The ease with which our brains learn to control our bodies belies intricate neural processing
which remains poorly understood. We know that a network of brain regions
work together in a carefully coordinated fashion to allow us to move from one place
to another. In mammals, we know that the motor cortex plays a central role in this
process, but precisely how its activity contributes to control is a matter of long and
continued debate. In this thesis we demonstrate the need for developing mechanistic
neural network models to address this question. Using such models, we show that contentious
response properties of non-human primate primary motor cortex (M1) neurons
can be understood as reflecting control processes which take into account the physics
of the body. And we develop new computational techniques for teaching neural network
models how to execute control. In the first study (Chapter 2), we critically examined a
recently developed correlation-based descriptive model for characterizing the activity
of M1 neuron activity. In the second study (Chapter 3), we developed neural network
control laws which performed reaching and postural tasks using a physics model of
the upper limb. We show that the population of artificial neurons in these networks exhibit
preferences for certain directions of movement and certain forces applied during
posture. These patterns parallel empirical observations in M1, and the model shows
that the patterns reflect particular features of the biomechanics of the arm. The final
study (Chapter 4) develops new techniques for building network models. To understand
how the brain solves difficult control tasks we need to be able to construct mechanistic
models which can do the same. And, we need to be able to construct controllers that
compute via simple neuron-like units. In this study, we combine tools for automatic computation
of derivatives with recently developed ideas about second-order approaches
to optimization to build better neural network control laws. Taken together, this thesis
helps develop arguments for, and the tools to build mechanistic neural network models
to understand how motor cortex contributes to control of the body. / Thesis (Ph.D, Neuroscience) -- Queen's University, 2014-01-31 10:34:43.816
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OKQ.1974/8606 |
| Date | 31 January 2014 |
| Creators | Lillicrap, Timothy Paul |
| Contributors | Queen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.)) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English, English |
| Detected Language | English |
| Type | Thesis |
| Rights | This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner. |
| Relation | Canadian theses |
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