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Neural network models of reinforcement learning and oculomotor decision-making in the basal ganglia and frontal cortex

Posted on:2002-01-24Degree:Ph.DType:Dissertation
University:Boston UniversityCandidate:Brown, Joshua WFull Text:PDF
GTID:1464390011997411Subject:Biology
Abstract/Summary:
This work develops and simulates neural models of how the brain learns to selectively respond to rewarding events. One project models how the brain learns to selectively respond to unexpected rewards. Another models how the brain learns to make saccadic (ballistic) eye movements to gain rewards.; After classically conditioned learning, dopaminergic substantia nigra pars compacta (SNc) cells of the basal ganglia respond only to conditioned stimuli, notably rewards, that are unexpected. These cells play an important role in reinforcement learning and signal reward prediction errors. A neural model explains the key neurophysiological properties of these cells before, during, and after conditioning, as well as related anatomical and neurophysiological data about the pedunculo-pontine tegmental nucleus (PPTN), lateral hypothalamus, ventral striatum, and striosomes. The model proposes how two parallel learning pathways from limbic cortex to the SNc, one devoted to excitatory conditioning (through the ventral striatum, ventral pallidum, and PPTN) and the other to adaptively timed inhibitory conditioning (through the striosomes), control SNc responses.; Dopaminergic reward-related signals broadcast to the basal ganglia and frontal cortex to reinforce rewarded behavior. These areas together allow animals to learn adaptive responses to acquire rewards when prepotent responses are insufficient. Anatomical studies show a rich interaction between the basal ganglia and distinct frontal cortical layers. Analysis of the laminar circuitry of the frontal cortex, especially the frontal eye fields, and its interactions with the basal ganglia, motor thalamus, superior colliculus, inferotemporal and parietal cortices, and related structures provides new insight into how these brain regions together learn and perform complex behaviors. A neural model simulates these interacting circuits and provides a functional explanation and real-time simulation of seventeen cell types. The model predicts how action planning or priming is dissociated from execution, how a cue may serve either as a movement target or as a discriminative cue to move elsewhere, and how the basal ganglia help choose among competing actions. The model simulates neurophysiological, anatomical, and behavioral data about how monkeys perform saccadic eye movement tasks, including fixation; single saccade, overlap, gap, and memory-guided saccades; anti-saccades; and parallel search among distractors.
Keywords/Search Tags:Basal ganglia, Model, Neural, Frontal, Brain learns, Cortex
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