An efficient training algorithm for dynamic synapse neural networks using trust region methods.
We formulate the dynamic synapse neural network from the averaged activity of local population of neurons perspective. We have applied the trust region nonlinear optimization approach to train the network and show the new learning method effectiveness in comparison to the genetic algorithms by optimizing large-scale networks.
Namarvar
Hassan H
HH
Department of Biomedical Engineering, University of Southern California, OHE-500, Los Angeles, CA 90089-1451, USA. heidarin@usc.edu
Berger
Theodore W
TW
D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D016571:Neural Networks, Computer; D013569:Synapses / Q000502:physiology