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} }