A bio-inspired auditory perception model for amplitude-frequency clustering

Paolo Arena; Luigi Fortuna; Mattia Frasca; Gaetana Ganci; Luca Patane

doi:10.1117/12.607862

29 June 2005 A bio-inspired auditory perception model for amplitude-frequency clustering (keynote Paper)

Paolo Arena, Luigi Fortuna, Mattia Frasca, Gaetana Ganci, Luca Patane

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Proceedings Volume 5839, Bioengineered and Bioinspired Systems II; (2005) https://doi.org/10.1117/12.607862
Event: Microtechnologies for the New Millennium 2005, 2005, Sevilla, Spain

Abstract

In this paper a model for auditory perception is introduced. This model is based on a network of integrate-and-fire and resonate-and-fire neurons and is aimed to control the phonotaxis behavior of a roving robot. The starting point is the model of phonotaxis in Gryllus Bimaculatus: the model consists of four integrate-and-fire neurons and is able of discriminating the calling song of male cricket and orienting the robot towards the sound source. This paper aims to extend the model to include an amplitude-frequency clustering. The proposed spiking network shows different behaviors associated with different characteristics of the input signals (amplitude and frequency). The behavior implemented on the robot is similar to the cricket behavior, where some frequencies are associated with the calling song of male crickets, while other ones indicate the presence of predators. Therefore, the whole model for auditory perception is devoted to control different responses (attractive or repulsive) depending on the input characteristics. The performance of the control system has been evaluated with several experiments carried out on a roving robot.

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Paolo Arena, Luigi Fortuna, Mattia Frasca, Gaetana Ganci, and Luca Patane "A bio-inspired auditory perception model for amplitude-frequency clustering (keynote Paper)", Proc. SPIE 5839, Bioengineered and Bioinspired Systems II, (29 June 2005); https://doi.org/10.1117/12.607862

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KEYWORDS

Neurons

Radon

Sensors

Biomimetics

Signal processing

Systems modeling

Control systems

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