def emulation(seed, connType=0, returnValue=None):
numberNeurons = 192
noInputs = 15
pynn.setup()
rngPrj = pynn.random.NumpyRNG(seed=seed,
parallel_safe=True) # this may not work?!
neurons = pynn.Population(numberNeurons, pynn.IF_facets_hardware1)
connector = None
if connType == 0:
connector = pynn.FixedNumberPreConnector(noInputs,
weights=pynn.minExcWeight())
elif connType == 1:
connector = pynn.FixedNumberPostConnector(noInputs,
weights=pynn.minExcWeight())
elif connType == 2:
connector = pynn.FixedProbabilityConnector(float(noInputs) /
numberNeurons,
weights=pynn.minExcWeight())
else:
assert False, 'invalid connector type'
prj = pynn.Projection(neurons,
neurons,
method=connector,
target='inhibitory',
rng=rngPrj)
connList = []
for conn in prj.connections():
connList.append(conn)
assert len(connList) > 0, 'no connections'
assert len(connList) < numberNeurons * \
(numberNeurons - 1), 'all-to-all connection'
pynn.run(1.0)
pynn.end()
if returnValue != None:
returnValue = connList
else:
return connList
import pyNN.hardware.spikey as pynn
import numpy as np
pynn.setup()
# 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
weight = w * pynn.minInhWeight() #default 4.0
numInhPerNeuron = k #default 25
neuronParams = {
'v_reset' : -80.0, # mV # default
'e_rev_I' : -80.0, # mV # default
'v_rest' : -35.0, # mV # for const-current emulation set to > v_thresh #-30.0 default
'v_thresh' : -55.0, # mV # default
'g_leak' : 20.0 # nS -> tau_mem = 0.2nF / 20nS = 10ms
}
neurons = pynn.Population(popSize, pynn.IF_facets_hardware1, neuronParams)
# the inhibitory projection of the complete population to itself, with identical number
# of presynaptic neurons. Enable after measuring the regular-firing case.
pynn.Projection(neurons, neurons, pynn.FixedNumberPreConnector(numInhPerNeuron, weights=weight), target='inhibitory')
# record spikes
neurons.record()
# record membrane potential of first 4 neurons
pynn.record_v([neurons[0], neurons[1], neurons[2], neurons[3]], '')
# start experiment
pynn.run(runtime)
spikes = neurons.getSpikes()
# end experiment (network keeps running...)
pynn.end()
resultCollector = []
pynn.setup(
calibTauMem=False) #turn off calibration of membrane time constant tau_mem
#build network
stimuli = pynn.Population(noStims, pynn.SpikeSourcePoisson, {
'start': 0,
'duration': runtime,
'rate': rateStim
})
neurons = pynn.Population(noNeurons, pynn.IF_facets_hardware1)
pynn.Projection(stimuli,
neurons,
pynn.FixedNumberPreConnector(noInputs,
weights=weight *
pynn.minExcWeight()),
target='excitatory')
neurons.record()
#sweep over g_leak values, emulate network and record spikes
for gLeakValue in gLeakList:
neurons.set({'g_leak': gLeakValue})
pynn.run(runtime)
resultCollector.append([
gLeakValue,
old_div(float(len(neurons.getSpikes())) / noNeurons, runtime) * 1e3
])
pynn.end()
#plot results
'v_reset': -80.0, # mV # default
'e_rev_I': -80.0, # mV # default
'v_rest':
-35.0, # mV # for const-current emulation set to > v_thresh #-30.0 default
'v_thresh': -55.0, # mV # default
'g_leak': 20.0 # nS -> tau_mem = 0.2nF / 20nS = 10ms
}
neurons = pynn.Population(popSize, pynn.IF_facets_hardware1,
neuronParams)
# the inhibitory projection of the complete population to itself, with identical number
# of presynaptic neurons. Enable after measuring the regular-firing case.
pynn.Projection(neurons,
neurons,
pynn.FixedNumberPreConnector(numInhPerNeuron,
weights=weight),
target='inhibitory')
# record spikes
neurons.record()
# record membrane potential of first 4 neurons
pynn.record_v([neurons[0], neurons[1], neurons[2], neurons[3]], '')
# start experiment
pynn.run(runtime)
spikes = neurons.getSpikes()
# end experiment (network keeps running...)
pynn.end()