Neural networks for temporal order learning and stimulus-specific habituation

The central theme is a quest for models of learning using the neural network approach. There are two parts to the dissertation: a formal neural network model of temporal order learning and a biological neural model for stimulus-specific habituation in toads. In the first part, learning mechanisms are explored at the abstract level, i.e., we design neural networks to learn, recognize, and reproduce complex temporal sequences. Short-term memory is modeled by a network of neural units with mutual inhibition. Sequences are acquired for long-term memory with a new rule, called the attentional learning rule, that combines Hebbian rule and a normalization rule with sequential system activation. Acquired sequences can be recognized without being affected by speeds in presentation and certain distortions in symbol forms. Sequence reproduction is achieved with two reciprocally connected layers, and reproduction of complex sequences can maintain the temporal course of learned sequences. Different layers of the model can be constructed in a feedforward manner to recognize hierarchically organized temporal structures, in a way similar to human information chunking. In the second part, learning mechanisms are studied at the neurobiological level with a specific animal. A computational model is first presented for visual pattern discrimination in toads. The anterior thalamus (AT) model integrates visual inputs from the retina and the tectum, and produces orderly average firing activities in response to the stimuli in a dishabituation hierarchy. The output from the AT model is fed to the model of the medial pallium (MP), where neuronal responses to the stimuli are further processed and stored. A model of synaptic plasticity is proposed for MP as an interaction of two dynamic processes which simulates acquisition and both short- and long-term forgetting. Large-scale computer simulations demonstrate that the model of the interacting brain structures can reproduce experimental data remarkably well. The model of AT and MP structures yields a range of experimental predictions concerning the properties of learning and pattern discrimination. Initial model testing experiments have validated certain predictions and lead to new findings of behavioral phenomena. (Copies available exclusively from Micrographics Department, Doheny Library, USC, Los Angeles, CA 90089-0182.)