Abstract
Computational neuroscience has produced a diversity of
software for simulations of networks of spiking neurons, with
both negative and positive consequences. On the one hand, each
simulator uses its own programming or configuration language,
leading to considerable difficulty in porting models from one
simulator to another. This impedes communication between
investigators and makes it harder to reproduce and build on
the work of others. On the other hand, simulation results can
be cross-checked between different simulators, giving greater
confidence in their correctness, and each simulator has
different optimizations, so the most appropriate simulator can
be chosen for a given modelling task. A common programming
interface to multiple simulators would reduce or eliminate the
problems of simulator diversity while retaining the
benefits. PyNN is such an interface, making it possible to
write a simulation script once, using the Python programming
language, and run it without modification on any supported
simulator (currently NEURON, NEST, PCSIM, Brian and the
Heidelberg VLSI neuromorphic hardware). PyNN increases the
productivity of neuronal network modelling by providing
high-level abstraction, by promoting code sharing and reuse,
and by providing a foundation for simulator-agnostic analysis,
visualization and data-management tools. PyNN increases the
reliability of modelling studies by making it much easier to
check results on multiple simulators. PyNN is open-source
software and is available from
http://neuralensemble.org/PyNN .
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