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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Advisor: Craig R. Forest, PhD (Georgia Institute of Technology)
Committee:
Edward S. Boyden, PhD (Massachusetts Institute of Technology)
Hongkui Zeng, PhD (Allen Institute for Brain Science)
Garrett B. Stanley, PhD (Georgia Institute of Technology)
Todd Sulchek, PhD (Georgia Institute of Technology)
Suhasa B. Kodandaramaiah, PhD (University of Minnesota)
“In Vivo Serial Patch Clamp Robotics for Cell-Type Identification in the Mouse Visual Cortex”
In 2013, President Obama announced the Brain Initiative to fund the development of new tools for studying the brain and to identify the root causes of nervous system disorders. Our knowledge of the brain is currently limited by our ability to record the dynamic activity of neurons in intact, behaving circuits. Here we show the development of robotics tools to investigate the unique behaviors of neurons in the visual cortex of mice and transform the highly manual art of obtaining patch clamp recordings into a systematic, automated procedure.
The patch clamp technique is the gold standard for recording the intracellular electrical activity of individual cells and has the highest resolution and specificity of any other technique. However, the manual methods used to control the position, pressure, and voltage of the glass recording pipette severely limit the throughput and the ability to perform multiple simultaneous recordings in vivo. This work shows the development of automation systems to precisely and repeatably prepare the recording pipette, position it in the brain, establish the recording, and conduct the entire electrophysiological experiment all without requiring the presence of a human operator. The robot has autonomously obtained multiple, consecutive recordings in vivo with the same quality and throughput as a human operator. Robotic hardware and software algorithms enable parallel scaling for increased throughput, systematic operation, and rapid dissemination of challenging techniques. These tools will increase our capacity to rapidly identify new cell-type classification schemes and understand the in vivo function and dysfunction of cells within the nervous system.
