Efficient visual object representation using a biologically plausible spike-latency code and winner-take-all inhibition. (arXiv:2205.10338v2 [cs.CV] UPDATED)

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
from the Fashion MNIST dataset. Stimuli were preprocessed with center-surround
receptive fields and then fed …

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