TY - GEN
T1 - Spiking neuron model approximation using GEP
AU - Espinosa-Ramos, Josafath I.
AU - Cortes, Nareli Cruz
AU - Vazquez, Roberto A.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881602040&partnerID=8YFLogxK
U2 - 10.1109/CEC.2013.6557969
DO - 10.1109/CEC.2013.6557969
M3 - Contribución a la conferencia
AN - SCOPUS:84881602040
SN - 9781479904549
T3 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
SP - 3260
EP - 3267
BT - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
T2 - 2013 IEEE Congress on Evolutionary Computation, CEC 2013
Y2 - 20 June 2013 through 23 June 2013
ER -