Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data.
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.
School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.
Eoin P
EP
Lynch
Department of Computer Science, University of Bristol Bristol, UK.
Conor J
CJ
Houghton
auditory neurons; evolutionary algorithms; parameter estimation; spike train metrics; spiking neurons