Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition.

Publisher: Springer Verlag Country of Publication: Germany NLM ID: 7502533 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1432-0770 (Electronic) Linking ISSN: 03401200 NLM ISO Abbreviation: Biol Cybern Subsets: MEDLINE

Publication: Berlin : Springer Verlag
Original Publication: Berlin, New York, Springer-Verlag.

Neuronal Plasticity*/physiology ; Models, Neurological*; Humans ; Neural Networks, Computer ; Learning/physiology ; Visual Perception/physiology

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses spike-latency coding and winner-take-all inhibition (WTA-I) to efficiently represent visual stimuli using multi-scale parallel processing. Mimicking neuronal response properties in early visual cortex, images were preprocessed with three different spatial frequency (SF) channels, before they were fed to a layer of spiking neurons whose synaptic weights were updated using spike-timing-dependent-plasticity. We investigate how the quality of the represented objects changes under different SF bands and WTA-I schemes. We demonstrate that a network of 200 spiking neurons tuned to three SFs can efficiently represent objects with as little as 15 spikes per neuron. Studying how core object recognition may be implemented using biologically plausible learning rules in SNNs may not only further our understanding of the brain, but also lead to novel and efficient artificial vision systems.
(© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

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Keywords: Multi-scale processing; Spike-latency code; Spike-timing-dependent-plasticity; Spiking neural networks; Winner-take-all inhibition

Date Created: 20230402 Date Completed: 20230508 Latest Revision: 20230509

20250114

10.1007/s00422-023-00956-x

37004546