def maxSpikesIn(runtime):
'''Maximum number of spikes that can be sent:
Should be limited by memory size on FPGA board (256MB => 256x1024x1024x8/32x3 approx. 200e6 spikes).'''
rate = 10.0
weight = 1.0
poissonParam = {'start': 0, 'duration': runtime, 'rate': rate}
pynn.setup()
stim = pynn.Population(256, pynn.SpikeSourcePoisson, poissonParam)
neuron = pynn.Population(192, pynn.IF_facets_hardware1)
prj = pynn.Projection(stim,
neuron,
pynn.AllToAllConnector(weights=pynn.minInhWeight() *
weight),
target='inhibitory')
neuron.record()
pynn.run(runtime)
spikes = neuron.getSpikes()
lost, sent = pynn.getInputSpikes()
print 'spikes in / out', sent, len(spikes)
pynn.end()
def test():
'''mapping of bio index to hardware index should work for networks
where not all neurons are recorded'''
pynn.setup()
if mappingOffset > 0:
dummy = pynn.Population(mappingOffset, pynn.IF_facets_hardware1)
neuronList = []
for i in range(noNeurons):
neuronList.append(pynn.Population(1, pynn.IF_facets_hardware1))
neuronList[-1].record()
dummy = pynn.Population(1, pynn.IF_facets_hardware1)
stim = pynn.Population(1, pynn.SpikeSourcePoisson)
for neuron in neuronList:
pynn.Projection(stim, neuron,
pynn.AllToAllConnector(weights=pynn.minExcWeight()))
pynn.run(1000.0)
pynn.end()
f = open('spikeyconfig.out')
for line in f:
for i in range(mappingOffset + 2 * noNeurons):
if line.find('w ' + str(192 + i)) >= 0:
weight = int(line.split(' ')[256 + 2 - 1])
print 192 + i, weight
assert (weight == shouldPatternWeights[i]
), 'results do not fit expectation'
f.close()
def run(mappingOffset):
"""
Measures firing rate of one neuron (determined by mappingOffset) in dependence on value of g_leak.
If linear fit to these firing rates does not show a significantly large slope,
g_leak is assumed to be not set correctly.
"""
pynn.setup(mappingOffset=mappingOffset,
calibTauMem=False,
calibSynDrivers=False,
calibVthresh=False)
# set v_rest over v_reset to get neuron firing
neuron = pynn.Population(1, pynn.IF_facets_hardware1, {
'v_rest':
pynn.IF_facets_hardware1.default_parameters['v_thresh'] + 10.0
})
neuron.record()
rateList = []
for gLeak in gLeakRange:
neuron.set({'g_leak': gLeak / default.iLeak_base})
pynn.hardware.hwa._neuronsChanged = True
pynn.run(runtime)
rateList.append(
[gLeak, float(len(neuron.getSpikes())) / runtime * 1e3])
pynn.end()
rateList = np.array(rateList)
pol = np.polyfit(rateList[:, 0], rateList[:, 1], 1) # linear fit
print 'fitted polynom:', pol
assert pol[0] > slopeMin, 'rate does not change with g_leak'
def getMem(self, voltageRest, mappingOffset, calibOutputPins,
calibNeuronMems):
import pyNN.hardware.spikey as pynn
pynn.setup(useUsbAdc=True,
avoidSpikes=True,
mappingOffset=mappingOffset,
calibTauMem=False,
calibOutputPins=calibOutputPins,
calibNeuronMems=calibNeuronMems)
neuron = pynn.Population(1, pynn.IF_facets_hardware1,
self.neuronParams)
#neuronDummy = pynn.Population(1, pynn.IF_facets_hardware1, self.neuronParams)
neuron.set({'v_rest': voltageRest})
#neuronDummy.set({'v_rest': self.voltageRange[0]})
neuron.record()
pynn.record_v(neuron[0], '')
pynn.run(self.runtime)
mem = pynn.membraneOutput
spikes = neuron.getSpikes()
pynn.end()
self.assertTrue(
(float(len(spikes)) / self.runtime * 1e3) <= self.limFreq,
'there should not be any (too much) spikes')
return mem.mean()
def run(noNeurons):
runtime = 1000.0
import numpy as np
import pyNN.hardware.spikey as pynn
pynn.setup()
neurons = pynn.Population(noNeurons, pynn.IF_facets_hardware1)
neurons.record()
stim = pynn.Population(10, pynn.SpikeSourcePoisson, {
'rate': 20.0,
'duration': runtime
})
prj = pynn.Projection(stim, neurons, pynn.AllToAllConnector())
prj.setWeights(pynn.maxExcWeight())
pynn.run(runtime)
spikes = neurons.getSpikes([])
# for neuron in np.unique(spikes[:,0]):
# print 'neuron', int(neuron), 'has', len(spikes[spikes[:,0] ==
# neuron]), 'spikes'
noSpikes = len(spikes)
lost, sent = pynn.getInputSpikes()
pynn.end()
print 'no neurons / spikes in / lost / out:', noNeurons + 1, sent, lost, noSpikes
return noSpikes
def test_noSpikesBug():
import numpy as np
import pyNN.hardware.spikey as pynn
duration = 10 * 1000.0 # ms
neuronParams = {
'v_reset': -80.0, # mV
'e_rev_I': -80.0, # mV
'v_rest': -45.0, # mV / rest above threshold
'v_thresh': -55.0, # mV
'g_leak': 20.0, # nS / without Scherzer calib approx. tau_m = 2ms
}
noTrials = 100
failCount = 0
for i in range(noTrials):
pynn.setup()
neuron = pynn.Population(1, pynn.IF_facets_hardware1, neuronParams)
neuron.record()
pynn.run(duration)
spikes = neuron.getSpikes()[:, 1]
pynn.end() # comment this out and everything is fine
if len(spikes) == 0:
failCount += 1
assert failCount == 0, str(
float(failCount) / noTrials * 1e2) + ' % of runs did not have spikes'
def maxRuntime(runtime):
'''Maximum runtime:
Limited by wrap around of counter (after approx. 6600s).
Can be extended to infinitly long runtimes by considering wrap around.
Subtract/Add offset to in/out spike times for each wrap around.'''
rate = 1.0
weight = 1.0
poissonParam = {'start': 0, 'duration': runtime, 'rate': rate}
pynn.setup()
stim = pynn.Population(1, pynn.SpikeSourcePoisson, poissonParam)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
prj = pynn.Projection(stim,
neuron,
pynn.AllToAllConnector(weights=pynn.minInhWeight() *
weight),
target='inhibitory')
neuron.record()
pynn.run(runtime)
spikes = neuron.getSpikes()
lost, sent = pynn.getInputSpikes()
print 'spikes in / out', sent, len(spikes)
pynn.end()
def maxSpikesOut(runtime):
'''Maximum number of spikes that can be received:
Should be limited by memory size on FPGA board (128MB approx. 100e6 spikes, other half for ADC).'''
neuronParams = {
'v_reset': -80.0, # mV
'e_rev_I': -80.0, # mV
'v_rest': -45.0, # mV / rest above threshold
'v_thresh': -55.0, # mV
'g_leak': 20.0, # nS / without Scherzer calib approx. tau_m = 2ms
}
pynn.setup()
neuron = pynn.Population(192, pynn.IF_facets_hardware1, neuronParams)
neuron.record()
pynn.run(runtime)
spikes = neuron.getSpikes()[:, 1]
lost, sent = pynn.getInputSpikes()
print 'spikes in / out', sent, len(spikes)
pynn.end()
def test_spikey5_allneurons():
'''
Tests mapping and firing of all 384 neurons.
'''
runtime = 1000.0
stimRate = 10.0
weight = 7
pynn.setup()
neurons = pynn.Population(384, pynn.IF_facets_hardware1)
stim = pynn.Population(10, pynn.SpikeSourcePoisson, {
'start': 0,
'duration': runtime,
'rate': stimRate
})
prj = pynn.Projection(
stim,
neurons,
method=pynn.AllToAllConnector(weights=pynn.minExcWeight() * weight),
target='excitatory')
pynn.run(runtime)
spikes = neurons.getSpikes()
print 'spikes from', len(np.unique(spikes)), 'different neurons'
# TODO: check for spikes from all neurons
pynn.end()
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 record_tau(self, neuronNr, iLeak, v_rest=None):
print 'now at neuron number', neuronNr
# linear dependency of simulation time on 1/iLeak
duration = 5.0*1000.0/float(iLeak)
duration = np.min([duration, 50000.0])
# initialize pyNN
mappingOffset = neuronNr
if self.chipVersion == 4:
mappingOffset = neuronNr - 192
p.setup(useUsbAdc=True, calibTauMem=False, calibVthresh=False, calibSynDrivers=False, calibIcb=False, mappingOffset=mappingOffset, workStationName=self.workstation)
# set g_leak such that iLeak is the desired value
iLeak_base = default.iLeak_base
g_leak = float(iLeak)/iLeak_base
# determine tau_mem, v_rest and v_reset all at once
trials = 0
params = deepcopy(self.neuronParams)
params['g_leak'] = g_leak
if v_rest != None:
params['v_rest'] = v_rest
neuron = p.Population(1, p.IF_facets_hardware1, params)
neuron.record()
p.record_v(neuron[0], '')
crossedTargetRate = False
while params['v_rest'] < self.maxVRest:
print 'now at trial', trials, '/ v_rest =', params['v_rest']
p.run(duration)
trace = p.membraneOutput
dig_spikes = neuron.getSpikes()[:,1]
memtime = p.timeMembraneOutput
timestep = memtime[1] - memtime[0]
# if neuron spikes with too low rate, try again with higher resting potential
if len(dig_spikes) < self.targetSpikes:
params['v_rest'] = params['v_rest'] + self.vRestStep
neuron.set(params)
print 'Neuron spiked with too low rate, trying again with parameters', params
trials += 1
else: # proper spiking
crossedTargetRate = True
break
if not crossedTargetRate:
utils.report('Could not find parameters for which neuron {0} spikes. Will return nan tau_mem'.format(neuronNr), self.reportFile)
return np.concatenate(([iLeak], [np.nan] * 6, [params['v_rest']]))
p.end()
# determine tau_mem from measurements
result = utils.fit_tau_mem(trace, memtime, dig_spikes, timestep=timestep, reportFile=self.reportFile)
if result == None: # fit failed
utils.report('Fit of membrane time constant for neuron {0} failed (iLeak = {1})'.format(neuronNr, iLeak), self.reportFile)
return np.concatenate(([iLeak], [np.nan] * 6, [params['v_rest']]))
return np.concatenate(([iLeak], result, [params['v_rest']]))
def runClosed():
global neurons
rateOutList = []
for rate in rateRange:
pynn.setup()
build(rate)
pynn.run(runtime)
rateOutList.append(
len(neurons.getSpikes()) / runtime * 1e3 / numNeurons)
pynn.end()
return rateOutList
def compareSpikesToMembrane(duration):
"""
Tests the precise timing of digital spikes and spikes extracted from the membrane potential.
The neuron is stimulated with Poisson spike sources.
"""
np.random.seed(int(time.time()))
neuronNo = np.random.random_integers(0, 191)
print 'Using neuron number', neuronNo
poissonParams = {'start': 100.0, 'duration': duration -
100.0, 'rate': 30.0} # offset of 100 ms to get all spikes
weightExc = 4 # digital hardware value
weightInh = 15 # digital hardware value
freqLimit = 1.0 # 1/s
meanLimit = 0.2 # ms
stdLimit = 0.2 # ms
import pyNN.hardware.spikey as pynn
pynn.setup(mappingOffset=neuronNo)
stimExc = pynn.Population(64, pynn.SpikeSourcePoisson, poissonParams)
stimInh = pynn.Population(192, pynn.SpikeSourcePoisson, poissonParams)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
prj = pynn.Projection(stimExc, neuron, pynn.AllToAllConnector(
weights=weightExc * pynn.minExcWeight()), target='excitatory')
prj = pynn.Projection(stimInh, neuron, pynn.AllToAllConnector(
weights=weightInh * pynn.minInhWeight()), target='inhibitory')
neuron.record()
pynn.record_v(neuron[0], '')
pynn.run(duration)
spikes = neuron.getSpikes()[:, 1]
membrane = pynn.membraneOutput
memTime = pynn.timeMembraneOutput
spikesMem, deriv, thresh = spikesFromMem(memTime, membrane)
pynn.end()
#plot(memTime, membrane, spikes, spikesMem, deriv, thresh)
print 'Spikes and spikes on membrane:', len(spikes), '/', len(spikesMem)
assert len(spikes) / duration * 1e3 >= freqLimit, 'Too less spikes.'
assert len(spikes) == len(spikesMem), 'Spikes do not match membrane.'
spikesDiff = spikesMem - spikes
spikesDiffMean = np.mean(spikesDiff)
spikesDiffStd = np.std(spikesDiff)
print 'Offset between spikes and membrane:', spikesDiffMean, '+-', spikesDiffStd
assert spikesDiffMean < meanLimit, 'Spike and membrane have too large offset.'
assert spikesDiffStd < stdLimit, 'Time axes of spikes and membrane are different.'
def runLoop():
global neurons, stim
rateOutList = []
pynn.setup()
build(0)
for rate in rateRange:
poissonParams = {'start': 0, 'duration': runtime, 'rate': rate}
stim.set(poissonParams)
pynn.run(runtime)
rateOutList.append(
len(neurons.getSpikes()) / runtime * 1e3 / numNeurons)
pynn.end()
return rateOutList
def emulate():
# pynn.setup(useUsbAdc=True)
pynn.setup()
stimI = pynn.Population(40, pynn.SpikeSourcePoisson, {
'start': 0,
'duration': runtime,
'rate': rate
})
stimE = pynn.Population(20, pynn.SpikeSourcePoisson, {
'start': 0,
'duration': runtime,
'rate': rate
})
neuron = pynn.Population(192, pynn.IF_facets_hardware1)
prjI = pynn.Projection(stimI,
neuron,
pynn.AllToAllConnector(weights=weight *
pynn.minInhWeight()),
target='inhibitory')
prjE = pynn.Projection(stimE,
neuron,
pynn.AllToAllConnector(weights=weight *
pynn.minExcWeight()),
target='excitatory')
stimI.record()
stimE.record()
neuron.record()
pynn.record_v(neuron[0], '')
pynn.run(runtime)
spikesInI = stimI.getSpikes()
spikesInE = stimE.getSpikes()
spikes = neuron.getSpikes()
mem = pynn.membraneOutput
print 'spikes out', len(spikes)
print 'spikes in', len(spikesInI), len(spikesInE)
print 'mem data points', len(mem)
pynn.end()
def recordTauRef(self, neuronNr, icb):
# necessary hardware setup
p.setup(useUsbAdc=True, mappingOffset=neuronNr-192, calibTauMem=True, calibVthresh=False, calibSynDrivers=False, calibIcb=False, workStationName=self.workstation)
p.hardware.hwa.setIcb(icb)
# observed neuron
neuron = p.Population(1, p.IF_facets_hardware1, self.neuronParams)
# stimulating population
input = p.Population(self.inputParameters['numInputs'], p.SpikeSourceArray, self.inputParameters['inputSpikes'])
# connect input and neuron
conn = p.AllToAllConnector(allow_self_connections=False, weights=self.inputParameters['weight'])
proj = p.Projection(input, neuron, conn, synapse_dynamics=None, target='excitatory')
# record spikes and membrane potential
neuron.record()
p.record_v(neuron[0],'')
# run experiment
p.run(self.duration)
# evaluate results
spikesDig = neuron.getSpikes()[:,1]
membrane = p.membraneOutput
time = p.timeMembraneOutput
# clean up
p.end()
# determine sampling bins
timestep = time[1] - time[0]
# detect analog spikes
spikesAna, isiAna = utils.find_spikes(membrane, time, spikesDig, reportFile=self.reportFile)
# determine refractory period from measurement of analog spikes
tau_ref, tau_ref_err, doubles_spikes = utils.fit_tau_refrac(membrane, timestep, spikesAna, isiAna, noDigSpikes=len(spikesDig), reportFile=self.reportFile, debugPlot=self.debugPlot)
return tau_ref, tau_ref_err, doubles_spikes, spikesDig
# set resting potential over spiking threshold
runtime = 1000.0 # ms
popSize = 192
weight = 7.0 * pynn.minExcWeight()
neuronParams = {"v_rest": -40.0}
neurons = pynn.Population(popSize, pynn.IF_facets_hardware1, neuronParams)
pynn.Projection(neurons, neurons, pynn.FixedNumberPreConnector(15, weights=weight), target="inhibitory")
neurons.record()
pynn.run(runtime)
spikes = neurons.getSpikes()
pynn.end()
# visualize
print "mean firing rate:", round(len(spikes) / runtime / popSize * 1000.0, 1), "1/s"
import matplotlib.pyplot as plt
color = "k"
plt.figure()
plt.plot(spikes[:, 1], spikes[:, 0], ls="", marker="o", ms=1, c=color, mec=color)
plt.xlim(0, runtime)
plt.xlabel("time (ms)")
plt.ylabel("neuron ID")
plt.ylim(-0.5, popSize - 0.5)
plt.savefig("decorr_network.png")
def calib(self):
self.result['datetime'] = dt.datetime.now()
self.result['temperature'] = 'TODO'
self.result['person'] = pwd.getpwuid(os.getuid()).pw_name
# one setup is necessary in order to determine spikey version
pynn.setup(workStationName=self.workstation, calibOutputPins=False, calibNeuronMems=False, calibTauMem=False, calibSynDrivers=False, calibVthresh=False, calibBioDynrange=False)
self.chipVersion = pynn.hardware.chipVersion()
for block in range(2):
if not self.chipVersion == 4 or block == 1:
for pin in range(4):
lower = -90.
upper = -40.
step = (upper - lower) / self.numVsteps
for vrest in numpy.arange(lower, upper+step/2., step):
pin_in = numpy.nan
pin_out = numpy.nan
for pinBlock in range(self.numPinBlocks):
neuron = block * 192 + pinBlock * 4 + pin
# necessary setup
mappingOffset = neuron
if self.chipVersion == 4:
mappingOffset = neuron % 192
pynn.setup(useUsbAdc=True, workStationName=self.workstation, mappingOffset=mappingOffset, rng_seeds=self.seeds, avoidSpikes=True, \
calibOutputPins=False, calibNeuronMems=False, calibTauMem=False, calibSynDrivers=False, calibVthresh=False)
self.neuronParams['v_rest'] = vrest
# set up network
n = pynn.create(pynn.IF_facets_hardware1,self.neuronParams,n=1)
pynn.record_v(n, '')
pynn.record(n, '')
#http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
traceSum = 0.0
traceSumSqr = 0.0
# execute the experiment in a loop
for i in range(self.numRuns):
pynn.run(self.duration, translateToBioVoltage=False)
if i==0:
pin_in = pynn.hardware.hwa.vouts[neuron/192,neuron%2 + 2]
else:
assert pin_in == pynn.hardware.hwa.vouts[neuron/192,neuron%2 + 2], 'vout should not differ'
mem = pynn.membraneOutput
memMean = mem.mean()
traceSum += memMean
traceSumSqr += numpy.power(memMean, 2)
noSpikes = len(pynn.spikeOutput[1])
if not float(noSpikes) / self.duration * 1e3 == 0:
self.noSpikesTotal += noSpikes
print 'there are', noSpikes, 'spikes on the membrane (most likely / hopefully ghost spikes)'
assert mem.std() < self.limMemStd, 'digital spikes and spikes on the membrane found!'
pin_out = traceSum / self.numRuns
pin_out_std = (traceSumSqr - (numpy.power(traceSum, 2) / self.numRuns)) / (self.numRuns - 1)
pynn.end()
print 'For neuron',neuron,'the written voltage',pin_in,'appeared on the scope as',pin_out,'/2'
#save raw data
newData = numpy.vstack((neuron, pin_in, pin_out, numpy.sqrt(pin_out_std))).T
if self.rawData == None:
self.rawData = newData
else:
self.rawData = numpy.vstack((self.rawData, newData))
def filter_and_fit(dataset):
#filter data
dataset = numpy.atleast_2d(dataset)
dataToFit = numpy.atleast_2d(dataset[dataset[:,1] >= self.voltageLimLow])
dataToFit = numpy.atleast_2d(dataToFit[dataToFit[:,1] <= self.voltageLimHigh])
noPins = len(numpy.unique(numpy.array(dataset[:,0] / 192, numpy.int) * 4 + dataset[:,0] % 4))
assert (len(dataset) - len(dataToFit)) % noPins == 0, 'discarding data failed'
print 'discarded', (len(dataset) - len(dataToFit)) / noPins, 'data points'
#fit polynomial
return numpy.polyfit(dataToFit[:,2], dataToFit[:,1], self.polyDegree)
for block in range(2):
if self.chipVersion == 4 and block == 0:
continue
for pin in range(4):
#data for output pin calibration
dataOnePin = self.rawData[numpy.array(self.rawData[:,0] / 192, numpy.int) * 4 + self.rawData[:,0] % 4 == block * 4 + pin]
#calculate mean over neurons with same pin
vouts = numpy.unique(dataOnePin[:,1])
mean = []
std = []
for vout in vouts:
mean.append(numpy.mean(dataOnePin[dataOnePin[:,1] == vout][:,2]))
std.append(numpy.std(dataOnePin[dataOnePin[:,1] == vout][:,2]))
dataOnePinMean = numpy.vstack((numpy.zeros_like(vouts), vouts, mean, std)).T
self.result['polyFitOutputPins']['pin' + str(block * 4 + pin)] = filter_and_fit(dataOnePinMean)
for pinBlock in range(self.numPinBlocks):
neuron = block * 192 + pinBlock * 4 + pin
#data for membrane calibration of single neurons
dataOneNeuron = self.rawData[self.rawData[:,0] == neuron]
self.result['polyFitNeuronMems']['neuron' + str(neuron).zfill(3)] = filter_and_fit(dataOneNeuron)
print 'total number of spikes', self.noSpikesTotal, 'in', len(numpy.unique(numpy.array(self.rawData[:,0] / 192, numpy.int) * 4 + self.rawData[:,0] % 4)) * (self.numVsteps + 1) * self.numPinBlocks * self.numRuns, 'runs'
return self.result, self.rawData
def emulate(self, driverIndexExc, driverIndexInh=None, drvirise=None, drvifallExc=None, drvifallInh=None, drvioutExc=None, drvioutInh=None, filename=None, calibSynDrivers=False):
'''Run emulations on hardware.'''
assert self.stimParams != None, 'specifiy stimulus first'
pynn.setup(calibTauMem=True,
calibSynDrivers=calibSynDrivers,
calibVthresh=False,
calibIcb=False,
workStationName=self.workstation,
writeConfigToFile=False)
#create neuron
self.neuronList.sort()
neuronCollector = []
currentIndex = 0
for neuronIndex in self.neuronList:
if neuronIndex > currentIndex: #insert dummy neurons if neuronList not continuous
dummyPopSize = neuronIndex - currentIndex
dummy = pynn.Population(dummyPopSize, pynn.IF_facets_hardware1, self.neuronParams)
currentIndex += dummyPopSize
self.logger.debug('inserted ' + str(dummyPopSize) + ' dummy neurons')
if neuronIndex == currentIndex:
neuron = pynn.Population(1, pynn.IF_facets_hardware1, self.neuronParams)
currentIndex += 1
neuron.record()
neuronCollector.append(neuron)
else:
raise Exception('Could not create all neurons')
#create input and connect to neuron
synDrivers = [[driverIndexExc, 'excitatory'], [driverIndexInh, 'inhibitory']]
synDrivers.sort()
currentIndex = 0
for synDriver in synDrivers:
toInsertIndex = synDriver[0]
targetType = synDriver[1]
if toInsertIndex == None:
self.logger.debug('skipping ' + targetType + ' stimulation')
continue
if toInsertIndex > currentIndex:
dummyPopSize = toInsertIndex - currentIndex
dummy = pynn.Population(dummyPopSize, pynn.SpikeSourcePoisson)
currentIndex += dummyPopSize
self.logger.debug('inserted ' + str(dummyPopSize) + ' dummy synapse drivers')
stim = pynn.Population(1, pynn.SpikeSourcePoisson, self.stimParams[targetType])
self.logger.debug('inserted 1 stimulus of type ' + targetType + ' with rate ' + str(self.stimParams[targetType]['rate']))
if targetType == 'excitatory':
hardwareWeightTemp = self.hardwareWeight * pynn.minExcWeight()
elif targetType == 'inhibitory':
hardwareWeightTemp = self.hardwareWeight * pynn.minInhWeight()
else:
raise Exception('Synapse type not supported!')
for neuron in neuronCollector:
pynn.Projection(stim, neuron, method=pynn.AllToAllConnector(weights=hardwareWeightTemp), target=targetType)
currentIndex += 1
#set custom parameters
if drvirise != None:
pynn.hardware.hwa.drvirise = drvirise
else:
pynn.hardware.hwa.drvirise = default.drvirise
if drvifallExc != None:
pynn.hardware.hwa.drvifall_base['exc'] = drvifallExc
else:
pynn.hardware.hwa.drvifall_base['exc'] = default.drvifall_base['exc']
if drvifallInh != None:
pynn.hardware.hwa.drvifall_base['inh'] = drvifallInh
else:
pynn.hardware.hwa.drvifall_base['inh'] = default.drvifall_base['inh']
if drvioutExc != None:
pynn.hardware.hwa.drviout_base['exc'] = drvioutExc
else:
pynn.hardware.hwa.drviout_base['exc'] = default.drviout_base['exc']
if drvioutInh != None:
pynn.hardware.hwa.drviout_base['inh'] = drvioutInh
else:
pynn.hardware.hwa.drviout_base['inh'] = default.drviout_base['inh']
#run
pynn.run(self.runtime)
#temperature = pynn.hardware.hwa.getTemperature()
#self.logger.debug('temperature ' + str(temperature) + ' degree Celsius')
#obtain firing rate
spikes = None
neuronCount = 0
for neuron in neuronCollector:
spikesNeuron = neuron.getSpikes()
neuronCount += neuron.size
if spikes == None:
spikes = spikesNeuron
else:
spikes = np.concatenate((spikes, spikesNeuron))
if len(spikes) > 0:
spikes = spikes[spikes[:,1] > self.cutfirst]
if len(spikes) > 0:
spikes = spikes[spikes[:,1] <= self.runtime]
if filename != None:
np.savetxt(os.path.join(self.folder, filename), spikes)
spikesPerNeuron = float(len(spikes)) / neuronCount
pynn.end()
rate = spikesPerNeuron * 1e3 / (self.runtime - self.cutfirst)
return rate
