Rapid decision threshold modulation by reward rate in a neural network
Neural Networks - 2006 Special issue: Neurobiology of decision making
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Neural network models of complex cognitive tasks are difficult to build. Most previous work has focused on the difficulty of using structured symbolic representations in neural networks. This thesis focuses on the problem of control. During problem solving, some form of control is necessary for sequencing operations, for selecting actions, and for manipulating goal representations. I present a set of control mechanisms inspired and constrained by brain organization that are powerful enough to guarantee basic problem solving ability; in fact, I show that they are computationally universal. These mechanisms exploit a simple method for controlling the temporal characteristics of activation in continuous-time neural networks that makes neural control of complex processes possible in properly organized neural cognitive models. The basic computational primitive is inspired by corticostriatal loops in which the cortical component is composed of columns organized in layers. An input layer and an output layer each form winner-take-all networks. These layers are connected via a corticostriatal loop that produces a controllable amount of internal propagation delay in signal transmission from input layer to output layer. Modules can be composed hierarchically to produce goal-directed control circuits for cognitive models that are formally equivalent to finite automata and share many properties of symbolic production systems. These control circuits are instantiated in a neural cognitive model of the Tower of London problem-solving task. The model implements the assumption that dorsolateral prefrontal cortex is preferentially involved in representing subgoal information during problem solving, and that frontostriatal loop circuits provide a timing function that is critical for proper problem solving performance. Normal subject performance is accurately simulated by the model, and performance under conditions of simulated prefrontal lesions and Parkinson's disease captures speed and accuracy impairments exhibited in patient data from the literature.