by Romain Brette, Dan F. M. Goodman
Abstract:
High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.
Reference:
Romain Brette, Dan F. M. Goodman, 2011.Vectorized algorithms for spiking neural network simulation, Neural computation, volume 23.
Bibtex Entry:
@article{Brette2011,
abstract = {High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.},
author = {Brette, Romain
and Goodman, Dan F. M.},
day = {11},
doi = {10.1162/NECO_a_00123},
issn = {1530-888X},
journal = {Neural computation},
keyword = {Synapses},
language = {eng},
month = {Jun},
number = {6},
pages = {1503--1535},
pmid = {21395437},
title = {Vectorized algorithms for spiking neural network simulation.},
volume = {23},
year = {2011}
}