Low-dimensional neural features reflect central features of muscle activation
- Zuley Rivera Alvidrez.
- Aug. 2011.
- Physical description
- online resource (xii, 89 pages) : illustrations (some color)
- Rivera Alvidrez, Zuley.
- Newsome, William T. thesis advisor.
- Ng, Andrew Y., 1976- thesis advisor.
- Shenoy, Krishna V. (Krishna Vaughn). thesis advisor (primary).
- Stanford University. Department of Electrical Engineering.
- Stanford University. Committee on Graduate Studies. degree grantor.
- Includes bibliographical references (p. 80-89). 76 refs.
- Any time we move, our brains solve the difficult problem of translating our motor intentions to muscle commands. Understanding how this computation takes place, and in particular, what role the motor cortex plays in movement generation, has been a central issue in systems neuroscience that remains unresolved. In this thesis, we took an unconventional approach to the analysis of cortical neural activity and its relationship to executed movements. We used dimensionality reduction to extract the salient patterns of neural population activity, and related those to the muscle activity patterns generated during arm reaches to a grid of targets. We found that salient neural activity patterns appeared to tightly reflect muscle activity patterns with a biologically-plausible lag. We also applied our analyses to movements that were planned before being executed, and found that a muscle-framework view of the cortical activity was consistent with previously-described predictions of movement kinematics based on the state of the cortical population activity. Overall, our results elucidate remarkable simplicity of the motor-cortical activity at the population level, despite the complexity and heterogeneity of individual cell's activities.
- Publication date
- Submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University.
- Thesis (Ph.D.)--Stanford University, 2011.