Nested reentrant and recurrent computation in early vision: a Bayesian neuromorphic model applied to hyperacuity

Abstract

Hyperacuity is demonstrated in a neuromorphic model of the early visual system. The model incorporates Bayesian principles which are embodied in the dynamics of reentrant and recurrent feedback processes. Each retinotopically mapped area in the model represents a transformation of data from the visual field. Sensory information propagates in a bottom-up direction from one area to the next, while information based on Bayesian priors propagates in a top-down direction through reentrant connections. The 'bottom-up' and 'top-down' information maintain a separate existence in distinct layers of the model, but they interact through local connections within each area. Transformations between one area and the next are defined by the reentrant synaptic connections between areas, while local prior probability maps are defined by local recurrent connections within layers. The representation of hyperacuity is accomplished using a model of functional multiplicity: the large ratio of neurons in striate cortex compared with the number of afferent fibers projecting from the lateral geniculate nucleus. High functional multiplicity, in conjunction with hierarchical reentrant processing, allows the model to represent a fine-grained restoration of the line structure of visual input.