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Neural Comput. 2011 Jun;23(6):1503-35. doi: 10.1162/NECO_a_00123. Epub 2011 Mar 11.

Vectorized algorithms for spiking neural network simulation.

Author information

1
Laboratoire Psychologie de la Perception, CNRS and Université Paris Descartes, Paris 75006, France, and Département d'Etudes Cognitives, Ecole Normale Supérieure, Paris Cedex 05, 75230 France. romain.brette@ens.fr

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.

PMID:
21395437
DOI:
10.1162/NECO_a_00123
[Indexed for MEDLINE]

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