SOLITON REVERBERATION

THIS PROPOSAL SPECIFICALLY ADDRESSES "KNOWLEDGE REPRESENTATION" BY PERFORMING RESEARCH INTO "SOFTWARE DEVICES FOR CAPTURING KNOWLEDGE FROM A REAL-TIME CONTEXT ANDCONVERTING IT TO AN EFFECTIVE COMPUTATIONAL FORM". THE PROPOSED ARTIFICIAL NEURAL NETWORK LEARNING PARADIGM OPERATES ON SPATIOTEMPORAL PATTERNS AND HAS THE FOLLOWING CHARACTERISTICS: (1) OPERATES CONTINUOUSLY WITHOUT AD HOC SUPERVISION; (2) STORES NOVEL DATA UPON A SINGLE PRESENTATION (HAS ADEQUATE "PLASTICITY"); (3) RETAINS STOREDPATTERNS INDEFINITELY (HAS ADEQUATE "STABILITY"); (4) MAKES EFFICIENT USE OF ITS STORAGE RESOURCES (UTILIZES ITS "CAPACITY"); AND (5) STORES PATTERNS IN A FORM WHICH, UPON RECALL, IS EFFECTIVE IN THE COMPUTATION OF PREDICTIVE ESTIMATES AND ADAPTIVE MOTOR CONTROL SIGNALS. THE ALORITHM EMPLOYS AN ENTROPIC DEFINITION OF HOMEOSTASIS AND IMPLEMENTSA GENERALIZED KALMAN FILTER USING LEARNED ENVIRONMENTAL DYNAMICS.