population(numNeurons, spynnaker.SpikeSourceArray, spikeData, label='popClassActivationSpikes')
populationsInput.append(popClassActivationSpikes)
else:
#create an orphan dummy popn of 1 neuron to take the place of the now unused spike source pop used in learning
#This is to ensure that the freed up core does not get co-opted by the PN layer config routine
# as this would makae the learning and testing configurations different in PN which would likely make the saved PNAN weight arrays incorrect
popClassActivationSpikes = spynnaker.Population(1, neuronModel, cell_params, label='dummy_popClassActivationSpikes')
populationsInput.append(popClassActivationSpikes)
def plot_spike_sources(filePath, fileName, nrInputNeurons, nrVR,
observationTime, totalSimulationTime,
classLabels, odourNames):
'''Plot the Poisson spike source matrix
Input:
-path of the spike times file
-name of the spike times file
-number of input neurons (= number of sensors)
-number of virtual receptors
-length of the Poisson spike train for each sample
-maximum simulation time for each recording
(number of samples for each recording)
x (length of Poisson spike train for each sample)
-class labels
-names of the odours used
'''
bckndNames =[[]]*len(odourNames)
spikeTimes = utils.readSpikeSourceDataFile(os.path.join(filePath,
fileName))['spike_times']
plt.figure(figsize=(20,20))
for idx, line in enumerate(spikeTimes):
for x in line:
plt.plot(x, idx, 'ko', markersize = 2)
for j in range(idx, nrVR):
plt.plot(0, j, 'k,')
for j, classLabel in enumerate(classLabels):
plt.axvspan(j*observationTime, j*observationTime+observationTime,
facecolor=colors[int(classLabel)], alpha=0.3)
for idxO, odour in enumerate(odourNames):
bckndNames[idxO] = mpatches.Patch(color=colors[idxO], label=odour)
plt.legend(handles=bckndNames, loc ='best', prop={'size':20})
plt.xlabel('Simulation time[ms]', fontsize=20)
plt.ylabel('%i Virtual receptors per sensor'%(nrVR/nrInputNeurons),
fontsize=20)
plt.tick_params(labelsize=20)
plt.title('VR spike times for classes %s'%str(classLabels), fontsize=20)
plt.savefig(fileName+'.pdf')
plt.close()
def setupLayerInput(params, settings, populationsInput):
numVR = params['NUM_VR']
numRatecodeNeurons = numVR
spikeSourceVrResponsePath = settings['SPIKE_SOURCE_VR_RESPONSE_TRAIN']
spikeSourceVrResponsePathTest = settings['SPIKE_SOURCE_VR_RESPONSE_TEST']
spikeSourceActiveClassPath = settings['SPIKE_SOURCE_CLASS_ACTIVATIONS']
learning = settings['LEARNING']
if learning:
#Create a population, one neuron per VR,
#where each neuron wil be loaded with the rate code spikes for the
#VR response over the training set
spikeDataVR = utils.readSpikeSourceDataFile(spikeSourceVrResponsePath)
popRateCodeSpikes = spynnaker.Population(numRatecodeNeurons,
spynnaker.SpikeSourceArray,
spikeDataVR,
label='popRateCodeSpikes')
populationsInput.append(popRateCodeSpikes)
#Create a population, one neuron per class,
#During training the neuron representing the current class will be
#active with significant spikes, the others will be quiet.
#
#The purpose is to innervate the relevant output class cluster/population
#so that fire-together-wire-together hebbian learning (via STDP)
#stregthens synapses from active PN clusters
#
#During testing all these neurons will be silent, leaving
#the strengthened synapses to trigger activity direct from PN layer
#in the correct output cluster.
spikeDataClass = utils.readSpikeSourceDataFile(
spikeSourceActiveClassPath)
numNeurons = params['NUM_CLASSES']
popClassActivationSpikes = spynnaker.Population(
numNeurons,
spynnaker.SpikeSourceArray,
spikeDataClass,
label='popClassActivationSpikes')
populationsInput.append(popClassActivationSpikes)
else:
#Create a population, one neuron per VR,
#where each neuron wil be loaded with the rate code spikes for
#the VR response over the test set
spikeDataVRTest = utils.readSpikeSourceDataFile(
spikeSourceVrResponsePathTest)
popRateCodeSpikesTest = spynnaker.Population(
numRatecodeNeurons,
spynnaker.SpikeSourceArray,
spikeDataVRTest,
label='popRateCodeSpikes')
populationsInput.append(popRateCodeSpikesTest)
#create an orphan dummy popn of 1 neuron to take the place of the now
#unused spike source pop used in learning
#This is to ensure that the freed up core does not get co-opted by the
#PN layer config routine
# as this would makae the learning and testing configurations different
#in PN which would likely make the saved PNAN weight arrays incorrect
popClassActivationSpikes = spynnaker.Population(
1,
neuronModel,
cell_params,
label='dummy_popClassActivationSpikes')
populationsInput.append(popClassActivationSpikes)
