Coupled Maps as Tool for Modeling Human Information Processing: Issues of Readout

In trying to establish neurally plausible models of human information processing, the connectionist movement failed because their models lacked interesting dynamics. We propose that coupled maps offer neurodynamically more plausible tools for modeling cognitive phenomena. Activity in coupled maps shows dynamic synchronization behavior that is phenomenally similar to large-scale brain activity. It is possible to tweak this behavior to enable reading-in of spatially patterned information, and to engineer mechanisms for encoding storage of these patterns. Previous work showed how these solutions lead to simple explanations of a number of phenomena observed in human visual perception, including some visual illusions and multi-stability; and in memory, such as iconic storage, slow decay, and spontaneous re-instantiation. The present work explores dynamic weight adjustment at multiple time scales simultaneously, in particular its possible utility for memory retrieval and read-out of patterned information.