Color-opponent mechanisms for local hue encoding in a hierarchical framework

TL;DR

This paper introduces a hierarchical, biologically plausible computational model that encodes local hue through neuron networks, revealing how visual cortical layers contribute to hue representation and the encoding of unique and physical hues.

Contribution

The model combines single-opponent and hue-selective neurons to simulate local hue encoding and demonstrates how cortical layers contribute to hue perception, aligning with primate visual system data.

Findings

Single-opponent neurons have wide tuning curves.

Hue-selective neurons exhibit narrower tunings similar to V4.

Neurons in V4 can encode unique hues and span physical hue space.

Abstract

A biologically plausible computational model for color representation is introduced. We present a mechanistic hierarchical model of neurons that not only successfully encodes local hue, but also explicitly reveals how the contributions of each visual cortical layer participating in the process can lead to a hue representation. Our proposed model benefits from studies on the visual cortex and builds a network of single-opponent and hue-selective neurons. Local hue encoding is achieved through gradually increasing nonlinearity in terms of cone inputs to single-opponent cells. We demonstrate that our model's single-opponent neurons have wide tuning curves, while the hue-selective neurons in our model V4 layer exhibit narrower tunings, resembling those in V4 of the primate visual system. Our simulation experiments suggest that neurons in V4 or later layers have the capacity of encoding unique hues. Moreover, with a few examples, we present the possibility of spanning the infinite space of physical hues by combining the hue-selective neurons in our model.