Parameter estimation of neuron models using in-vitro and in-vivo electrophysiological data - Lynch, Eoin P.; Houghton, Conor J. (Frontiers in Neuroinformatics, 2015)
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 spiking neurons and in-vivo extracellular auditory data is modelled using a two stage model consisting of 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 this purpose.