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 modelling 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 modelling two experimental data sets with spiking neurons; in-vitro current injection responses from a regular spiking pyramidal neuron are modelled using spikingneurons and in-vivo extracellular auditory data is modelled using a two stage model consistingof a stimulus filter and spiking neuron model. We develop techniques for applying the spike train metric to neuron model optimisation and demonstrate this is an effective technique for thispurpose.