* News: A testable alpha version of Brian 2.0 has been released *
Brian is a simulator for spiking neural networks available on almost all platforms. The motivation for this project is that a simulator should not only save the time of processors, but also the time of scientists.
Brian is easy to learn and use, highly flexible and easily extensible. The Brian package itself and simulations using it are all written in the Python programming language, which is an easy, concise and highly developed language with many advanced features and development tools, excellent documentation and a large community of users providing support and extension packages.
The following code defines a randomly connected network of integrate and fire neurons with exponential inhibitory and excitatory currents, runs the simulation and makes the raster plot on the right.
from brian import * eqs = ''' dv/dt = (ge+gi-(v+49*mV))/(20*ms) : volt dge/dt = -ge/(5*ms) : volt dgi/dt = -gi/(10*ms) : volt ''' P = NeuronGroup(4000, eqs, threshold=-50*mV, reset=-60*mV) P.v = -60*mV Pe = P.subgroup(3200) Pi = P.subgroup(800) Ce = Connection(Pe, P, 'ge', weight=1.62*mV, sparseness=0.02) Ci = Connection(Pi, P, 'gi', weight=-9*mV, sparseness=0.02) M = SpikeMonitor(P) run(1*second) raster_plot(M) show()
The efficiency of Brian relies on vectorised computations (using NumPy), so that the code above is only about 25% slower than C.
How to cite Brian: if you use Brian for your published research, we suggest that you cite one of our introductory articles: (1) Goodman DF and Brette R (2009). The Brian simulator. Front Neurosci doi:10.3389/neuro.01.026.2009; or (2) Stimberg M, Goodman DFM, Benichoux V, Brette R (2014). Equation-oriented specification of neural models for simulations. Frontiers Neuroinf, doi: 10.3389/fninf.2014.00006. You can also download our logo for posters and presentations.