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:2011:10.3389/fnins.2011.00009,
author = {Rossant, C and Goodman, DFM and Fontaine, B and Platkiewicz, J and Magnusson, AK and Brette, R},
doi = {10.3389/fnins.2011.00009},
journal = {Front Neurosci},
title = {Fitting neuron models to spike trains.},
url = {http://dx.doi.org/10.3389/fnins.2011.00009},
volume = {5},
year = {2011}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Computational modeling is increasingly used to understand the function of neural circuits in systems neuroscience. These studies require models of individual neurons with realistic input-output properties. Recently, it was found that spiking models can accurately predict the precisely timed spike trains produced by cortical neurons in response to somatically injected currents, if properly fitted. This requires fitting techniques that are efficient and flexible enough to easily test different candidate models. We present a generic solution, based on the Brian simulator (a neural network simulator in Python), which allows the user to define and fit arbitrary neuron models to electrophysiological recordings. It relies on vectorization and parallel computing techniques to achieve efficiency. We demonstrate its use on neural recordings in the barrel cortex and in the auditory brainstem, and confirm that simple adaptive spiking models can accurately predict the response of cortical neurons. Finally, we show how a complex multicompartmental model can be reduced to a simple effective spiking model.
AU - Rossant,C
AU - Goodman,DFM
AU - Fontaine,B
AU - Platkiewicz,J
AU - Magnusson,AK
AU - Brette,R
DO - 10.3389/fnins.2011.00009
PY - 2011///
TI - Fitting neuron models to spike trains.
T2 - Front Neurosci
UR - http://dx.doi.org/10.3389/fnins.2011.00009
UR - https://www.ncbi.nlm.nih.gov/pubmed/21415925
UR - http://hdl.handle.net/10044/1/40626
VL - 5
ER -