def genMaxOpti(nb):
res = []
for i in xrange(0, nb):
a = brian.rand()
b = brian.rand()
c = brian.rand()
if max(a,b,c) == a:
res.append('a')
if max(b,c) == b:
res.append('b')
res.append('c')
else:
res.append('c')
res.append('b')
elif max(a,b,c) == b:
res.append('b')
if max(a,c) == a:
res.append('a')
res.append('c')
else:
res.append('c')
res.append('a')
else:
res.append('c')
if max(a,b) == a:
res.append('a')
res.append('b')
else:
res.append('b')
res.append('a')
return res
def _build_model(self, traj, brian_list, network_dict):
"""Builds the neuron groups from `traj`.
Adds the neuron groups to `brian_list` and `network_dict`.
"""
model = traj.parameters.model
# Create the equations for both models
eqs_dict = self._build_model_eqs(traj)
# Create inhibitory neurons
eqs_i = eqs_dict['i']
neurons_i = NeuronGroup(N=model.N_i,
model=eqs_i,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Create excitatory neurons
eqs_e = eqs_dict['e']
neurons_e = NeuronGroup(N=model.N_e,
model=eqs_e,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Set the bias terms
neurons_e.mu = rand(
model.N_e) * (model.mu_e_max - model.mu_e_min) + model.mu_e_min
neurons_i.mu = rand(
model.N_i) * (model.mu_i_max - model.mu_i_min) + model.mu_i_min
# Set initial membrane potentials
neurons_e.V = rand(model.N_e)
neurons_i.V = rand(model.N_i)
# Add both groups to the `brian_list` and the `network_dict`
brian_list.append(neurons_i)
brian_list.append(neurons_e)
network_dict['neurons_e'] = neurons_e
network_dict['neurons_i'] = neurons_i
def _build_model(self, traj, brian_list, network_dict):
"""Builds the neuron groups from `traj`.
Adds the neuron groups to `brian_list` and `network_dict`.
"""
assert(isinstance(traj,SingleRun))
model = traj.parameters.model
# Create the equations for both models
eqs_dict = self._build_model_eqs(traj)
# Create inhibitory neurons
eqs_i = eqs_dict['i']
neurons_i = NeuronGroup(N=model.N_i,
model = eqs_i,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Create excitatory neurons
eqs_e = eqs_dict['e']
neurons_e = NeuronGroup(N=model.N_e,
model = eqs_e,
threshold=model.V_th,
reset=model.reset_func,
refractory=model.refractory,
freeze=True,
compile=True,
method='Euler')
# Set the bias terms
neurons_e.mu =rand(model.N_e) * (model.mu_e_max - model.mu_e_min) + model.mu_e_min
neurons_i.mu =rand(model.N_i) * (model.mu_i_max - model.mu_i_min) + model.mu_i_min
# Set initial membrane potentials
neurons_e.V = rand(model.N_e)
neurons_i.V = rand(model.N_i)
# Add both groups to the `brian_list` and the `network_dict`
brian_list.append(neurons_i)
brian_list.append(neurons_e)
network_dict['neurons_e']=neurons_e
network_dict['neurons_i']=neurons_i
def genTabFreq(maxi):
res = []
if maxi == 'a':
if brian.rand() > 0.5:
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
else:
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
elif maxi == 'b':
if brian.rand() > 0.5:
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
else:
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
else:
if brian.rand() > 0.5:
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
else:
res.append(2*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(2*brian.Hz)
res.append(50*brian.Hz)
res.append(50*brian.Hz)
return res
#! /usr/bin/python # -*- coding: utf-8 -*- import brian tau = 20 * brian.msecond Vt = -50 * brian.mvolt Vr = -60 * brian.mvolt El = -60 * brian.mvolt N = 40 G = brian.NeuronGroup(N=N, model='dV/dt = -(V-El)/tau : volt' , threshold=Vt, reset=Vr) M = brian.SpikeMonitor(G) G.V = Vr + 2*(Vt-Vr)*brian.rand(N) brian.run(1 * brian.second) print M.nspikes
