Designing simple nonlinear filters using hysteresis of single recurrent neurons for acoustic signal recognition in robots

Authors:
Poramate Manoonpong;Frank Pasemann;Christoph Kolodziejski;Florentin Wörgötter
Affiliations:
Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany;Institute of Cognitive Science, University of Osnabrück, Osnabrück, Germany;Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany;Bernstein Center for Computational Neuroscience, University of Göttingen, Göttingen, Germany
Venue:
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part I
Year:
2010

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Abstract

In this article we exploit the discrete-time dynamics of a single neuron with self-connection to systematically design simple signal filters. Due to hysteresis effects and transient dynamics, this single neuron behaves as an adjustable low-pass filter for specific parameter configurations. Extending this neuro-module by two more recurrent neurons leads to versatile high- and band-pass filters. The approach presented here helps to understand how the dynamical properties of recurrent neural networks can be used for filter design. Furthermore, it gives guidance to a new way of implementing sensory preprocessing for acoustic signal recognition in autonomous robots.