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Biophysical modeling and optical imaging tools for studies of cerebellar motor learning
Eran Abraham Mukamel, Ph.D.
Stanford University, 2009Motor learning plays an important role in each of our lives, whether we regularly play a musical instrument, throw a ball, or simply wish to move about with coordination and grace. The neural circuit in the cerebellum that helps us to learn new movements and to adjust our performance of familiar reflexes is remarkable for its quasi-crystalline architecture, which has been conserved at the cellular level across vertebrate species. This thesis develops and applies tools based on biophysical and computational network modeling, as well as analysis of data sets from optical microscopy experiments, to study neural mechanisms of motor learning in the cerebellum. In the first part of the thesis, we use theoretical methods including compartmental modeling and statistical learning theory to explore hypotheses concerning temporal aspects of learned movements. We propose that the geometric structure of the cerebellar cortex may be useful for improving the neural representation of time. We go on to use a "lock and key" theory to explain a longstanding observation regarding the required causal temporal relationship among sensory stimuli for effective motor training. Our theory relates this timing aspect of motor learning to a biophysical mechanism, rebound conductances, present in the neurons of the deep cerebellar nuclei. In the second half of the thesis we use data from calcium fluorescence imaging experiments to probe the intracellular dynamics of cerebellar neurons and glial cells in living subjects. We develop computational and statistical analysis procedures for extracting physiological signals, including neuronal spike trains and glial calcium transients, from noisy and high-dimensional fluorescence imaging data sets. We discuss a novel miniaturized microscope that can be used to record fluorescence imaging data from freely moving mice. We discovered three forms of dynamic activation in networks of cerebellar Bergmann glia, one of which is associated with the onset of locomotor activity. We also used automated procedures, including independent component analysis, to identify the locations and extract the temporal dynamics of individual cells in movie data. Our procedures solve the cell sorting problem that is analogous to the challenge of spike sorting in extracellular electrophysiology. We applied cell sorting to calcium imaging data sets to reveal patterns of correlated complex spiking in groups of Purkinje cell dendrites. Using in vivo optical microscopy we showed that microzones of correlated complex spiking are stable over time and between resting and actively moving behavioral states.
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