Computational Study of the Mechanisms Underlying Oscillation in Neuronal Locomotor Circuits
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In this thesis we model two very different movement-related neuronal circuits, both of which produce oscillatory patterns of activity.
In one case we study oscillatory activity in the basal ganglia under both normal and Parkinsonian conditions. First, we used a detailed Hodgkin-Huxley type spiking model to investigate the activity patterns that arise when oscillatory cortical input is transmitted to the globus pallidus via the subthalamic nucleus. Our model reproduced a result from rodent studies which shows that two anti-phase oscillatory groups of pallidal neurons appear under Parkinsonian conditions. Secondly, we used a population model of the basal ganglia to study whether oscillations could be locally generated. The basal ganglia are thought to be organised into multiple parallel channels. In our model, isolated channels could not generate oscillations, but if the lateral inhibition between channels is sufficiently strong then the network can act as a rhythm-generating ``pacemaker'' circuit. This was particularly true when we used a set of connection strength parameters that represent the basal ganglia under Parkinsonian conditions.
Since many things are not known about the anatomy and electrophysiology of the basal ganglia, we also studied oscillatory activity in another, much simpler, movement-related neuronal system: the spinal cord of the Xenopus tadpole. We built a computational model of the spinal cord containing approximately 1,500 biologically realistic Hodgkin-Huxley neurons, with synaptic connectivity derived from a computational model of axon growth. The model produced physiological swimming behaviour and was used to investigate which aspects of axon growth and neuron dynamics are behaviourally important. We found that the oscillatory attractor associated with swimming was remarkably stable, which suggests that, surprisingly, many features of axonal growth and synapse formation are not necessary for swimming to emerge. We also studied how the same spinal cord network can generate a different oscillatory pattern in which neurons on both sides of the body fire synchronously. Our results here suggest that under normal conditions the synchronous state is unstable or weakly stable, but that even small increases in spike transmission delays act to stabilise it.
Finally, we found that although the basal ganglia and the tadpole spinal cord are very different systems, the underlying mechanism by which they can produce oscillations may be remarkably similar. Insights from the tadpole model allow us to predict how the basal ganglia model may be capable of producing multiple patterns of oscillatory activity.
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