Title :
Supervised learning with spiking neural networks
Author :
Xin, Jianguo ; Embrechts, Mark J.
Author_Institution :
Rensselaer Polytech. Inst., Troy, NY, USA
Abstract :
We derive a supervised learning algorithm for a spiking neural network which encodes information in the timing of spike trains. This algorithm is similar to the classical error backpropagation algorithm for sigmoidal neural network but the learning parameter is adaptively changed. The algorithm is applied to a complex nonlinear classification problem and the results show that the spiking neural network is capable of performing nonlinearly separable classification tasks. Several issues concerning the spiking neural network are discussed
Keywords :
adaptive systems; backpropagation; feedforward neural nets; neural net architecture; pattern classification; adaptive learning; error backpropagation; network architecture; nonlinear classification; spike train timing; spiking neural network; supervised learning; Aerospace engineering; Biomembranes; Delay effects; Feedforward neural networks; Feedforward systems; Mechanical engineering; Neural networks; Neurons; Supervised learning; Systems engineering and theory;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.938430
