High-density microelectrode arrays (MEAs) permit the ensemble recording of single neurons which mediate sensory and motor processing, perception, and learning. MEAs have the potential to monitor functional alterations in neuronal circuits within awake, behaving subjects to better undestrand and quantify cortical plasticity. They can also serve as brain machine interfaces to provide real-time control of assistive devices in persons with high level paralysis. The challenge in this project is the ability to extract spike trains of individual neurons and local field potentials of large populations within the constraints imposed by implantability requirements (such as chip size, power dissipation, real-time operation, and limited telemetry bandwidth), which signficantly limits the utility of MEAs in basic neuroscience and clinical applications.
The objective of this project is to develop a scalable, low-power, distributed microsystem to monitor and extract biologically relevant information from high-yield neural data in real-time. Our approach relies heavily on statistical signal processing and is typically validated through advanced models of neuronal signaling and benchmarked against wired data acquistion systems that are commercially available. The intended application of the system under development is geared towards large-scale monitoring of neural activity for basic neuroscience research and clinical Brain Machine Interface applications.