title: Computational dynamics of gradient bistable networks.
abstract: We describe a neural-like, homogeneous network consisting of coupled bistable elements and we study its abilities of learning, pattern recognition and computation. The technique allows new possibilities of pattern recognition, including the memorization and perfect recall of several memory patterns, without interference from spurious states. When the coupling strength between elements exceeds a critical value, the network readily converges to a unique attractor. Below this critical value one could perfectly recall all memorized patterns.
authors.affiliation: Department of Chemistry, University of Toronto, Toronto, Canada. vchinaro@chem.utoronto.ca
authors.forename: V
authors.initials: V
authors.lastname: Chinarov
authors.forename: M
authors.initials: M
authors.lastname: Menzinger
mesh_terms: D000595:Amino Acid Sequence; D008956:Models, Chemical; D016571:Neural Networks, Computer; D009842:Oligopeptides / Q000737:chemistry; D017510:Protein Folding
chemical_list: D009842:Oligopeptides