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 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
connInh = pynn.AllToAllConnector(weights=weightInh)
connExc_strong = pynn.FixedProbabilityConnector(p_connect=1.0, weights=weightExc * 2)
# 1st neuron is stimulated by background
pynn.Projection(stimExc, neuronA, connExc, target="excitatory")
pynn.Projection(stimInh, neuronA, connInh, target="inhibitory")
# 2nd neuron is stimulated by 1st neuron
pynn.Projection(neuronA, neuronB, connExc_strong, synapse_dynamics=None, target="excitatory")
# define which observables to record
# spike times
neuronA.record()
# membrane potential
pynn.record_v(neuronB[0], '')
# execute the experiment
pynn.run(runtime)
# evaluate results
spikes = neuronA.getSpikes()[:,1]
membrane = pynn.membraneOutput
membraneTime = pynn.timeMembraneOutput
pynn.end()
####################################################################
# data visualization
####################################################################
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
# connect stimulus
pynn.Projection(stimExc, popCollector['exc'][0], pynn.FixedProbabilityConnector(p_connect=probExcExc, weights=weightStimExcExc), target='excitatory')
pynn.Projection(stimExc, popCollector['inh'][0], pynn.FixedProbabilityConnector(p_connect=probExcInh, weights=weightStimExcInh), target='excitatory')
# connect synfire chain populations
for popIndex in range(noPops):
#if popIndex < noPops - 1: # open chain
pynn.Projection(popCollector['exc'][popIndex], popCollector['exc'][(popIndex + 1) % noPops],
pynn.FixedProbabilityConnector(p_connect=probExcExc, weights=weightExcExc), target='excitatory')
pynn.Projection(popCollector['exc'][popIndex], popCollector['inh'][(popIndex + 1) % noPops],
pynn.FixedProbabilityConnector(p_connect=probExcInh, weights=weightExcInh), target='excitatory')
pynn.Projection(popCollector['inh'][popIndex], popCollector['exc'][popIndex],
pynn.FixedProbabilityConnector(p_connect=probInhExc, weights=weightInhExc), target='inhibitory')
# record from first neuron of first excitatory population of chain
pynn.record_v(popCollector['exc'][0][0], '')
# hack to elongate refractory period of all neurons
# will be configurable via neuron parameters, soon
pynn.hardware.hwa.setIcb(0.02)
pynn.run(runtime)
# collect all spikes in one array
spikeCollector = np.array([]).reshape(0,2)
for synType in ['exc', 'inh']:
for popIndex in range(noPops):
spikeCollector = np.vstack((spikeCollector, popCollector[synType][popIndex].getSpikes()))
# get membrane
membrane = pynn.membraneOutput
