Cable theory for dendritic neurons
Methods in neuronal modeling
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Audio-Visual Speech Recognition One Pass Learning with Spiking Neurons
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A Spatio-temporal Perceptron for On-Line Handwritten Character Recognition
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Spatio-Temporal organization map: a speech recognition application
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Comparison between two spatio-temporal organization maps for speech recognition
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
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The technique presented in this article comes from the formalization of some passive electric properties of the dendritic trees in biological neurons using a complex-valued spatio-temporal coding. The introduction of this coding in the complex-valued neural networks makes it possible to give an algebraic formalization to the spiking neural networks. Our finality in term of applications is the processing of spatio-temporal patterns in the field of human-computer interactions. This technique was thus evaluated by simulation on handwritten character recognition and, audio and visual speech recognition problems. To improve the performances of it we present in this paper a design methodology which helps building multinetwork spiking machines. The implementation of a pen oriented interface made for an industrialist illustrates the method.