  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 to a layer of spiking neurons whose synaptic
weights were updated using spike-timing-dependent-plasticity (STDP). We
investigate how the quality of the represented objects changes under different
WTA-I schemes and demonstrate that a network of 150 spiking neurons can
efficiently represent objects with as little as 40 spikes. 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.
