Learning reconstruction and prediction of natural stimuli by a population of spiking neurons

M. Gutmann, A. Hyvärinen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a model for learning representations of time dependent data with a population of spiking neurons. Encoding is based on a standard spiking neuron model, and the spike timings of the neurons represent the stimulus. Learning is based on the sole principle of maximization of representation accuracy: the stimulus can be decoded from the spike timings with minimum error. Since the encoding is causal, we propose two different representation strategies: The spike timings represent the stimulus either in a predictive manner or by reconstructing past input. We apply the model to speech data and discuss differences between
the emergent representations.
Original languageEnglish
Title of host publication17th European Symposium on Artificial Neural Networks (ESANN)
EditorsM. Verleysen
Publisherd-side publications
Pages409-414
Number of pages6
Publication statusPublished - 2009

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