In vivo serial patch clamp robotics for cell-type identification in the mouse visual cortex

Electrophysiology;Patch-clamp;Neuroscience;Visual cortex;Layer 5;Robotics;Cell-type;Neuron

Holst, Gregory Leonard ; Forest, Craig R. Mechanical Engineering Boyden, Edward S. Stanley, Garrett B. Sulchek, Todd Zeng, Hongkui Kodandaramaiah, Suhasa B. ; Forest, Craig R.

University:Georgia Institute of Technology

Department:Mechanical Engineering

关键词: Electrophysiology;    Patch-clamp;    Neuroscience;    Visual cortex;    Layer 5;    Robotics;    Cell-type;    Neuron;   

Others  :  https://smartech.gatech.edu/bitstream/1853/56270/1/HOLST-DISSERTATION-2016.pdf

美国|英语

来源: SMARTech Repository

【 摘 要 】

Our ability to probe the immense complexity of the brain, with its approximately 80 billion neurons, is currently limited by the available tools to record and modulate neural activity within intact, functioning, neural circuits. We have yet to develop a complete catalog of all the types of neurons and their basic functions, and to identify the root causes of most nervous system disorders. To achieve a full understanding of the fundamental principles behind brain function, new tools must be developed to increase scale, resolution, and efficiency of neural recording. Here we show the development of robotics tools to investigate the unique behaviors of cell types in layer 5 of the visual cortex of mice and transform the highly manual art of obtaining patch-clamp electrophysiological recordings in vivo into a systematic, automated procedure. The patch clamp technique is the current 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 an 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 highly-skilled 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 across entire brain regions and discover the in vivo function and dysfunction of cells within the nervous system.