Abstract
Traditional views of visual processing suggest that early visual neurons in areas V1 and V2 are static spatiotemporal filters that extract local features from a visual scene. The extracted information is then channeled through a feedforward chain of modules in successively higher visual areas for further analysis. Recent electrophysiological recordings from early visual neurons in awake behaving monkeys reveal that there are many levels of complexity in the information processing of the early visual cortex, as seen in the long-latency responses of its neurons. These new findings suggest that activity in the early visual cortex is tightly coupled and highly interactive with the rest of the visual system. They lead us to propose a new theoretical setting based on the mathematical framework of hierarchical Bayesian inference for reasoning about the visual system. In this framework, the recurrent feedforward/feedback loops in the cortex serve to integrate top-down contextual priors and bottom-up observations so as to implement concurrent probabilistic inference along the visual hierarchy. We suggest that the algorithms of particle filtering and Bayesian-belief propagation might model these interactive cortical computations. We review some recent neurophysiological evidences that support the plausibility of these ideas.
Highlights
In this paper we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern theory and on (b) recent neurophysiological experimental evidence
We bring in a powerful and widely applicable paradigm from artificial intelligence and computer vision to propose some new ideas about the algorithms of visual cortical processing and the nature of representations in the visual cortex
Recent neurophysiological experiments have provided a variety of evidence suggesting that feedback from higherorder areas can modulate the processing of the early visual cortex
Summary
In this paper we propose a Bayesian theory of hierarchical cortical computation based both on (a) the mathematical and computational ideas of computer vision and pattern theory and on (b) recent neurophysiological experimental evidence. The latter has been used for tracking moving objects in the presence of clutter and irregular motion (situations in which all other techniques have failed).[23,24] Its use is developing rapidly in the robotics community, for example, for solving mapping and localization problems in mobile robots in a real-world scenario.[25] We see some very attractive features in both of these algorithms that might be implemented naturally by cortical neural networks We believe that this theory provides a plausible and much more tightly coupled model of the processing in visual areas and especially in V1 and V2. We will first sketch the general theoretical framework and in subsequent sections review the experimental evidence that points in the direction of this theory
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