The basal ganglia are a group of subcortical nuclei that contain about 100million neurons in humans. Different modes of basal ganglia dysfunction lead toParkinson;;s disease and Huntington;;s disease, which have debilitating motor andcognitive symptoms. However, despite intensive study, both the internal computationalmechanisms of the basal ganglia, and their contribution to normal brainfunction, have been elusive. The goal of this thesis is to identify basic principles thatunderlie basal ganglia function, with a focus on signal representation, computation,dynamics, and plasticity.This process begins with a review of two current hypotheses of normal basalganglia function, one being that they automatically select actions on the basis ofpast reinforcement, and the other that they compress cortical signals that tend tooccur in conjunction with reinforcement. It is argued that a wide range of experimentaldata are consistent with these mechanisms operating in series, and that inthis configuration, compression makes selection practical in natural environments.Although experimental work is outside the present scope, an experimental meansof testing this proposal in the future is suggested.The remainder of the thesis builds on Eliasmith & Anderson;;s Neural EngineeringFramework (NEF), which provides an integrated theoretical account of computation,representation, and dynamics in large neural circuits. The NEF providesconsiderable insight into basal ganglia function, but its explanatory power is potentiallylimited by two assumptions that the basal ganglia violate. First, like mostlarge-network models, the NEF assumes that neurons integrate multiple synapticinputs in a linear manner. However, synaptic integration in the basal ganglia isnonlinear in several respects. Three modes of nonlinearity are examined, includingnonlinear interactions between dendritic branches, nonlinear integration within terminalbranches, and nonlinear conductance-current relationships. The first modeis shown to affect neuron tuning. The other two modes are shown to enable alternativecomputational mechanisms that facilitate learning, and make computationmoreflexible, respectively.Secondly, while the NEF assumes that the feedforward dynamics of individualneurons are dominated by the dynamics of post-synaptic current, many basalganglia neurons also exhibit prominent spike-generation dynamics, including adaptation,bursting, and hysterses. Of these, it is shown that the NEF theory ofnetwork dynamics applies fairly directly to certain cases offiring-rate adaptation.However, more complex dynamics, including nonlinear dynamics that are diverseacross a population, can be described using the NEF equations for representation.In particular, a neuron;;s response can be characterized in terms of a more complexfunction that extends over both present and past inputs. It is therefore straightforwardto apply NEF methods to interpret the effects of complex cell dynamics atthe network level.The role of spike timing in basal ganglia function is also examined. Althoughthe basal ganglia have been interpreted in the past to perform computations onthe basis of mean firing rates (over windows of tens or hundreds of milliseconds)it has recently become clear that patterns of spikes on finer timescales are alsofunctionally relevant. Past work has shown that precise spike times in sensorysystems contain stimulus-related information, but there has been little study of how post-synaptic neurons might use this information. It is shown that essentially any neuron can use this information to perform flexible computations, and that thesecomputations do not require spike timing that is very precise. As a consequence,irregular and highly-variable firing patterns can drive behaviour with which theyhave no detectable correlation.Most of the projection neurons in the basal ganglia are inhibitory, and the effectof one nucleus on another is classically interpreted as subtractive or divisive. Theoretically, very flexible computations can be performed within a projection if eachpresynaptic neuron can both excite and inhibit its targets, but this is hardly everthe case physiologically. However, it is shown here that equivalent computational flexibility is supported by inhibitory projections in the basal ganglia, as a simple consequence of inhibitory collaterals in the target nuclei.Finally, the relationship between population coding and synaptic plasticity isdiscussed. It is shown that Hebbian plasticity, in conjunction with lateral connections, determines both the dimension of the population code and the tuning ofneuron responses within the coded space. These results permit a straightforwardinterpretation of the effects of synaptic plasticity on information processing at thenetwork level.Together with the NEF, these new results provide a rich set of theoretical principlesthrough which the dominant physiological factors that affect basal gangliafunction can be more clearly understood.
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