Feed-forward and noise-tolerant detection of feature homogeneity in spiking networks with a latency code

TL;DR

This paper introduces a rapid, feed-forward mechanism for detecting feature homogeneity in visual processing using spike latency coding, which can enhance image segmentation and is robust to neuronal noise.

Contribution

It presents a novel, purely feed-forward model that detects feature homogeneity via spike timing, integrating noise mitigation through delayed inhibition.

Findings

The model supports rapid detection of homogeneity in visual inputs.

Delayed feed-forward inhibition reduces noise effects.

The mechanism enables low-latency processing in spiking networks.

Abstract

In studies of the visual system as well as in computer vision, the focus is often on contrast edges. However, the primate visual system contains a large number of cells that are insensitive to spatial contrast and, instead, respond to uniform homogeneous illumination of their visual field. The purpose of this information remains unclear. Here, we propose a mechanism that detects feature homogeneity in visual areas, based on latency coding and spike time coincidence, in a purely feed-forward and therefore rapid manner. We demonstrate how homogeneity information can interact with information on contrast edges to potentially support rapid image segmentation. Furthermore, we analyze how neuronal crosstalk (noise) affects the mechanism's performance. We show that the detrimental effects of crosstalk can be partly mitigated through delayed feed-forward inhibition that shapes bi-phasic post-synaptic events. The delay of the feed-forward inhibition allows effectively controlling the size of the temporal integration window and, thereby, the coincidence threshold. The proposed model is based on single-spike latency codes in a purely feed-forward architecture that supports low-latency processing, making it an attractive scheme of computation in spiking neuronal networks where rapid responses and low spike counts are desired.