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Compact Early Vision Signal Analyzers in Neuromorphic Technology


Baruzzi, Valentina; Indiveri, Giacomo; Sabatini, Silvio (2020). Compact Early Vision Signal Analyzers in Neuromorphic Technology. In: 15th International Conference on Computer Vision Theory and Applications, Valletta, Malta, 27 February 2020 - 29 February 2020.

Abstract

Reproducing the dynamics of biological neural systems using mixed signal analog/digital neuromorphic circuits makes these systems ideal platforms to implement low-power bio-inspired devices for a wide range of application domains. Despite these principled assets, neuromorphic system design has to cope with the limited resources presently available on hardware. Here, different spiking networks were designed, tested in simulation, and implemented on the neuromorphic processor DYNAP-SE, to obtain silicon neurons that are tuned to visual stimuli oriented at specific angles and with specific spatial frequencies, provided by the event camera DVS. Recurrent clustered inhibition was successfully tested on spiking neural networks, both in simulation and on the DYNAP-SE board, to obtain neurons with highly structured Gabor-like receptive fields (RFs); these neurons are characterized by tuning curves that are sharper or at least comparable to the ones obtained using equivalent feed-forward schem es, but require a significantly lower number of synapses. The resulting harmonic signal description provided by the proposed neuromorphic circuit could be potentially used for a complete characterization of the 2D local structure of the visual signal in terms of phase relationships from all the available oriented channels.

Abstract

Reproducing the dynamics of biological neural systems using mixed signal analog/digital neuromorphic circuits makes these systems ideal platforms to implement low-power bio-inspired devices for a wide range of application domains. Despite these principled assets, neuromorphic system design has to cope with the limited resources presently available on hardware. Here, different spiking networks were designed, tested in simulation, and implemented on the neuromorphic processor DYNAP-SE, to obtain silicon neurons that are tuned to visual stimuli oriented at specific angles and with specific spatial frequencies, provided by the event camera DVS. Recurrent clustered inhibition was successfully tested on spiking neural networks, both in simulation and on the DYNAP-SE board, to obtain neurons with highly structured Gabor-like receptive fields (RFs); these neurons are characterized by tuning curves that are sharper or at least comparable to the ones obtained using equivalent feed-forward schem es, but require a significantly lower number of synapses. The resulting harmonic signal description provided by the proposed neuromorphic circuit could be potentially used for a complete characterization of the 2D local structure of the visual signal in terms of phase relationships from all the available oriented channels.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Neuroinformatics
Dewey Decimal Classification:570 Life sciences; biology
Scopus Subject Areas:Physical Sciences > Computer Graphics and Computer-Aided Design
Physical Sciences > Computer Science Applications
Physical Sciences > Computer Vision and Pattern Recognition
Language:English
Event End Date:29 February 2020
Deposited On:15 Feb 2021 10:22
Last Modified:16 Feb 2021 21:01
Publisher:SciTePress
ISBN:978-989-758-402-2
OA Status:Hybrid
Publisher DOI:https://doi.org/10.5220/0009171205300537

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