def run(parameters, sim_list):
sim_time = parameters.sim_time
spike_interval = parameters.spike_interval
stgen = StGen()
seed = parameters.seed
stgen.seed(seed)
model_parameters = ParameterSet({
'system':
parameters.system,
'input_spike_times':
stgen.poisson_generator(1000.0 / spike_interval,
t_stop=sim_time,
array=True),
'cell_type':
parameters.cell.type,
'cell_parameters':
parameters.cell.params,
'plasticity': {
'short_term': None,
'long_term': None
},
'weights':
parameters.weights,
'delays':
parameters.delays,
})
networks = MultiSim(sim_list, SimpleNetwork, model_parameters)
networks.run(sim_time)
spike_data = networks.get_spikes()
vm_data = networks.get_v()
networks.end()
return spike_data, vm_data, model_parameters
def build_static_bar(self, f0=100,stim_start=0, stim_duration=4000, phi=0, save=None):
if rank()==0: print "Creating or loading the LGN input: static bar at %s rad...."%phi
sx=.7*self.N
sy=0.1*self.N
n0=(self.N-1)/2.
spk = StGen(seed=5423689)
st = []
for i in range(self.N):
for j in range(self.N):
bar_fr=f0*np.exp(-((i-n0)*np.cos(phi)+(j-n0)*np.sin(phi))**2/sx**2-(-(i-n0)*np.sin(phi)+(j-n0)*np.cos(phi))**2/sy**2)
spk_times = spk.poisson_generator(bar_fr, 0, stim_duration, array=True)
st.append(spk_times)
for cell,spikes in zip(self.LGN, st):
cell.spike_times = spikes+stim_start
if save:
cPickle.dump(st, open(save,'w'))
def run(parameters, sim_list):
sim_time = parameters.sim_time
spike_interval = parameters.spike_interval
stgen = StGen()
seed = parameters.seed
stgen.seed(seed)
model_parameters = ParameterSet({
'system': parameters.system,
'input_spike_times': stgen.poisson_generator(1000.0/spike_interval, t_stop=sim_time, array=True),
'cell_type': parameters.cell.type,
'cell_parameters': parameters.cell.params,
'plasticity': { 'short_term': None, 'long_term': None },
'weights': parameters.weights,
'delays': parameters.delays,
})
networks = MultiSim(sim_list, SimpleNetwork, model_parameters)
networks.run(sim_time)
spike_data = networks.get_spikes()
vm_data = networks.get_v()
networks.end()
return spike_data, vm_data, model_parameters
sim_time = 200.0
spike_interval = 20.0 # ms
recording_interval = 1.0
stgen = StGen()
seed = int(1e9 * (time() % 1))
stgen.seed(seed)
parameters = ParameterSet({
'system': {
'timestep': 0.01,
'min_delay': 0.1,
'max_delay': 10.0
},
'input_spike_times':
stgen.poisson_generator(rate=1000.0 / spike_interval,
t_stop=sim_time,
array=True),
'trigger_spike_times':
stgen.poisson_generator(rate=1000.0 / spike_interval,
t_stop=sim_time,
array=True),
'cell_type':
'IF_curr_exp',
'cell_parameters': {
'tau_refrac': 10.0,
'tau_m': 2.0,
'tau_syn_E': 1.0
},
'plasticity': {
'short_term': None,
'long_term': {
cellsA.randomInit(voltDistr)
cellsB.randomInit(voltDistr)
freq = 150 # Hz
number = int(tstop*freq/1000.0)
print "Number of spikes expected in %d ms at %dHz: %d"%(tstop, freq, number)
from NeuroTools.stgen import StGen
stgen = StGen()
stgen.seed(seed)
spike_times = stgen.poisson_generator(rate=freq, t_stop=tstop, array=True)
input_population = Population(cellNumA, SpikeSourceArray, {'spike_times': spike_times}, label="inputsToA")
for i in input_population:
i.spike_times = stgen.poisson_generator(rate=freq, t_stop=tstop, array=True)
print "spike_times: " +str(i.spike_times)
inputConns = []
for i in range(0,cellNumA):
inputConns.append([i, i, 0.1, 3.0])
connector2= FromListConnector(inputConns)
input_proj = Projection(input_population, cellsA, connector2, target='excitatory', label='InputProj' ,synapse_dynamics=None)
from NeuroTools.stgen import StGen
from simple_network import SimpleNetwork
from calc import STDPSynapse
PLOT_FIGURES = True
sim_list = [nest, neuron, pcsim]
sim_time = 200.0
spike_interval = 20.0 # ms
recording_interval = 1.0
stgen = StGen()
seed = int(1e9*(time()%1))
stgen.seed(seed)
parameters = ParameterSet({
'system': { 'timestep': 0.01, 'min_delay': 0.1, 'max_delay': 10.0 },
'input_spike_times': stgen.poisson_generator(rate=1000.0/spike_interval, t_stop=sim_time, array=True),
'trigger_spike_times': stgen.poisson_generator(rate=1000.0/spike_interval, t_stop=sim_time, array=True),
'cell_type': 'IF_curr_exp',
'cell_parameters': { 'tau_refrac': 10.0, 'tau_m': 2.0, 'tau_syn_E': 1.0 },
'plasticity': { 'short_term': None,
'long_term': {
'timing_dependence': { 'model': 'SpikePairRule',
'params': { 'tau_plus': 20.0,
'tau_minus': 20.0 }},
'weight_dependence': { 'model': 'AdditiveWeightDependence',
'params': { 'w_min': 0, 'w_max': 0.1,
'A_plus': 0.01, 'A_minus': 0.01 }},
'ddf': 1.0,
}
},
'weights': 0.01,
cellsA.randomInit(voltDistr)
cellsB.randomInit(voltDistr)
freq = 150 # Hz
number = int(tstop * freq / 1000.0)
print "Number of spikes expected in %d ms at %dHz: %d" % (tstop, freq, number)
from NeuroTools.stgen import StGen
stgen = StGen()
stgen.seed(seed)
spike_times = stgen.poisson_generator(rate=freq, t_stop=tstop, array=True)
input_population = Population(cellNumA,
SpikeSourceArray, {'spike_times': spike_times},
label="inputsToA")
for i in input_population:
i.spike_times = stgen.poisson_generator(rate=freq,
t_stop=tstop,
array=True)
print "spike_times: " + str(i.spike_times)
inputConns = []
for i in range(0, cellNumA):
inputConns.append([i, i, 0.1, 3.0])
