CRCNS Research Proposal: Modeling neural dynamics of naturalistic movements across contexts

University Of Southern California, Los Angeles CA

Investigators

Abstract

Everyday tasks, such as tying shoelaces, throwing a ball, reaching for a cup involve performing complex, require precise and coordinated hand and arm movements in various behavioral contexts. How our brain controls these rich movements; that is, the neural basis of motor control, remains elusive. Investigating this neural basis not only will advance our understanding of brain function, but also will have broad translational implications for developing brain-machine interfaces to restore function in disabled patients and for understanding neurological disorders such as Parkinson’s disease and devising deep brain stimulation therapies. This proposal investigates how a population of neurons in the motor cortex of the brain controls movements by utilizing motor experiments that record simultaneously from large motor cortical areas including thousands of neurons, and by leveraging computational tools that can uncover behaviorally relevant structure in the complex neural population data. This research involves a very close collaboration between computational and experimental methods. The experimental component incorporates a variety of different behavioral conditions that produce a rich repertoire of movements, while recording from large populations of motor cortical neurons in an animal model. The primary focus of this project is on the relationship between movement kinematics (the trajectory of motion) and the temporal dynamics of these large neuronal populations in motor and premotor cortex. To study this relationship, we analyze the computational component that characterizes neural population dynamics using a low-dimensional state variable that is directly related to behaviorally relevant movement kinematics. This is achieved by leveraging linear dynamical modeling methods that can identify low-dimensional behaviorally relevant state dynamics in motor cortical populations, examining how these states change, and modeling local and input dynamics during behavior. Integrating these computational and experimental components, will advance our understanding of the dynamical principles underlying how motor cortical population activity gives rise to rich movements and has the potential to lead to breakthroughs in restoring motor function in patients with impaired movement. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.