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

ABSTRACT: 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.

SUBMITTER: Lynch EP

PROVIDER: S-EPMC4403314 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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

Lynch Eoin P EP Houghton Conor J CJ

Frontiers in neuroinformatics 20150420

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 discove ...[more]

PMID: 25941485

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