A Neural Model of Biased Oscillations in Aplysia Head-Waving Behavior

A long-term bias in the exploratory head-waving behavior of Aplysia can be induced using bright lights as an aversive stimulus: coupling onset of the lights with head movements to one side results in a bias away from that side (Cook & Carew, 1986). This bias has been interpreted as a form of operant conditioning, and has previously been simulated with a neural network model based on associative synaptic facilitation (Raymond, Baxter, Buonomano, & Byrne, 1992). In this article we simulate the head-waving behavior using a recurrent gated dipole, a nonlinear dynamical neural model that has previously been used to explain various data including oscillatory behavior in biological pacemakers. Within the recurrent gated dipole, two channels operate antagonistically to generate oscillations, which drive the side-to-side head waving. The frequency of oscillations depends on transmitter mobilization dynamics, which exhibit both short- and long-term adaptation. We assume that light onset results in a nonspecific increase in arousal to both channels of the dipole. Repeated pairing of arousal increments with activation of one channel (the "reinforced" channel) of the dipole leads to a bias in transmitter dynamics, which causes the oscillation to last a shorter time on the reinforced channel than on the non-reinforced channel. Our model provides a parsimonious explanation of the observed behavior, and it avoids some of the unexpected results obtained with the Raymond et al. model. In addition, our model makes predictions concerning the rate of onset and extinction of the biases, and it suggests new lines of experimentation to test the nature of the head-waving behavior.Office of Naval Research (N00014-92-J-4015, N00014-91-J-4100, N0014-92-J-1309); Air Force Office of Scientific Research (F49620-92-J-0499); A.P. Sloan Foundation (BR-3122

Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

A Multi-Time Scale Learning Mechanism for Neuromimic Processing

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