The Biological and Computational Bases of Vision

of feature abstraction from visual stimuli stored in higher cortical areas would make accurate classification of lower visual processing information possible, if the computational machinery of hierarchical Bayesian inference were realized in neuronal circuits.58 The store of prior distributions of abstracted parameters of visual targets could be learned from experience and stored in relatively compact form in memory.

The possibility of hierarchical Bayesian methods being realized in the function of neuronal circuits has been argued convincingly by several authors.55,56,58 Furthermore, the concept of hierarchical processing across levels of the visual system and higher cortical centers suggests a number of testable hypotheses, some of which have been evaluated by fMRI and behavioral experiments.57 Overall, the most compelling conclusion of this line of research is that the brain is apparently using algorithms as sophisticated (and perhaps identical to) the most advanced methods used in current image analysis and computational science. A further exciting aspect is that the brain is likely using methods we have yet to learn that are even more intricate and efficient than the best of our current computational methods.58

In conclusion, the visual system is the best-studied sensory system. It has incredible facility for rapid recognition of objects in the visual field and evocation of appropriate responses. As our understanding of higher processing in the visual system advances, we have great opportunity for learning and profiting from the methods used by the optimized biological image processors that have resulted from eons of natural selection.

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