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{Rossant:2010:10.3389/neuro.11.002.2010,
author = {Rossant, C and Goodman, DFM and Platkiewicz, J and Brette, R},
doi = {10.3389/neuro.11.002.2010},
journal = {Front Neuroinform},
title = {Automatic fitting of spiking neuron models to electrophysiological recordings.},
url = {http://dx.doi.org/10.3389/neuro.11.002.2010},
volume = {4},
year = {2010}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Spiking models can accurately predict the spike trains produced by cortical neurons in response to somatically injected currents. Since the specific characteristics of the model depend on the neuron, a computational method is required to fit models to electrophysiological recordings. The fitting procedure can be very time consuming both in terms of computer simulations and in terms of code writing. We present algorithms to fit spiking models to electrophysiological data (time-varying input and spike trains) that can run in parallel on graphics processing units (GPUs). The model fitting library is interfaced with Brian, a neural network simulator in Python. If a GPU is present it uses just-in-time compilation to translate model equations into optimized code. Arbitrary models can then be defined at script level and run on the graphics card. This tool can be used to obtain empirically validated spiking models of neurons in various systems. We demonstrate its use on public data from the INCF Quantitative Single-Neuron Modeling 2009 competition by comparing the performance of a number of neuron spiking models.
AU - Rossant,C
AU - Goodman,DFM
AU - Platkiewicz,J
AU - Brette,R
DO - 10.3389/neuro.11.002.2010
PY - 2010///
TI - Automatic fitting of spiking neuron models to electrophysiological recordings.
T2 - Front Neuroinform
UR - http://dx.doi.org/10.3389/neuro.11.002.2010
UR - https://www.ncbi.nlm.nih.gov/pubmed/20224819
UR - http://hdl.handle.net/10044/1/40619
VL - 4
ER -