GeNN can simulate models written for the Brian simulator via the Brian2GeNN interface [6] . The easiest way to install everything needed is to install the Anaconda or Miniconda Python distribution and then follow the instructions to install Brian2GeNN with the conda package manager. When Brian2GeNN is installed in this way, it comes with a bundled version of GeNN and no further configuration is required. In all other cases (e.g. an installation from source), the path to GeNN and the CUDA libraries has to be configured via the GENN_PATH and CUDA_PATH environment variables as described in Installation or via the devices.genn.path and devices.genn.cuda_path Brian preferences.

To use GeNN to simulate a Brian script, import the brian2genn package and switch Brian to the genn device. As an example, the following Python script will simulate Leaky-integrate-and-fire neurons with varying input currents to construct an f/I curve:

from brian2 import *
import brian2genn
set_device('genn')
n = 1000
duration = 1*second
tau = 10*ms
eqs = '''
dv/dt = (v0 - v) / tau : volt (unless refractory)
v0 : volt
'''
group = NeuronGroup(n, eqs, threshold='v > 10*mV', reset='v = 0*mV',
                    refractory=5*ms, method='exact')
group.v = 0*mV
group.v0 = '20*mV * i / (n-1)'
monitor = SpikeMonitor(group)
run(duration)

Of course, your simulation should be more complex than the example above to actually benefit from the performance gains of using a GPU via GeNN.

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