NCS-FO: Collaborative Research: Optoelectronic Tools for Closed-Loop Neuron Ensemble Recording and Control during Complex Behaviors

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Brain science will benefit from the capabilities in tracing complex animal behaviors down to ensembles of individual neurons, and moreover establishing a real-time closed-loop brain-interface, ideally with deep brain access in a free-moving animal. This research project aims to address the focus areas in this National Science Foundation program by merging novel neurotechnology, evaluation of neural circuits during performance of complex cognitive behaviors, and large-scale neuron ensemble analysis and closed-loop behavioral control. The outcome of this research will result in new technologies and computational tools that can be used across the field of neuroscience and behavior, strengthening research efforts of multiple research groups. The educational objectives of this proposal are aimed at training and inspiring young engineers and scientists who are equipped with the multidisciplinary background required to help define the future trajectory of brain interfaces and data sciences. The broader impacts of this project include: 1) advancing transformative device technologies for next-generation neurotechnology and providing new and more powerful tools for neuroscience studies, 2) educating underrepresented undergraduate and graduate researchers to contribute to the nation's workforce needs in biotechnology, 3) contributing to the K-12 science, technology, engineering, and mathematics education through weekend seminars and mentoring student-teacher pairs from local middle/high schools; and 4) promoting neuroscience and neurotechnology among local senior citizens and support groups for neurological diseases. The research objective of this proposal is to combine high precision optoelectronic neural probes with real-time neural decoding to feedback optogenetic control over animal behavior. Such closed-loop neural interface will establish a generalizable technology platform to study complex animal behaviors using optogenetic tools and real-time learning. The proposed work will open ample research opportunities and form connections among hardware engineering, cognitive neuroscience, and data science. The intellectual merit of the proposed work will be evidenced by three major contributions: 1) demonstration of high-precision optogenetic brain interface that combines multiplexed recording from and bi-directional control over neuron ensembles, 2) demonstration of closed-loop brain interface that employs real-time neural decoding and adaptive learning to control animal behavior, and 3) characterization of complex decision-making using high-precision, multiplexed data linking multiple brain areas.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.