1 / 5

Romain Brette & Dan Goodman Ecole Normale Supérieure Projet Odyssée

http://brian.di.ens.fr. Romain Brette & Dan Goodman Ecole Normale Supérieure Projet Odyssée. brette@di.ens.fr goodman@di.ens.fr. Spirit of the project. easy and flexible model coding for every day use Example: minimalexample. Brian in action. from brian import * eqs = ”””

ivory
Download Presentation

Romain Brette & Dan Goodman Ecole Normale Supérieure Projet Odyssée

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

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

  3. Brian in action frombrian 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() Ci P Pi Pe Ce

  4. 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 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) Neuron groups Update matrix A State matrix S v ge gi

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

More Related