Brain-structured Connectionist Networks that Perceive and Learn

Abstract This paper specifies the main features of connectionist and brain-like connectionist models; argues for the need for, and usefulness of, appropriate successively larger brainlike structures; and examines parallel-hierarchical Recognition Cone models of perception from this perspective, as examples of networks exploiting such structures (e.g. local receptive fields, global convergence-divergence). The anatomy, physiology, behavior, and development of the visual system are briefly summarized to motivate the architecture of brain-structured networks for perceptual recognition. Results are presented from simulations of carefully pre-designed Recognition Cone structures that perceive objects (e.g. houses) in digitized photographs. A framework for perceptual learning is introduced, including mechanisms for generation learning, i.e. the growth of new links and possibly, nodes, subject to brain-like topological constraints. The information processing transforms discovered through feedback-guided generati...

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