Networks of spiking neurons: a review of tools and strategies (Brette et al. 2007)

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Accession:83319
This package provides a series of codes that simulate networks of spiking neurons (excitatory and inhibitory, integrate-and-fire or Hodgkin-Huxley type, current-based or conductance-based synapses; some of them are event-based). The same networks are implemented in different simulators (NEURON, GENESIS, NEST, NCS, CSIM, XPP, SPLIT, MVAspike; there is also a couple of implementations in SciLab and C++). The codes included in this package are benchmark simulations; see the associated review paper (Brette et al. 2007). The main goal is to provide a series of benchmark simulations of networks of spiking neurons, and demonstrate how these are implemented in the different simulators overviewed in the paper. See also details in the enclosed file Appendix2.pdf, which describes these different benchmarks. Some of these benchmarks were based on the Vogels-Abbott model (Vogels TP and Abbott LF 2005).
Reference:
1 . Vogels TP, Abbott LF (2005) Signal propagation and logic gating in networks of integrate-and-fire neurons. J Neurosci 25:10786-95 [PubMed]
2 . Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Diesmann M, Morrison A, Goodman PH, Harris FC, Zirpe M, Natschläger T, Pecevski D, Ermentrout B, Djurfeldt M, Lansner A, Rochel O, Vieville T, Muller E, Davison AP, El Boustani S, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349-98 [PubMed]
Citations  Citation Browser
Model Information (Click on a link to find other models with that property)
Model Type: Realistic Network;
Brain Region(s)/Organism:
Cell Type(s): Abstract integrate-and-fire leaky neuron;
Channel(s):
Gap Junctions:
Receptor(s):
Gene(s):
Transmitter(s):
Simulation Environment: NEURON; GENESIS; NEST; C or C++ program; XPP; CSIM; NCS; SPLIT; MVASpike; SciLab; Brian; PyNN; Python;
Model Concept(s): Activity Patterns; Methods;
Implementer(s): Carnevale, Ted [Ted.Carnevale at Yale.edu]; Hines, Michael [Michael.Hines at Yale.edu]; Davison, Andrew [Andrew.Davison at iaf.cnrs-gif.fr]; Destexhe, Alain [Destexhe at iaf.cnrs-gif.fr]; Ermentrout, Bard [bard_at_pitt.edu]; Brette R; Bower, James; Beeman, Dave; Diesmann M; Morrison A ; Goodman PH; Harris Jr, FC; Zirpe M ; Natschlager T ; Pecevski D ; Djurfeldt M; Lansner, Anders [ala at kth.se]; Rochel O ; Vieville T ; Muller E ; El Boustani, Sami [elboustani at unic.cnrs-gif.fr]; Rudolph M ;
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destexhe_benchmarks
Brian
README.txt
COBA.py
COBAHH.py
CUBA.py
                            
# coding: latin-1
"""
This is a Brian script implementing a benchmark described
in the following review paper:

Simulation of networks of spiking neurons: A review of tools and strategies (2007).
Brette, Rudolph, Carnevale, Hines, Beeman, Bower, Diesmann, Goodman, Harris, Zirpe,
Natschläger, Pecevski, Ermentrout, Djurfeldt, Lansner, Rochel, Vibert, Alvarez, Muller,
Davison, El Boustani and Destexhe.
Journal of Computational Neuroscience 23(3):349-98

Benchmark 3: random network of HH neurons with exponential synaptic conductances

Clock-driven implementation with exponential Euler integration
(no spike time interpolation)

R. Brette - Dec 2007
--------------------------------------------------------------------------------------
Brian is a simulator for spiking neural networks written in Python, developed by
R. Brette and D. Goodman.
http://brian.di.ens.fr
"""

from brian import *
import time

# Parameters
area=20000*umetre**2
Cm=(1*ufarad*cm**-2)*area
gl=(5e-5*siemens*cm**-2)*area
El=-60*mV
EK=-90*mV
ENa=50*mV
g_na=(100*msiemens*cm**-2)*area
g_kd=(30*msiemens*cm**-2)*area
VT=-63*mV
# Time constants
taue=5*ms
taui=10*ms
# Reversal potentials
Ee=0*mV
Ei=-80*mV
we=6*nS # excitatory synaptic weight (voltage)
wi=67*nS # inhibitory synaptic weight

start_time=time.time()
# The model
eqs=Equations('''
dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)-g_na*(m*m*m)*h*(v-ENa)-g_kd*(n*n*n*n)*(v-EK))/Cm : volt 
dm/dt = alpham*(1-m)-betam*m : 1
dn/dt = alphan*(1-n)-betan*n : 1
dh/dt = alphah*(1-h)-betah*h : 1
dge/dt = -ge*(1./taue) : siemens
dgi/dt = -gi*(1./taui) : siemens
alpham = 0.32*(mV**-1)*(13*mV-v+VT)/(exp((13*mV-v+VT)/(4*mV))-1.)/ms : Hz
betam = 0.28*(mV**-1)*(v-VT-40*mV)/(exp((v-VT-40*mV)/(5*mV))-1)/ms : Hz
alphah = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz
betah = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz
alphan = 0.032*(mV**-1)*(15*mV-v+VT)/(exp((15*mV-v+VT)/(5*mV))-1.)/ms : Hz
betan = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz
''')

P=NeuronGroup(4000,model=eqs,\
              threshold=EmpiricalThreshold(threshold=-20*mV,refractory=3*ms),\
              implicit=True,freeze=True,compile=False)
Pe=P.subgroup(3200)
Pi=P.subgroup(800)
Ce=Connection(Pe,P,'ge')
Ci=Connection(Pi,P,'gi')
Ce.connect_random(Pe, P, 0.02,weight=we)
Ci.connect_random(Pi, P, 0.02,weight=wi)
# Initialization
P.v=El+(randn(len(P))*5-5)*mV
P.ge=(randn(len(P))*1.5+4)*10.*nS
P.gi=(randn(len(P))*12+20)*10.*nS

# Record the number of spikes and a few traces
Me=PopulationSpikeCounter(Pe)
Mi=PopulationSpikeCounter(Pi)
trace=StateMonitor(P,'v',record=[1,10,100])

print "Network construction time:",time.time()-start_time,"seconds"
print "Simulation running..."
run(1*msecond)
start_time=time.time()

run(1000*msecond)
duration=time.time()-start_time
print "Simulation time:",duration,"seconds"
print Me.nspikes,"excitatory spikes"
print Mi.nspikes,"inhibitory spikes"

plot(trace.times/ms,trace[1]/mV)
plot(trace.times/ms,trace[10]/mV)
plot(trace.times/ms,trace[100]/mV)
show()