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. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Manoonpong, P., Pasemann, F., Kolodziejski, C., & Wörgötter, F. (2010). Designing simple nonlinear filters using hysteresis of single recurrent neurons for acoustic signal recognition in robots. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6352 LNCS, pp. 374–383). https://doi.org/10.1007/978-3-642-15819-3_50
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