postStd,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=-5.0),
receptor_type='inhibitory',
label='inh_proj',
)
sProj = sim.Projection(
preInh,
postShunt,
sim.OneToOneConnector(),
sim.StaticSynapse(weight=-5.0),
receptor_type='inhShunt',
label='shunt_proj',
)
sim.run(runtime)
data_exc = preExc.get_data().segments[0]
data_inh = preInh.get_data().segments[0]
data_std = postStd.get_data().segments[0]
data_shunt = postShunt.get_data().segments[0]
sim.end()
figsize = (15, 5)
plot_data(data_exc, data_inh, data_shunt, title='shunt', figsize=figsize)
plt.tight_layout()
plot_data(data_exc, data_inh, data_std, title='standard', figsize=figsize)
plt.tight_layout()
plt.show()
def run(self, params):
self._do_record = True
self._network = {}
self._network['timestep'] = 0.1 #ms
self._network['min_delay'] = 0.1 #ms
self._network['run_time'] = 1000
sim.setup(self._network['timestep'],
model_name=self.name,
backend='CUDA')
# Neuron populations
pops = {}
pops['input'] = self.gen_input_pop()
pops['output'] = self.gen_output_pop()
self._network['populations'] = pops
# Projections
projs = {}
projs['inupt to output'] = self.gen_input_to_output_proj()
self._network['projections'] = projs
# Run
log.info('Running sim for {}ms.'.format(self._network['run_time']))
sim.run(self._network['run_time'])
# Collect data
if self._do_record:
log.info('Collect data from all populations:')
for popname, pop in pops.items():
log.info(' -> Saving recorded data for "{}".'.format(popname))
# Voltages
voltagedata = pop.get_data('v')
signal = voltagedata.segments[0].analogsignals[0]
source_ids = signal.annotations['source_ids']
for idx in range(len(source_ids)):
s_id = source_ids[idx]
filename = "%s_%s_%s.dat" % (
pop.label, pop.id_to_index(s_id), signal.name)
vm = signal.transpose()[idx]
tt = np.array([
t * sim.get_time_step() / 1000. for t in range(len(vm))
])
times_vm = np.array([tt, vm / 1000.]).transpose()
np.savetxt(filename, times_vm, delimiter='\t', fmt='%s')
# Spikes
log.info(pop)
spikedata = pop.get_data('spikes')
filename = '{}.spikes'.format(pop.label)
thefile = open(filename, 'w')
for spiketrain in spikedata.segments[0].spiketrains:
source_id = spiketrain.annotations['source_id']
source_index = spiketrain.annotations['source_index']
# #log.info(pp.pprint(vars(spiketrain)))
for t in spiketrain:
thefile.write('%s\t%f\n' %
(source_index, t.magnitude / 1000.))
thefile.close()
# End
sim.end()
