Fast sigmoidal networks via spiking neurons
Neural Computation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Image Processing Using Pulse-Coupled Neural Networks
Image Processing Using Pulse-Coupled Neural Networks
Pulsed Neural Networks
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Advances in Neural Information Processing Systems 5, [NIPS Conference]
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ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Incremental Learning with a Stopping Criterion - Experimental Results
IWANN '96 Proceedings of the International Workshop on Artificial Neural Networks: From Natural to Artificial Neural Computation
Guest Editorial Overview Of Pulse Coupled Neural Network (PCNN) Special Issue
IEEE Transactions on Neural Networks
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We present a model of spiking neuron that emulates the output of the usual static neurons with sigimodial activation functions. It allows for hardware implementations of standard feedforward networks, trained off-line with any classical learning algorithm (i.e. back-propagation and its variants). The model is validated on hand-written digits recognition, and image classification tasks. A digital architecture is proposed and evaluated. The area needed for implementing the spiking neuron on a chip is 10 times smaller than that for the corresponding static neuron. The accuracy of the network's output increases with time, and reaches that of the emulated static neural network after an adequate integration period. Single errors in the spike trains, or interruption of the relaxation process, due for example to irradiation in harsh environments, are harmless.