Abstract
Knowledge about how the brain processes information is expanding rapidly. Two examples from neuroscience systems are (1) the observation that shifts in chemical modulation markedly alter the intrinsic behavior of neural networks and (2) the identification of nested modules within the neocortex that are capable of integrating parallel distributed and hierarchical processes. These examples have not been fully examined from a computational perspective. Algorithms motivated by neuroscience are interesting, and their unusual properties may have implications for information science and the design of novel computing architectures.
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