Autores
Espinosa Ramos Josafath Israel
Cruz Cortés Nareli
Título Spiking neuron model approximation using GEP
Tipo Congreso
Sub-tipo SCOPUS
Descripción 2013 IEEE Congress on Evolutionary Computation
Resumen 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. © 2013 IEEE.
Observaciones CEC 2013; Category numberCFP13ICE-ART; Code 98485
Lugar Cancún
País Mexico
No. de páginas 3260-3267
Vol. / Cap. Article number 6557969
Inicio 2013-06-20
Fin 2013-06-23
ISBN/ISSN 978-147990454-9