Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition
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Author(s)
Sanchez-Garcia, Melani
Chauhan, Tushar
Cottereau, Benoit R.
Beyeler, Michael
Date Issued
April 1, 2023
Publisher
Springer Berlin Heidelberg
Citation
Sanchez-Garcia, Melani, Chauhan, Tushar, Cottereau, Benoit R. and Beyeler, Michael. 2023. "Efficient multi-scale representation of visual objects using a biologically plausible spike-latency code and winner-take-all inhibition."
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Author's final manuscript
Abstract
Abstract
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.
MIT Department
Picower Institute for Learning and Memory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s00422-023-00956-x
