def test_regular_packed():
"""Maximum rate with packing:
Each clock cycle a full (filled with 3 spikes) spike packet."""
import numpy as np
import pyNN.hardware.spikey as pynn
duration = 1000.0 # ms
h = 1e3 / 5000.0 / 2.0 # 0.1 ms
spikeTimes = np.arange(0, duration + h / 2.0, h)
pynn.setup()
stim = pynn.Population(256, pynn.SpikeSourceArray)
# spikes have to be distributed over blocks of line drivers for efficient
# packing
stim[0].set_parameters(spike_times=spikeTimes)
stim[64].set_parameters(spike_times=spikeTimes)
stim[128].set_parameters(spike_times=spikeTimes)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
pynn.Projection(stim,
neuron,
method=pynn.AllToAllConnector(weights=0),
target='inhibitory')
neuron.record()
pynn.run(duration)
lost, sent = pynn.getInputSpikes()
print 'Number of input spikes (lost, sent)', lost, sent
assert lost == 0, 'There should not be any spikes lost!'
pynn.end()
def test_poisson():
"""Test with Poisson source."""
import pyNN.hardware.spikey as pynn
duration = 1000.0 # ms
rate = 5000.0 # 1/s
poissonParam = {'start': 0, 'duration': duration, 'rate': rate}
limLost = 1.0 # %
pynn.setup()
stim = pynn.Population(1, pynn.SpikeSourcePoisson, poissonParam)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
pynn.Projection(stim,
neuron,
method=pynn.AllToAllConnector(weights=0),
target='inhibitory')
neuron.record()
pynn.run(duration)
lost, sent = pynn.getInputSpikes()
print 'Number of input spikes (lost, sent, %lost)', lost, sent, float(
lost) / sent * 1e2
assert float(lost) / sent * 1e2 < limLost, 'Too many spikes lost!'
pynn.end()
def test_regular():
"""Maximum rate without packing:
Each second clock cycle a minimal loaded (filled with 1 spikes) spike packet."""
import numpy as np
import pyNN.hardware.spikey as pynn
import time
np.random.seed(int(time.time()))
lineDriverNo = np.random.random_integers(0, 255)
print 'Using line driver number', lineDriverNo
duration = 1000.0 # ms
h = 1e3 / 5000.0 # 0.2 ms
pynn.setup()
stim = pynn.Population(256, pynn.SpikeSourceArray)
stim[lineDriverNo].set_parameters(
spike_times=np.arange(0, duration + h / 2.0, h))
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
pynn.Projection(stim,
neuron,
method=pynn.AllToAllConnector(weights=0),
target='inhibitory')
neuron.record()
pynn.run(duration)
lost, sent = pynn.getInputSpikes()
print 'Number of input spikes (lost, sent)', lost, sent
assert lost == 0, 'There should not be any spikes lost!'
pynn.end()
def testRegularMaxPacked():
'''Maximum rate with packing:
Each clock cycle a full (filled with 3 spikes) spike packet.'''
import numpy as np
import pyNN.hardware.spikey as pynn
duration = 10000.0 # ms
h = 1e3 / 5000.0 / 2.0 # 10kHz for each of 3 sources = 30kHz
spikeTimes = np.arange(0, duration + h / 2.0, h)
pynn.setup()
stim = pynn.Population(256, pynn.SpikeSourceArray)
stim[0].set_parameters(spike_times=spikeTimes)
stim[63].set_parameters(spike_times=spikeTimes)
stim[127].set_parameters(spike_times=spikeTimes)
neuron = pynn.Population(1, pynn.IF_facets_hardware1)
pynn.Projection(stim,
neuron,
method=pynn.AllToAllConnector(weights=0),
target='inhibitory')
neuron.record()
pynn.run(duration)
print 'no out spikes:', len(neuron.getSpikes())
lost, sent = pynn.getInputSpikes()
print 'no in spikes (lost, sent)', lost, sent
assert lost == 0, 'there should not be any spikes lost!'
pynn.end()
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 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 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()