RLE News Articles

Wornell releases new paper: Neural population partitioning and a concurrent brain-machine interface for sequential motor function


Brain-machine interface (BMI) research has largely focused on the problem of restoring lost motor function in individuals. However, a more compelling aim of such research is the development of a truly “intelligent” BMI that can transcend original motor function by considering the higher-level goal of the motor activity and reformulating the motor plan accordingly. This would allow, for example, a task to be performed faster than is possible by natural movement, or more safely or efficiently than originally conceived. Since a typical motor activity consists of a sequence of planned movements, such a BMI must be capable of analyzing the complete sequence before action. As such, its feasibility hinges fundamentally on whether all elements of the motor plan can be decoded concurrently from working memory.

Research reported in the current issue of Nature Neuroscience demonstrates for the first time that development of an “intelligent” BMI is possible using specially designed advanced neural decoding algorithms. This work is part of a multi-disciplinary collaboration between former RLE member Dr. Maryam Shanechi, who has recently earned her doctorate in Electrical Engineering and Computer Science at MIT, RLE investigator and MIT Electrical Engineering and Computer Science Prof. Gregory Wornell, Prof. Emery Brown of the Brain and Cognitive Sciences department at MIT, and neurosurgeon Dr. Ziv Williams at Massachusetts General Hospital.

In their work, the researchers develop and implement a real-time BMI that accurately and simultaneously decodes in advance a sequence of planned movements from neural activity in the premotor cortex. Their research reveals that elements of the motor plan are encoded concurrently during the working memory period prior to movement. Furthermore, experimental results cited in the paper also reveal, interestingly: that the elements of the plan are encoded by largely disjoint subpopulations of neurons; that surprisingly small subpopulations are sufficient for reliable decoding of the motor plan; and that the subpopulation dedicated to one plan element is largely unchanged when a second plan element is added to working memory. These results have significant implications for the architecture and design of future generations of BMIs with enhanced motor function capabilities.