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Front Neuroinform. 2015 Apr 20;9:10. doi: 10.3389/fninf.2015.00010. eCollection 2015.

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

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School of Mathematics, Trinity College Dublin Dublin, Ireland ; Department of Computer Science, University of Bristol Bristol, UK.
Department of Computer Science, University of Bristol Bristol, UK.


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.


auditory neurons; evolutionary algorithms; parameter estimation; spike train metrics; spiking neurons

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