Neural activity patterns for motor control and motor learning [electronic resource]

The cerebellum is one of the few regions in the mammalian brain where the ability to learn complex transformations from sensory input to motor output can be understood at the level of circuits. As the sole output neurons of the cerebellar cortex, Purkinje cells are central to this process. However, many questions remain regarding how information is transferred from Purkinje cells spikes to downstream circuits to affect behavior, how Purkinje cell spikes instruct plasticity downstream, and how plasticity at multiple sites—both outside the cerebellum and at synaptic inputs to Purkinje cells—interact in the context of the complete circuit to implement learning. In this dissertation, I examine the role of neural activity in Purkinje cells for both driving movements and contributing to learning. I first address the question of whether Purkinje cell spike pattern affects motor behavior in addition to spike rate (Chapter 2). Previous models predict that irregular Purkinje cell spikes should less effectively control target neurons than regular spikes. However, convergent evidence from recording and optogenetic stimulation approaches indicated that spike irregularity does not affect motor output in this system. A biophysical model of the Purkinje cell to deep cerebellar nucleus synapse extended the experimental results by demonstrating that asynchronous synaptic transmission should also be independent of spike pattern at this synapse, and, more generally, delineated a range of parameters for which the fine temporal structure of spikes does or does not matter for downstream circuits. Purkinje cells not only drive ongoing movement, but are also critical for both instructing and undergoing plasticity. In Chapter 3 I use a data-driven modelling approach to consider how multiple sites of plasticity contribute to learning, given different assumptions about the unknown strength of feedback loops. Circuit models with weak or no positive feedback could explain all available data, both before and after learning, and the results reconcile conflicting previous models of the circuit underlying learning of the vestibulo-ocular reflex (VOR). In Chapters 4 and 5, I return to experimental study of the role of Purkinje cells in supporting motor learning. The classic Marr-Albus-Ito model of cerebellar learning proposes that climbing fiber input triggers PF-PC LTD, but the specific conditions that recruit this form of plasticity during learning are unknown. In Chapter 4, I analyze recordings of Purkinje cell activity during VOR learning to reveal a tight, single trial correlation between a climbing fiber spike on one trial, and an adaptive decrease in Purkinje cell simple spike activity on the next, which was precisely timed to compensate for the processing delays of visual error feedback. In addition to undergoing plasticity, Purkinje cells also instruct plasticity at downstream sites through increases in their simple spike output. In Chapter 5, I investigate whether decreases in Purkinje simple spike output can instruct motor learning outside of the cerebellar cortex. Using optogenetic suppression of Purkinje cells expressing archaerhodopsin-2, I found that correctly timed suppression could induce learned increases in VOR gain in the absence of the visual error signals that normally drive learning, and that this form of learning depended primarily on a site of plasticity outside of the cerebellar cortex. Finally, in Chapter 6 I describe a magnetic eye tracking technique used in the experiments of Chapters 2 and 5. Magnetic eye tracking in mice was developed to overcome limitations of previous eye tracking methods, enabling high spatial and temporal resolution measurements of eye movements, including in unrestrained animals.

Advisor: Raymond Jennifer L. primary advisor. Thesis advisor
Chen Lu (Professor of neurosurgery) advisor. Thesis advisor
Goldman Mark
Huguenard John advisor. Thesis advisor