A Search For Principles of Basal Ganglia Function

The basal ganglia are a group of subcortical nuclei that contain about 100 million neurons in humans. Di erent modes of basal ganglia dysfunction lead to Parkinson's disease and Huntington's disease, which have debilitating motor and cognitive symptoms. However, despite intensive study, both the internal computational mechanisms of the basal ganglia, and their contribution to normal brain function, have been elusive. The goal of this thesis is to identify basic principles that underlie 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 basal ganglia function, one being that they automatically select actions on the basis of past reinforcement, and the other that they compress cortical signals that tend to occur in conjunction with reinforcement. It is argued that a wide range of experimental data are consistent with these mechanisms operating in series, and that in this con guration, compression makes selection practical in natural environments. Although experimental work is outside the present scope, an experimental means of testing this proposal in the future is suggested. The remainder of the thesis builds on Eliasmith & Anderson's Neural Engineering Framework (NEF), which provides an integrated theoretical account of computation, representation, and dynamics in large neural circuits. The NEF provides considerable insight into basal ganglia function, but its explanatory power is potentially limited by two assumptions that the basal ganglia violate. First, like most large-network models, the NEF assumes that neurons integrate multiple synaptic inputs in a linear manner. However, synaptic integration in the basal ganglia is nonlinear in several respects. Three modes of nonlinearity are examined, including nonlinear interactions between dendritic branches, nonlinear integration within terminal branches, and nonlinear conductance-current relationships. The rst mode is shown to a ect neuron tuning. The other two modes are shown to enable alternative computational mechanisms that facilitate learning, and make computation more exible, respectively. Secondly, while the NEF assumes that the feedforward dynamics of individual neurons are dominated by the dynamics of post-synaptic current, many basal ganglia neurons also exhibit prominent spike-generation dynamics, including adaptation, bursting, and hysterses. Of these, it is shown that the NEF theory of network dynamics applies fairly directly to certain cases of ring-rate adaptation. However, more complex dynamics, including nonlinear dynamics that are diverse across 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 complex function that extends over both present and past inputs. It is therefore straightforward to apply NEF methods to interpret the e ects of complex cell dynamics at the network level. The role of spike timing in basal ganglia function is also examined. Although the basal ganglia have been interpreted in the past to perform computations on