EFRI BRAID: Neurally Inspired, Resilient Closed Loop Feedback Control of Learned Motor Dynamics

点击翻译按钮获取中文摘要

This project addresses a critical unmet challenge in the emerging field of brain machine interfaces (BMIs). By directly mapping brain activity to movement and speech, BMI technology holds great potential for restoring movement and communication abilities lost to injury or disease. Existing BMIs only operate well for specific tasks and situations, however, and restoration capability remains far from normal levels of skill in hand movements and speech. For example, BMIs for restoring hand movement can pick up and move objects in controlled laboratory settings, but may fail catastrophically when a new object is introduced. Likewise, BMIs that partially restore speech and communication are limited to vocabularies of only dozens of words, or require words to be spelled out letter-by-letter. To overcome these challenges and advance beyond the current state-of-the-art, this project develops BMIs that combine recent, major advances in the understanding of how the brain controls complex movement. The main goal is to deliver better BMI algorithms to predict intended movement and speech from brain activity. The project uses neuroscience-based simulations of real brain activity to rapidly prototype BMI algorithms that are then tested experimentally to advance knowledge of how the brain controls movement. This approach will accelerate the development of BMIs that restore movement and speech ability to the level of an able-bodied person. To broaden participation in the computational neuroscience community, the principal investigators will, in parallel to the research, host workshops for undergraduate students to learn and apply computational neuroscience skills. To provide educational resources and connect students to the broader scientific community, a showcase of students’ work along with the workshop materials will be made publicly available. The primary objective of this project is to apply recent theoretical advances in the understanding of biological motor control to develop BMI decoding strategies for rapid and effective continual learning, enabling robust control across multiple behavioral contexts. These efforts build upon feedforward neural network decoders that have demonstrated initial success in controlled environments. Existing algorithms will be augmented with theoretically and empirically motivated features of the biological motor system. BMI algorithm design is accelerated in silico by incorporating validated modular recurrent neural networks (mRNNs). By simulating brain dynamics in the motor areas from which the BMI measures neural activity, mRNNs emulate the user as a BMI controller. The second objective evaluates algorithm innovations against existing nonhuman primate (NHP) and songbird neurophysiology data. These offline, post hoc analyses test the algorithms’ capacity to infer intended motor behavior in open loop. The third objective evaluates and validates developed strategies with in vivo experiments in NHP and songbirds. New experiments will collect data for a wider set of behavioral context manipulations to further test the learning and generalization capacity of designed algorithms. Critically, NHP experiments will validate algorithms during closed loop BMI control. By co-developing neuroscience theory and algorithms across species and contexts, the proposed work has the potential to develop and validate superior BMI decoding algorithms and to uncover generalized principles for developing machine learning algorithms that interact with intelligent controllers with extreme energy efficiency and flexibility.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.