Publication Summary and Abstract
Prescott, T. J., Gurney, K., and Redgrave, P. (2003), Basal Ganglia, The Handbook of Brain Theory and Neural Networks, M. A. Arbib (Ed.), 2nd Edition .
Lying either side of the forebrain/midbrain boundary, at the hub of the mammalian brain, the basal ganglia are a group of highly interconnected brain structures with a critical influence over movement and cognition. The importance of these nuclei for a cluster of human brain disorders including Parkinson's disease, Huntington's disease, and schizophrenia, has produced a century or more of strong clinical interest, and a prodigious volume of neurobiological research. Given the wealth of relevant data, and a pressing need for a better functional understanding of these structures, the basal ganglia provide one of the most exciting prospects for computational modelling of brain function. This article will begin by summarising aspects of the functional architecture of the mammalian basal ganglia and then describe the computational approaches that have been developed over the course of the past decade (see also Houk, Davis, and Beiser, 1995; Wickens, 1997; Gillies and Arbuthnott, 2000). An important task for an appraisal of computational models is to provide a framework for comparing pieces of work that can differ radically in their breadth of focus, level of analysis, computational premises, and methodology, and whose relative merits can consequently be difficult to ascertain (see LEVELS OF ANALYSIS). Here, we first distinguish between models that attempt to incorporate appropriate biological data (anatomical and/or physiological) and those that attempt an explanation of function using generic neural network architectures. In this review we limit ourselves to just those models that incorporate known neurobiological constraints and consider some of the implications for these models of recent biological data. Second, we divide models into two main levels of analysis: (i) those that work at a comparatively low level of detail (membrane properties of individual neurons and micro-anatomical features) and which restrict themselves to a single component of the basal ganglia nucleus; and (ii) those that deal at the system-levelš with the basal ganglia as a whole and/or with their interactions with related structures (e.g. thalamus and cortex). Third, we seek to classify system-level models in terms of the primary computational role that they suppose is being addressed by the neural substrate.
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