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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.

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