This project aims to develop new algorithms and design tools for the design of and experimentation with novel systems for optimized neuromodulation. Neuromodulation devices are becoming one of the most powerful tools for the treatment of brain disorders, enhancing neurocognitive performance, and demonstrating causality. A precise neuromodulation system integrates neural activity monitoring, real-time neural decoding, and neuromodulation. In precise neuromodulation, a decoding device predicts a behavior variable based on neural data streams in real-time. Based on the decoding results, neuromodulation parameters such as timing, frequency, duration, and amplitude are changed. Precise neuromodulation systems with closed-loop real-time feedback are superior to the fixed (open-loop) neuromodulation paradigm.
Real-time neural decoding centers on predicting behavior based on neural activity data at a pace that reliably keeps up with the behavior that is being monitored. Real-time neural decoding is a critical component of precise neuromodulation systems that are closed-loop and adjust neural stimulation parameters based on feedback from the neural decoding device. Such precise neuromodulation systems with closed-loop real-time feedback are personalized. They provide more effective treatments than fixed and open-loop neuromodulation systems.
Miniature cellular imaging optically records activities of hundreds of neurons in freely behaving animals with cellular spatial resolution and sub-second temporal resolution. Miniature cellular imaging is one of the most powerful ways to study neural circuits and is rapidly revolutionizing the interrogation of neural circuit activity under behavior. Real-time neural decoding for miniature cellular imaging captures the central vision of the neuroscience, in that it has the potential to deliver unprecedented capability for monitoring neural activity, linking it to behavior, and performing precise neuromodulation.
However, miniature cellular imaging generates massive amounts of high-dimensional spatiotemporal data. Real-time signal processing in this context is very challenging due to the speed of data generation, the need for processing to keep with the arriving data, and the complexity of the underlying algorithms. In this project, we are addressing these challenges to enable the design and deployment of more effective devices and experimental platforms for neural activity monitoring and neuromodulation.
We refer to this project as the BCNM Project. BCNM stands for [B]altimore / [C]ollege Park Neuro[M]odulation; the acronym is derived from the locations of the two University of Maryland campuses that are involved in the project.
A list of publications from the BCNM project can be found on the BCNM Project Publications Page.
This research is supported in part by the National Institutes of Health (NIH) / National Institute of Neurological Disorders and Stroke (NINDS) under Grant No. R01NS110421 and the Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative.
This webpage was last updated on January 14, 2024.