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examples-frompapers_Brette_Guigon_2003.txt
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examples-frompapers_Brette_Guigon_2003.txt
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.. currentmodule:: brian
.. index::
pair: example usage; subplot
pair: example usage; plot
pair: example usage; run
pair: example usage; show
pair: example usage; raster_plot
pair: example usage; linked_var
pair: example usage; SpikeMonitor
pair: example usage; linspace
pair: example usage; NeuronGroup
pair: example usage; StateMonitor
.. _example-frompapers_Brette_Guigon_2003:
Example: Brette_Guigon_2003 (frompapers)
========================================
Reliability of spike timing
---------------------------
Adapted from Fig. 10D,E of
Brette R and E Guigon (2003). Reliability of Spike Timing Is a General Property
of Spiking Model Neurons. Neural Computation 15, 279-308.
This shows that reliability of spike timing is a generic property of spiking
neurons, even those that are not leaky.
This is a non-physiological model which can be leaky or anti-leaky depending
on the sign of the input I.
All neurons receive the same fluctuating input, scaled by a parameter p that
varies across neurons. This shows:
1. reproducibility of spike timing
2. robustness with respect to deterministic changes (parameter)
3. increased reproducibility in the fluctuation-driven regime (input crosses
the threshold)
::
from brian import *
N=500
tau=33*ms
taux=20*ms
sigma=0.02
eqs_input='''
B=2./(1+exp(-2*x))-1 : 1
dx/dt=-x/taux+(2/taux)**.5*xi : 1
'''
eqs='''
dv/dt=(v*I+1)/tau + sigma*(2/tau)**.5*xi : 1
I=0.5+3*p*B : 1
B : 1
p : 1
'''
input=NeuronGroup(1,eqs_input)
neurons=NeuronGroup(N,eqs,threshold=1,reset=0)
neurons.p=linspace(0,1,N)
neurons.v=rand(N)
neurons.B=linked_var(input,'B')
M=StateMonitor(input,'B',record=0)
S=SpikeMonitor(neurons)
run(1000*ms)
subplot(211) # The input
plot(M.times/ms,M[0])
subplot(212)
raster_plot(S)
plot([0,1000],[250,250],'r')
show()