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Towards adaptive micro-robotic neural interfaces: Autonomous navigation of microelectrodes in the brain for optimal neural recording


Abstract Advances in implantable MEMS technology has made possible adaptive micro-robotic implants that can track and record from single neurons in the brain. Development of autonomous neural interfaces opens up exciting possibilities of micro-robots performing standard electrophysiological techniques that would previously take researchers several hundred hours to train and achieve the desired skill level. It would result in more reliable and adaptive neural interfaces that could record optimal neural activity 24/7 with high fidelity signals, high yield and increased throughput. The main contribution here is validating adaptive strategies to overcome challenges in autonomous navigation of microelectrodes inside the brain. The following issues pose s... (more)
Created Date 2013
Contributor Anand, Sindhu (Author) / Muthuswamy, Jitendran (Advisor) / Tillery, Stephen H (Committee member) / Buneo, Christopher (Committee member) / Abbas, James (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Subject Biomedical engineering / Neurosciences / Electrical engineering / closed-loop control / MEMS / micro-robotic implants / neural recording / reliability / viscoelastic
Type Doctoral Dissertation
Extent 156 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Bioengineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis