Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Spiking neural network training using evolutionary algorithms
AU - Pavlidis, Nicos
AU - Tasoulis, DK
AU - Plagianakos, Vassilis P.
AU - Vrahatis, Michael N.
AU - Nikiforidis, G.
PY - 2005
Y1 - 2005
N2 - Networks of spiking neurons can perform complex non-linear computations in fast temporal coding just as well as rate coded networks. These networks differ from previous models in that spiking neurons communicate information by the timing, rather than the rate, of spikes. To apply spiking neural networks on particular tasks, a learning process is required. Most existing training algorithms are based on unsupervised Hebbian learning. In this paper, we investigate the performance of the parallel differential evolution algorithm, as a supervised training algorithm for spiking neural networks. The approach was successfully tested on well-known and widely used classification problems.
AB - Networks of spiking neurons can perform complex non-linear computations in fast temporal coding just as well as rate coded networks. These networks differ from previous models in that spiking neurons communicate information by the timing, rather than the rate, of spikes. To apply spiking neural networks on particular tasks, a learning process is required. Most existing training algorithms are based on unsupervised Hebbian learning. In this paper, we investigate the performance of the parallel differential evolution algorithm, as a supervised training algorithm for spiking neural networks. The approach was successfully tested on well-known and widely used classification problems.
U2 - 10.1109/IJCNN.2005.1556240
DO - 10.1109/IJCNN.2005.1556240
M3 - Conference contribution/Paper
SN - 0-7803-9048-2
VL - 4
SP - 2190
EP - 2194
BT - International Joint Conference on Neural Networks (IJCNN 2005)
PB - IEEE
ER -