AUTHOR=Lynch Eoin P. , Houghton Conor J. 

TITLE=Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data

JOURNAL=Frontiers in Neuroinformatics

VOLUME=9

YEAR=2015

URL=https://www.frontiersin.org/journals/neuroinformatics/articles/10.3389/fninf.2015.00010

DOI=10.3389/fninf.2015.00010

ISSN=1662-5196

ABSTRACT=<p>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 <italic>in-vivo</italic> 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; <italic>in-vitro</italic> current injection responses from a regular spiking pyramidal neuron are modeled using spiking neurons and <italic>in-vivo</italic> extracellular auditory data is modeled using a two stage model consisting of a stimulus filter and spiking neuron model.</p>