%A Lynch,Eoin P.
%A Houghton,Conor J.
%D 2015
%J Frontiers in Neuroinformatics
%C
%F
%G English
%K parameter estimation,spiking neurons,evolutionary algorithms,spike train metrics,auditory neurons
%Q
%R 10.3389/fninf.2015.00010
%W
%L
%N 10
%M
%P
%7
%8 2015-April-20
%9 Original Research
%+ Mr Eoin P. Lynch,School of Mathematics, Trinity College Dublin,Dublin, Ireland,eplynch@maths.tcd.ie
%+ Mr Eoin P. Lynch,Department of Computer Science, University of Bristol,Bristol, UK,eplynch@maths.tcd.ie
%#
%! Parameter estimation of neuron models.
%*
%<
%T Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data
%U https://www.frontiersin.org/article/10.3389/fninf.2015.00010
%V 9
%0 JOURNAL ARTICLE
%@ 1662-5196
%X Spiking neuron models can accurately predict the response of neurons to somatically injected currents if the model parameters are carefully tuned. Predicting the response of in-vivo neurons responding to natural stimuli presents a far more challenging modeling problem. In this study, an algorithm is presented for parameter estimation of spiking neuron models. The algorithm is a hybrid evolutionary algorithm which uses a spike train metric as a fitness function. We apply this to parameter discovery in modeling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and in-vivo extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.