Description: Learning and representing and reasoning about temporal relations, particularly causal relations, is a deep problem in artificial intelligence (AI). Learning such representations in the real world is complicated by the fact that phenomena are subject to multiple time scale influences and may operate with a strange attractor dynamic. This dissertation proposes a new computational learning mechanism, the adaptrode, which, used in a neuromimic processing architecture may help to solve some of these problems. The adaptrode is shown to emulate the dynamics of real biological synapses and represents a significant departure from the classical weighted input scheme of conventional artificial neural networks. Indeed the adaptrode is shown, by analysis of the deep structure of real synapses, to have a strong structural correspondence with the latter in terms of multi-time scale biophysical processes. Simulations of an adaptrode-based neuron and a small network of neurons are shown to have the same learning capabilities as invertebrate animals in classical conditioning. Classical conditioning is considered a fundamental learning task in animals. Furthermore, it is subject to temporal ordering constraints that fulfill the criteria of causal relations in natural systems. It may offer clues to the learning of causal relations and mechanisms for causal reasoning. The adaptrode is shown to solve an advanced problem in classical conditioning that addresses the problem of real world dynamics. A network is able to learn multiple, contrary associations that separate in time domains, that is a long-term memory can co-exist with a short-term contrary memory without destroying the former. This solves the problem of how to deal with meaningful transients while maintaining long-term memories. Possible applications of adaptrode-based neural networks are explored and suggestions for future research are made.
Date: August 1994
Creator: Mobus, George E. (George Edward)
Partner: UNT Libraries