def runTest(self):
import numpy
import pyNN.hardware.brainscales as pynn
# set-up the simulator
pynn.setup(
timestep=0.1,
useSystemSim=True,
speedupFactor=8000,
ignoreDatabase=True,
ignoreHWParameterRanges=True,
hardware=pynn.hardwareSetup['one-hicann'],
)
# Set the neuron model class
neuron_model = pynn.IF_cond_exp # I&F
neuron_parameters = {
'cm': 0.281, # membrane capacitance nF
'e_rev_E': 0.0, # excitatory reversal potential in mV
'e_rev_I':
-50.0, # inhibitory reversal potential in mV (HIGHER THAN v_thresh)
'i_offset': 0.0, # offset current
'tau_m': 9.3667, # membrane time constant
'tau_refrac': 1.0, # absolute refractory period
'tau_syn_E': 5.0, # excitatory synaptic time constant
'tau_syn_I': 5.0, # inhibitory synaptic time constant
'v_reset': -70.6, # reset potential in mV
'v_rest': -70.6, # resting potential in mV
'v_thresh': -55., # spike initiaton threshold voltage in mV
}
# We create a Population with 1 neuron of our
N1 = pynn.Population(size=1,
cellclass=neuron_model,
cellparams=neuron_parameters)
spiketimes = numpy.arange(10., 60., 5.)
S1 = pynn.Population(1,
pynn.SpikeSourceArray,
cellparams={'spike_times': spiketimes})
pynn.Projection(S1,
N1,
pynn.AllToAllConnector(weights=0.010425507084882381),
target='excitatory') # max weight in hardware
# record the voltage of all neurons of the population
N1.record()
# run the simulation for 200 ms
pynn.run(80.)
spikes = N1.getSpikes()
pynn.end()
self.assertEqual(
spikes.shape, (3, 2),
"Number of spikes is not as expected") # should have 3 spikes!!
def test_restart_loop():
extra = {"loglevel": 0, "useSystemSim": True, "hardware": sim.hardwareSetup["one-hicann"]}
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
def test_restart_loop():
extra = {
'loglevel': 0,
'useSystemSim': True,
'hardware': sim.hardwareSetup['one-hicann']
}
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
def run_experiment(self, weight_distortion=None):
import pyNN.hardware.brainscales as pynn
setup_params = dict(
useSystemSim=True,
ignoreDatabase=True,
ignoreHWParameterRanges=True,
hardware=pynn.hardwareSetup["one-hicann"],
rng_seeds=[123567],
)
if weight_distortion is not None:
setup_params['ess_params'] = {
'weightDistortion': weight_distortion
}
pynn.setup(**setup_params)
num_neurons = 2
pop = pynn.Population(num_neurons, pynn.IF_cond_exp)
# two different input spike trains, with identical rate but shifted to avoid loss in merger tree
spike_times1 = np.arange(100., 500, 10.)
spike_times2 = np.arange(105., 505, 10.)
pop_source = pynn.Population(2, pynn.SpikeSourceArray)
pop_source.tset('spike_times', [spike_times1, spike_times2])
weight = 0.1 # weight = pop[0].cell.getConductanceRange("weight")[1]
conn = pynn.OneToOneConnector(weights=weight)
proj_0 = pynn.Projection(pop_source, pop, conn)
pop.record()
pynn.run(600.)
spikes = pop.getSpikes()
spikes_nrn_0 = spikes[spikes[:, 0] == 0, 1]
spikes_nrn_1 = spikes[spikes[:, 0] == 1, 1]
pynn.end()
return len(spikes_nrn_0), len(spikes_nrn_1)
def test_restart_loop():
if not have_hardware_brainscales:
raise SkipTest
extra = {'loglevel':0, 'useSystemSim': True, 'hardware': sim.hardwareSetup['one-hicann']}
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
#def test_several_runs():
if not have_hardware_brainscales:
raise SkipTest
def test_restart_loop():
if not have_hardware_brainscales:
raise SkipTest
extra = {'loglevel': 0, 'useSystemSim': True, 'hardware': sim.hardwareSetup['one-hicann']}
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
sim.setup(**extra)
sim.run(10.0)
sim.end()
# def test_several_runs():
if not have_hardware_brainscales:
raise SkipTest
def tearDown(self):
sim.end()
def test_end(self):
sim.setup(**extra)
sim.end() # need a better test
def tearDown(self):
sim.end()
def test_end(self):
sim.setup(**extra)
sim.end() # need a better test
def runTest(self):
import numpy
import pyNN.hardware.brainscales as pynn
# set-up the simulator
pynn.setup(
timestep=0.1,
useSystemSim=True,
speedupFactor=10000,
ignoreDatabase=True,
ignoreHWParameterRanges=True,
hardware=pynn.hardwareSetup['one-hicann'],
)
# Set the neuron model class
neuron_model = pynn.IF_cond_exp # I&F
neuron_parameters = {
'cm': 0.281, # membrane capacitance nF
'e_rev_E': 0.0, # excitatory reversal potential in mV
'e_rev_I': -100.0, # inhibitory reversal potential in mV
'i_offset': 0.0, # offset current
'tau_m': 9.3667, # membrane time constant
'tau_refrac': 1.0, # absolute refractory period
'tau_syn_E': 5.0, # excitatory synaptic time constant
'tau_syn_I': 5.0, # inhibitory synaptic time constant
'v_reset': -70.6, # reset potential in mV
'v_rest': -70.6, # resting potential in mV
'v_thresh': -55., # spike initiaton threshold voltage in mV
}
# We create a Population with 1 neuron of our
N1 = pynn.Population(size=2,
cellclass=neuron_model,
cellparams=neuron_parameters)
N1a = N1[0:1]
N1b = N1[1:2]
v_shift = 5.
# shift all voltages of 2nd neuron by 5 mV
N1b.set('v_rest', neuron_parameters['v_rest'] - v_shift)
N1b.set('v_reset', neuron_parameters['v_reset'] - v_shift)
N1b.set('v_thresh', neuron_parameters['v_thresh'] - v_shift)
N1b.set('e_rev_E', neuron_parameters['e_rev_E'] - v_shift)
N1b.set('e_rev_I', neuron_parameters['e_rev_I'] - v_shift)
spiketimes = [10., 50., 110., 122.5]
S1 = pynn.Population(1,
pynn.SpikeSourceArray,
cellparams={'spike_times': spiketimes})
pynn.Projection(S1,
N1,
pynn.AllToAllConnector(weights=0.0123938304114),
target='inhibitory') # max weight in hardware
# record the voltage of all neurons of the population
N1.record_v()
# run the simulation for 200 ms
pynn.run(150.)
# format of get_v: [id, t, v]
mean_v_a = N1a.get_v().transpose()[2].mean()
mean_v_b = N1b.get_v().transpose()[2].mean()
self.assertAlmostEqual(
mean_v_a,
mean_v_b + v_shift,
places=3,
msg=
'The mean voltage of neuron a and b should have a difference of 5 mV'
)
pynn.end()
def test_sim_without_setup():
if not have_hardware_brainscales:
raise SkipTest
sim.end()
def test_sim_without_setup():
sim.end()
def test_sim_without_setup():
if not have_hardware_brainscales:
raise SkipTest
sim.end()
def test_sim_without_setup():
sim.end()
def runTest(self):
import numpy
backend = "hardware.brainscales"
import pyNN.hardware.brainscales as pynn
# set-up the simulator
pynn.setup(
timestep=0.1,
useSystemSim=True,
speedupFactor=8000,
ignoreDatabase=True,
ignoreHWParameterRanges=True,
hardware=pynn.hardwareSetup['one-hicann'],
)
neuron_count = 1 # size of the Population we will create
# Set the neuron model class
neuron_model = pynn.EIF_cond_exp_isfa_ista # an Adaptive Exponential I&F Neuron
neuron_parameters = {
'a' : 10.0, # adaptation variable a in nS
'b' : 0.0805, # adaptation variable b in pA
'cm' : 0.281, # membrane capacitance nF
'delta_T' : 2.0, # delta_T fom Adex mod in mV, determines the sharpness of spike initiation
'e_rev_E' : 0.0, # excitatory reversal potential in mV
'e_rev_I' : -100.0, # inhibitory reversal potential in mV
'i_offset' : 0.0, # offset current
'tau_m' : 9.3667, # membrane time constant
'tau_refrac' : 8.0, # absolute refractory period
'tau_syn_E' : 5.0, # excitatory synaptic time constant
'tau_syn_I' : 5.0, # inhibitory synaptic time constant
'tau_w' : 144.0, # adaptation time constant
'v_reset' : -70.6, # reset potential in mV
'v_rest' : -70.6, # resting potential in mV
'v_spike' : -40.0, # spike detection voltage in mV
'v_thresh' : -66.9, # spike initiaton threshold voltage in mV
}
# We create a Population with 1 neuron of our
N1 = pynn.Population( size = 1, cellclass = neuron_model, cellparams = neuron_parameters)
N1.initialize('v',neuron_parameters['v_rest'])
spiketimes = numpy.arange(100.,800.,0.5)
S1 = pynn.Population(1,pynn.SpikeSourceArray, cellparams = {'spike_times':spiketimes})
pynn.Projection(S1,N1,pynn.OneToOneConnector(weights=0.0123938304114), target='inhibitory') # max weight in hardware
# record the spikes of all neurons of the population
N1.record()
# run the simulation for 500 ms
pynn.run(1000.)
num_spikes = 0
# After the simulation, we get Spikes and save the voltage trace to a file
spike_times = N1.getSpikes()
for pair in spike_times:
print "Neuron ", int(pair[0]), " spiked at ", pair[1]
num_spikes += 1
pynn.end()
self.assertEqual(int(num_spikes),1,'The Neuron spiked more than once, i.e. there was a rebound burst, and not a single rebound spike as it should be')
