TY - GEN
T1 - Creation of spiking neuron models applied in pattern recognition problems
AU - Espinosa-Ramos, Josafath I.
AU - Cruz-Cortes, Nareli
AU - Vazquez, Roberto A.
PY - 2013
Y1 - 2013
N2 - Some spiking neuron models have proved to solve different linear and non-linear pattern recognition problems. Indeed, only one spiking neuron can generate comparable results as classical artificial neural network. However, depending on the classification problem, one spiking model could be better or less efficient than other. In this paper we propose a methodology to create spiking neuron models using Gene Expression Programming. The new models created are applied in eight pattern recognition problems. The results obtained are compared with previous results generated adopting the Izhikevich spiking neuron model. This first effort will help us to generate spiking neuron models which will be adaptable to a specific pattern recognition problem.
AB - Some spiking neuron models have proved to solve different linear and non-linear pattern recognition problems. Indeed, only one spiking neuron can generate comparable results as classical artificial neural network. However, depending on the classification problem, one spiking model could be better or less efficient than other. In this paper we propose a methodology to create spiking neuron models using Gene Expression Programming. The new models created are applied in eight pattern recognition problems. The results obtained are compared with previous results generated adopting the Izhikevich spiking neuron model. This first effort will help us to generate spiking neuron models which will be adaptable to a specific pattern recognition problem.
UR - http://www.scopus.com/inward/record.url?scp=84893531623&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2013.6706795
DO - 10.1109/IJCNN.2013.6706795
M3 - Contribución a la conferencia
AN - SCOPUS:84893531623
SN - 9781467361293
T3 - Proceedings of the International Joint Conference on Neural Networks
BT - 2013 International Joint Conference on Neural Networks, IJCNN 2013
T2 - 2013 International Joint Conference on Neural Networks, IJCNN 2013
Y2 - 4 August 2013 through 9 August 2013
ER -