def detTotalWeightChange(inputArr, spikeTrain, iterations, showPlot):
# Initialize the time steps required to emulate the total runtime iteratively
dt = 1 # Time between iterations
train = spikeTrain
totalDuration = train.shape[1] # Total duration to run
timeToEnumerate = np.arange(
1, totalDuration + 1,
1) # Determine the time-step array [0, 0.125, 0.25, 0.375, ..., 800]
# Assign initial parameters
# ---------------------------------
tauX = 1024
tauY = 32
tauPlus = 64
tauMinus = 256
# ---------------------------------
A2Plus = inputArr[0]
A2Minus = inputArr[1]
A3Plus = inputArr[2]
A3Minus = inputArr[3]
# ---------------------------------
r1 = genDE(tauPlus, True, dt)
r2 = genDE(tauX, True, dt)
o1 = genDE(tauMinus, False, dt)
o2 = genDE(tauY, False, dt)
# ---------------------------------
syn = Synapse(0, A2Plus, A2Minus, A3Plus, A3Minus)
synWeight = np.zeros((1, math.floor(totalDuration / dt)))
# Enumerate through the total runtime
for timeIndex, currentTime in enumerate(timeToEnumerate):
# Update the current state of t-pre and t-post
tpre = (train[0, timeIndex] == 1)
tpost = (train[1, timeIndex] == 1)
# Update the current values of r1, r2, o1 and o2
r1.updateSynapticWeight(tpre, tpost)
r2.updateSynapticWeight(tpre, tpost)
o1.updateSynapticWeight(tpre, tpost)
o2.updateSynapticWeight(tpre, tpost)
# Update the current synaptic weight
syn.updateSynapticWeight(tpre, tpost, r1.currentValue,
r2.previousValue, o1.currentValue,
o2.previousValue)
currentVal = syn.currentWeight
synWeight[0, timeIndex] = currentVal
totalWeightChange = synWeight[0, -1] - synWeight[0, 0]
if (showPlot):
print('Total Weight Change: [%f]' % (totalWeightChange))
print('Total Weight Change [n = 60]: [%f]' % (totalWeightChange * 60))
plt.plot(timeToEnumerate,
synWeight[0, :],
label='Change in Synaptic Weight')
plt.legend(loc='lower center')
plt.title('Change in Synaptic Weight')
plt.xlim([0, totalDuration])
plt.show()
return totalWeightChange * iterations
def runSimulation(inputArr):
# Initialize the time steps required to emulate the total runtime iteratively
totalDuration = 1000 # Total duration to run
dt = 1 # Time between iterations
timeToEnumerate = np.arange(1, totalDuration + 1, 1) # Determine the time-step array [0, 0.125, 0.25, 0.375, ..., 800]
# Generate a random pre-synaptic and post-synaptic spike train to send to the network
trainGen = GenSpikeTrain(math.floor(totalDuration/dt))
preTrain = trainGen.SpikeTrainGen()
postTrain = trainGen.SpikeTrainGen()
train = np.vstack((preTrain, postTrain))
# Assign initial parameters
# ---------------------------------
tauX = 575
tauY = 47
tauPlus = 16.8
tauMinus = 33.7
# ---------------------------------
A2Plus = inputArr[0]
A2Minus = inputArr[1]
A3Plus = inputArr[2]
A3Minus = inputArr[3]
# ---------------------------------
r1 = genDE(tauPlus, True, dt)
r2 = genDE(tauX, True, dt)
o1 = genDE(tauMinus, False, dt)
o2 = genDE(tauY, False, dt)
# ---------------------------------
syn = Synapse(0, A2Plus, A2Minus, A3Plus, A3Minus)
# ---------------------------------
synWeight = np.zeros(math.floor(totalDuration/dt))
# Enumerate through the total runtime
for timeIndex, currentTime in enumerate(timeToEnumerate):
# Update the current state of t-pre and t-post
tpre = (train[0, timeIndex] == 1)
tpost = (train[1, timeIndex] == 1)
# Update the current values of r1, r2, o1 and o2
r1.updateSynapticWeight(tpre, tpost)
r2.updateSynapticWeight(tpre, tpost)
o1.updateSynapticWeight(tpre, tpost)
o2.updateSynapticWeight(tpre, tpost)
# Update the current synaptic weight
syn.updateSynapticWeight(tpre, tpost, r1.currentValue, r2.currentValue, o1.currentValue, o2.currentValue)
currentVal = syn.currentWeight
synWeight[timeIndex] = currentVal
totalWeightChange = synWeight[-1] - synWeight[0]
print('Total Weight Change: [%f]' % (totalWeightChange))
plt.plot(timeToEnumerate, synWeight)
plt.title('Change in Synaptic Weight for Random Spike Train Inputs')
plt.xlim([0,500])
plt.show()