  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
(STDP). 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.
