|Developer(s)||Romain Brette, Dan Goodman|
|Stable release||1.4.1 / April 30, 2013|
|Type||Neural network software|
Brian is aimed at researchers developing models based on networks of spiking neurons. The design is aimed at minimizing users' development time, with execution speed a secondary goal. Users specify neuron models by giving their differential equations in standard mathematical form, create groups of neurons and connect them via synapses. The intent is to make the process as flexible as possible, so that researchers are not restricted to using neuron models already built into the simulator. The entire simulator is written in Python, using the NumPy and SciPy numerical and scientific computing packages. Parts of the simulator can optionally be run using C code generated on the fly. Computationally, Brian uses vectorization techniques, so that for large numbers of neurons, execution speed is of the same order of magnitude as C code.
The following code defines, runs and plots a randomly connected network of leaky integrate and fire neurons with exponential inhibitory and excitatory currents.
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()
Comparison to other simulators
- Goodman and Brette 2009
- Goodman, D. and Brette, R. (2008). "Brian: a simulator for spiking neural networks in Python", Front. Neuroinform. 2:5. doi:10.3389/neuro.11.005.2008 PMID 19115011
- Goodman, D.F.M. and Brette, R. (2009). "The Brian simulator", Front. Neurosci. doi:10.3389/neuro.01.026.2009
Lua error in package.lua at line 80: module 'Module:Buffer' not found.