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• W1844320817 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." @default.
• W1844320817 created "2016-06-24" @default.
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• W1844320817 date "2010-01-01" @default.
• W1844320817 modified "2023-10-17" @default.
• W1844320817 title "Designing Simple Nonlinear Filters Using Hysteresis of Single Recurrent Neurons for Acoustic Signal Recognition in Robots" @default.
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• W1844320817 doi "https://doi.org/10.1007/978-3-642-15819-3_50" @default.
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