The neurophysiology of stereoscopic vision

Abstract

Many animals are able to perceive stereoscopic depth owing to the disparity information that arises from the left and right eyes' horizontal displacement on the head. The initial computation of disparity happens in primary visual cortex (V1) and is largely considered to be a correlation-based computation. In other words, the computational role of V1 as it pertains to stereoscopic vision can be seen to roughly perform a binocular cross-correlation between the images of the left and right eyes. This view is based on the unique success of a correlation-based model of disparity-selective cells { the binocular energy model (BEM). This thesis addresses two unresolved challenges to this narrative. First, recent evidence suggests that a correlation-based view of primary visual cortex is unable to account for human perception of depth in a stimulus where the binocular correlation is on average zero. Chapters 1 and 2 show how a simple extension of the BEM which better captures key properties of V1 neurons allows model cells to signal depth in such stimuli. We also build a psychophysical model which captures human performance closely, and recording from V1 in the macaque, we then show that these predicted properties are indeed observed in real V1 neurons. The second challenge relates to the long-standing inability of the BEM to capture responses to anticorrelated stimuli: stimuli where the contrast is reversed in the two eyes (e.g. black features in the left eye are matched with identical white features in the right eye). Real neurons respond less strongly to these stimuli than model cells. In Chapter 3 and 4, we make use of recent advances in optimisation routines and exhaustively test the ability of a generalised BEM to capture this property. We show that even the best- tting generalised BEM units only go some way towards describing neuronal responses. This is the rst exhaustive empirical test of this in uential modelling framework, and we speculate on what is needed to develop a more complete computational account of visual processing in primary visual cortex.