Modelling binocular disparity processing from statistics in natural scenes. (November 2020)

• Record Type: Journal Article
• Authors: Chauhan, Tushar; Héjja-Brichard, Yseult; Cottereau, Benoit R.
• Is Part Of: Vision research. Volume 176(2020)
• Page Start: 27
• Page End: 39
• Publication Date: 2020-11
• Subjects: Computational neuroscience -- Natural scene statistics -- Binocular disparity -- Binocular vision; Vision -- Periodicals; 573.88
• DOI: 10.1016/j.visres.2020.07.009
• Languages: English
• ISSNs: 0042-6989

Abstract

Abstract: The statistics of our environment impact not only our behavior, but also the selectivity and connectivity of the early sensory cortices. Over the last fifty years, powerful theories such as efficient coding, sparse coding, and the infomax principle have been proposed to explain the nature of this influence. Numerous computational and theoretical studies have since demonstrated solid, testable evidence in support of these theories, especially in the visual domain. However, most such work has concentrated on monocular, luminance-field descriptions of natural scenes, and studies that systematically focus on binocular processing of realistic visual input have only been conducted over the past two decades. In this review, we discuss the most recent of these binocular computational studies, with particular emphasis on disparity selectivity. We begin with a report of the relevant literature demonstrating concrete evidence for the relationship between natural disparity statistics, neural selectivity, and behavior. This is followed by a discussion of supervised and unsupervised computational studies. For each study, we include a description of the input data, theoretical principles employed in the models, and the contribution of the results in explaining biological data (neural and behavioral). In the discussion, we compare these models to the binocular energy model, and examine their application to the modelling of normal and abnormal development of vision. We conclude with a short description of what we believe are the most important limitations of the current state-of-the-art, and directions for future work which could address these shortcomings and enrich current and future models.