Jie Zhang
Massachusetts Institute of Technology
Picower Institute for Learning and Memory
Title: Highspeed image sensor and systems for resolving millisecond scale neural dynamics underlying behavior.
Abstract: Over the last century, advancements in neural recording technologies have significantly enhanced our ability to capture high-fidelity neural activity during behavior. This has enabled neuroscience to progress from studying the stimulus-response of individual neurons to deciphering how neural networks compute and control behaviors, leading to many groundbreaking discoveries. To study increasingly sophisticated aspects of brain function, there are strong and consistent demands for recording technology with even higher spatiotemporal resolution that can also accommodate diverse and complex animal behaviors.
Here, I will discuss our work on advancing current neural recording technologies, particularly in fluorescence imaging. Using enhanced state-of-the-art designs, we have successfully imaged voltage from neurons across the whole-brain volume of a larval zebrafish at millisecond resolution, and have developed a miniature, light-weight acquisition platform for high-bandwidth imaging and electrophysiology acquisition in freely moving mice. I will also describe how our new CMOS image sensor with pixel-wise programmable exposures (“PE-CMOS”) addresses the fundamental trade-off of speed, signal-to-noise-ratio (SNR), spatial and temporal resolution during high-speed acquisition of fluorescence signals to enable broad application of large-scale, high-speed, voltage imaging in freely behaving animals.
I will conclude by outlining the plan to combine these innovations to optically resolve spiking events and temporal coding of neuron ensembles while allowing for naturalistic behavior in rodents and larval zebrafish — two widely used model systems in neuroscience research. I will also share the experimental plan using these systems to investigate the cell-type specific neural computations underlying complex behaviors such as learning and memory consolidation.
Bio: Jie (Jack) Zhang is a Research Scientist at the Picower Institute for Learning and Memory and the Department of Brain and Cognitive Sciences at MIT, working with Dr. Matthew Wilson. His research focuses on novel neurotechnology development and the study of neural mechanisms underlying spatial memory consolidation. He received his B.S. and Ph.D. degrees in Electrical and Computer Engineering from Johns Hopkins University in 2010 and 2016, working with Dr. Ralph Etienne-Cummings, and was also an international scholar at IMEC in Belgium. His Ph.D. research focuses on computational image sensor design and integrated circuits for neural electrophysiology. He also builds open-source tools for the neuroscience community and is a key contributor to Open-Ephys, an open-source electrophysiology non-profit organization.
