Imperial College London

Dr Dan Goodman

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Lecturer
 
 
 
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Contact

 

+44 (0)20 7594 6264d.goodman Website

 
 
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Location

 

1001Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Fontaine:2011:10.3389/fninf.2011.00009,
author = {Fontaine, B and Goodman, DFM and Benichoux, V and Brette, R},
doi = {10.3389/fninf.2011.00009},
journal = {Front Neuroinform},
title = {Brian hears: online auditory processing using vectorization over channels.},
url = {http://dx.doi.org/10.3389/fninf.2011.00009},
volume = {5},
year = {2011}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The human cochlea includes about 3000 inner hair cells which filter sounds at frequencies between 20 Hz and 20 kHz. This massively parallel frequency analysis is reflected in models of auditory processing, which are often based on banks of filters. However, existing implementations do not exploit this parallelism. Here we propose algorithms to simulate these models by vectorizing computation over frequency channels, which are implemented in "Brian Hears," a library for the spiking neural network simulator package "Brian." This approach allows us to use high-level programming languages such as Python, because with vectorized operations, the computational cost of interpretation represents a small fraction of the total cost. This makes it possible to define and simulate complex models in a simple way, while all previous implementations were model-specific. In addition, we show that these algorithms can be naturally parallelized using graphics processing units, yielding substantial speed improvements. We demonstrate these algorithms with several state-of-the-art cochlear models, and show that they compare favorably with existing, less flexible, implementations.
AU - Fontaine,B
AU - Goodman,DFM
AU - Benichoux,V
AU - Brette,R
DO - 10.3389/fninf.2011.00009
PY - 2011///
TI - Brian hears: online auditory processing using vectorization over channels.
T2 - Front Neuroinform
UR - http://dx.doi.org/10.3389/fninf.2011.00009
UR - https://www.ncbi.nlm.nih.gov/pubmed/21811453
UR - http://hdl.handle.net/10044/1/40617
VL - 5
ER -