Dimensionality Reduction in the Control of the Human Hand

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The goal of this project is to characterize the dimensionality reduction in the control of hand movements and to identify the neural control principles underlying the dimensionality reduction. A comprehensive method is taken that combines non-invasive human experiments with control and information theoretic approaches. The theoretic approaches complement the experiments and bridge different levels of description of neural activities and hand behaviors. The research plan consists of two parts. The first part is to identify the movement primitives, i.e. the fundamental building blocks, of hand movements. An important component of this part is to model the hierarchical neural networks responsible for the formation and implementation of movement primitives. The second part takes an information theoretic method to obtain indication for the flexibility and information capacity of the hand control under peripheral and central constraints. These two parts offer different views of the same problem, but work complementarily to promote the understanding of dimensionality reduction in neural control of the hand. The research is motivated by the neural control of the human hand. The hand has a large number of mechanical degrees-of-freedom, which offers tremendous flexibility for the hand to perform skilled finger movements. On the other hand, however, the flexibility of the hand makes the control problem very challenging. It is still an unsolved mystery how the neural system handle the high dimensionality underlying the hand control. The proposed research aims to promote the understanding of neural organization and mechanism for the dimensionality reduction in the control of hand movements. Study in this direction will be valuable for the design of dexterous robots, brain-like circuits, and brain-machine interfaces with a variety of potential applications in industry, defense, and medicine, for example, in developing prosthetics that may aid stroke victims, handicapped individuals, and those with brain or spinal cord damages. Furthermore, inspired by the neural principles for handling problems of high dimensionality, this research will provide insights for the control of large-scale engineering systems.