def run(lowThreshold):
runtime = 1000.0
pynn.setup()
# set STDP params for low threshold -> fails when vcthigh-vctlow < 0.04
if lowThreshold:
pynn.hardware.hwa.setSTDPParams(0.0, default.tpcsec,
default.tpcorperiod, 1.0, 1.0, 1.0,
0.98, 2.5)
else:
pynn.hardware.hwa.setSTDPParams(0.0, default.tpcsec,
default.tpcorperiod, 1.0, 1.0, 1.0,
0.85, 2.5)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
spikeArray = pynn.Population(1, pynn.SpikeSourceArray)
stdp_model = pynn.STDPMechanism(
timing_dependence=pynn.SpikePairRule(),
weight_dependence=pynn.AdditiveWeightDependence())
prj = pynn.Projection(
spikeArray,
neuron,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() * 0),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(slow=stdp_model))
pynn.run(runtime)
pynn.end()
def run(withSTDP):
runtime = 1000.0
pynn.setup()
stim = pynn.Population(1, pynn.SpikeSourcePoisson, {
'start': 0, 'duration': runtime, 'rate': 100.0})
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
if withSTDP:
stdp_model = pynn.STDPMechanism(timing_dependence=pynn.SpikePairRule(),
weight_dependence=pynn.AdditiveWeightDependence())
pynn.Projection(stim, neuron,
method=pynn.AllToAllConnector(
weights=pynn.maxExcWeight()),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(slow=stdp_model))
else:
pynn.Projection(stim, neuron,
method=pynn.AllToAllConnector(
weights=pynn.maxExcWeight()),
target='excitatory')
pynn.run(runtime)
pynn.end()
def runTest(self):
import numpy as np
column = 4
row = 4
n = 20 # number of spike pairs
deltaTList = [-1.0, 1.0] # ms
deltaTLimit = 0.3 # allowed deviation
delay = 2.9 # ms (between stimulus and post)
# at beginning in ms (should be larger than max deltaT)
experimentOffset = 100.0
deltaTPairs = 100.0 # time between pre-post-pairs in ms
noStimulators = 3
weightStimulator = 15 # weight for stimulator neurons
weightMeasure = 0 # weight for measured neuron
procCorrOffset = 100.0 # time after experiment until correlations are processed in ms
for deltaT in deltaTList:
stimulus = np.arange(experimentOffset,
(n - 0.5) * deltaTPairs + experimentOffset,
deltaTPairs)
self.assertTrue(len(stimulus) == n)
stimulusMeasure = stimulus + delay - deltaT
import pyNN.hardware.spikey as pynn
import hwconfig_default_s1v2 as default
pynn.setup()
if column > 0:
pynn.Population(column, pynn.IF_facets_hardware1)
# stimulated neuron
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
spikeSourceStim = None
spikeSourceMeasure = None
# stimulators above measured synapse
if row < noStimulators:
if row > 0:
dummy = pynn.Population(row, pynn.SpikeSourceArray)
spikeSourceMeasure = pynn.Population(
1, pynn.SpikeSourceArray, {'spike_times': stimulusMeasure})
spikeSourceStim = pynn.Population(noStimulators,
pynn.SpikeSourceArray,
{'spike_times': stimulus})
# stimulators below measured synapse
if row >= noStimulators:
if row > noStimulators:
dummy = pynn.Population(row - noStimulators,
pynn.SpikeSourceArray)
spikeSourceMeasure = pynn.Population(
1, pynn.SpikeSourceArray, {'spike_times': stimulusMeasure})
# connect and record
stdp_model = pynn.STDPMechanism(
timing_dependence=pynn.SpikePairRule(),
weight_dependence=pynn.AdditiveWeightDependence())
pynn.Projection(
spikeSourceStim,
neuron,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() *
weightStimulator),
target='excitatory')
prj = pynn.Projection(
spikeSourceMeasure,
neuron,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() *
weightMeasure),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(slow=stdp_model))
neuron.record()
#######
# RUN #
#######
# correlation flags:
# 0: no weight change
# 1: causal weight change
# 2: anti-causal weight change
pynn.hardware.hwa.setLUT(
[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
lastInputSpike = np.max(np.concatenate(
(stimulus, stimulusMeasure)))
runtime = lastInputSpike + procCorrOffset
pynn.hardware.hwa.autoSTDPFrequency = runtime
print 'runtime: ' + str(runtime) + '; last input spike: ' + str(
lastInputSpike) + '; STDP readout: ' + str(runtime)
pynn.run(runtime)
# get flag and spikes
corrFlag = (
np.array(prj.getWeightsHW(readHW=True, format='list')) /
pynn.minExcWeight())[0]
spikes = neuron.getSpikes()[:, 1]
print 'stimulus:', stimulus
print 'measure:', stimulusMeasure
print 'post:', spikes
self.assertTrue(
len(stimulusMeasure) == len(spikes), 'No proper spiking!')
print 'correlation flag: ' + str(corrFlag)
print 'deltaT (is / should / limit):', np.mean(
spikes - stimulusMeasure), '/', deltaT, '/', deltaTLimit
self.assertTrue(
abs(np.mean(spikes - stimulusMeasure) - deltaT) <= deltaTLimit,
'No precise spiking!')
if deltaT > 0: # causal
self.assertTrue(corrFlag == 1, 'Wrong correlation flag!')
else: # anti-causal
self.assertTrue(corrFlag == 2, 'Wrong correlation flag!')
pynn.end()
runtime = 1000.0
pynn.setup(mappingOffset=column)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
dummy = pynn.Population(row, pynn.SpikeSourceArray, stimParams)
stimulus = pynn.Population(1, pynn.SpikeSourceArray, stimParams)
# enable and configure STP
stp_model = pynn.TsodyksMarkramMechanism(**stpParams)
pynn.Projection(
stimulus,
neuron,
method=pynn.AllToAllConnector(weights=weight * pynn.minExcWeight()),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(
fast=stp_model) #hier auskommentieren
)
pynn.record_v(neuron[0], '')
pynn.run(runtime)
membrane = np.array(zip(pynn.timeMembraneOutput, pynn.membraneOutput))
pynn.end()
# plot
import matplotlib.pyplot as plt
plt.plot(membrane[:, 0], membrane[:, 1])
plt.xlabel('time (ms)')
plt.ylabel('membrane potential (mV)')
stdp_model = pynn.STDPMechanism(
timing_dependence=pynn.SpikePairRule(),
weight_dependence=pynn.AdditiveWeightDependence())
# connect stimulus
pynn.Projection(spikeSourceStim,
neuron,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() *
weightStim),
target='excitatory')
# create plastic synapse
prj = pynn.Projection(
spikeSourcePlastic,
neuron,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() * weightPlastic),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(slow=stdp_model))
neuron.record()
## custom correlation flags:
## 0: no weight change
## 1: one (or multiple) weight changes triggered by pre-post spike pairs
## 2: one (or multiple) weight changes triggered by post-pre spike pairs
#pynn.hardware.hwa.setLUT([1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0],
#[2,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0])
lastInputSpike = np.max(np.concatenate((stimulus, stimulusPlastic)))
runtime = lastInputSpike + stimulusOffset
# configure frequency of STDP controller manually, otherwise maximum frequency is used
pynn.hardware.hwa.autoSTDPFrequency = runtime
pynn.run(runtime)
stimParams = {'spike_times': np.concatenate((np.arange(100.0, 401.0, 50.0), [700.0]))}
stpParams = {'U': 0.4, 'tau_rec': 100.0}
runtime = 1000.0
pynn.setup(mappingOffset=column)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
dummy = pynn.Population(row, pynn.SpikeSourceArray, stimParams)
stimulus = pynn.Population(1, pynn.SpikeSourceArray, stimParams)
# enable and configure STP
stp_model = pynn.TsodyksMarkramMechanism(**stpParams)
pynn.Projection(stimulus, neuron,
method=pynn.AllToAllConnector(weights=weight * pynn.minExcWeight()),
target='excitatory',
synapse_dynamics=pynn.SynapseDynamics(fast=stp_model))
pynn.record_v(neuron[0], '')
pynn.run(runtime)
membrane = np.array(list(zip(pynn.timeMembraneOutput, pynn.membraneOutput)))
pynn.end()
# plot
import matplotlib.pyplot as plt
plt.plot(membrane[:,0], membrane[:,1])
plt.xlabel('time (ms)')
plt.ylabel('membrane potential (mV)')
plt.savefig('stp.png')