Spiking Neuron Model approximation using GEP

Spiking Neuron Models can accurately predict the spike trains produced by cortical neurons in response to somatically injected electric currents. Since the specific model characteristics depend on the neuron; a computational method is required to fit models to electrophysiological recordings. However, models only work within defined limits and it is possible that they could only be applied to the example presented. Moreover, there is not a methodology to fit the models; in fact, the fitting procedure can be very time consuming both in terms of computer simulations and code writing. In this paper a first effort is presented not to fit models, but to create a methodology to generate neuron models automatically. We propose to use Gene Expression Programming to create mathematical expressions that replicate the behavior of a state of the art neuron model. We will present how this strategy is feasible to solve more complex problems and provide the basis to find new models which could be applied in a wide range of areas from the field of computational neurosciences as pyramidal neurons spike train prediction, or in artificial intelligence as pattern recognition problems.