A modified one-layer spiking neural network involves derivative of the state function at firing time

Authors:
Wenyu Yang;Jie Yang;Wei Wu
Affiliations:
School of Mathematical Sciences, Dalian University of Technology, Dalian, China;School of Mathematical Sciences, Dalian University of Technology, Dalian, China;School of Mathematical Sciences, Dalian University of Technology, Dalian, China
Venue:
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part I
Year:
2012

Citing 7
Cited 0

Networks of spiking neurons: the third generation of neural network models

Transactions of the Society for Computer Simulation International - Special issue: simulation methodology in transportation systems
Topos: Spiking neural networks for temporal pattern recognition in complex real sounds

Neurocomputing
Improved spiking neural networks for EEG classification and epilepsy and seizure detection

Integrated Computer-Aided Engineering
Fast and adaptive network of spiking neurons for multi-view visual pattern recognition

Neurocomputing
A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection

Neural Networks
A gradient descent rule for spiking neurons emitting multiple spikes

Information Processing Letters - Special issue on applications of spiking neural networks
Evolving spiking neural networks for audiovisual information processing

Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Usual spiking neural network with a hidden layer whose input and output are all spike times is very powerful for performing classification on real-world data. In this paper, we investigate the performance of a modified one-layer spiking neural network that involves both the spike time and derivative of the state function at firing time. It is shown by numerical experiments that a modified one-layer spiking neural network using same or fewer encoding neurons is almost as good as a usual spiking neural network with a hidden layer for solving some benchmark problems.