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Brian Documentation, Release 1.5dev
from brian import *
N = 100
c = .2
nu = linspace(1*Hz, 10*Hz, N)
P = c*dot(nu.reshape((N,1)), nu.reshape((1,N)))/mean(nu**2)
tauc = 5*ms
spikes = mixture_process(nu, P, tauc, 1*second)
input = SpikeGeneratorGroup(N, spikes)
S = SpikeMonitor(input)
run(1000 * ms)
raster_plot(S)
show()
Example: gap_junctions (misc)
Network of noisy IF neurons with gap junctions
from brian import *
N = 300
v0 = 5 * mV
tau = 20 * ms
sigma = 5 * mV
vt = 10 * mV
vr = 0 * mV
g_gap = 1. / N
beta = 60 * mV * 2 * ms
delta = vt - vr
eqs = '''
dv/dt=(v0-v)/tau+g_gap*(u-N*v)/tau : volt
du/dt=(N*v0-u)/tau : volt # input from other neurons
'''
def myreset(P, spikes):
P.v[spikes] = vr # reset
P.v += g_gap * beta * len(spikes) # spike effect
P.u -= delta * len(spikes)
group = NeuronGroup(N, model=eqs, threshold=vt, reset=myreset)
@network_operation
def noise(cl):
x = randn(N) * sigma * (cl.dt / tau) ** .5
group.v += x
group.u += sum(x)
trace = StateMonitor(group, 'v', record=[0, 1])
spikes = SpikeMonitor(group)
rate = PopulationRateMonitor(group)
run(1 * second)
subplot(311)
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Chapter 3. Getting started