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Romain Brette & Dan Goodman Ecole Normale Supérieure Projet Odyssée http://brian.di.ens.fr brette@di.ens.fr goodman@di.ens.fr

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Spirit of the project easy and flexible model coding for every day use Example: minimalexample

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from brian import * eqs = ””” dv/dt = (ge+gi-(v+49*mV))/(20*ms) dge/dt = -ge/(5*ms) dgi/dt = -gi/(10*ms)””” P = NeuronGroup(4000,model=eqs,threshold=-50*mV,reset=-60*mV) P.v=-60*mV Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge') Ci = Connection(Pi, P, 'gi') Ce.connectRandom(Pe, P, 0.02, weight=1.62*mV) Ci.connectRandom(Pi, P, 0.02, weight=-9*mV) M = SpikeMonitor(P) run(1*second) rasterPlot(M) show() Brian in action PiPe Ce Ci P

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eqs = ””” dv/dt = (ge+gi-(v+49*mV))/(20*ms) dge/dt = -ge/(5*ms) dgi/dt = -gi/(10*ms)””” P = NeuronGroup(4000,model=eqs,threshold=-50*mV,reset=-60*mV) How it works Synchronous operations are implemented as vector operations (uses Scipy) Cost of each vector-based operation = O(N) Cost of interpretation = O(1) = negligible for large networks Neuron groups State matrix SUpdate matrix A v ge gi

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How it is efficient Vector-based computation – uses Scipy/Numpy Ex. on minimalexample

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