Scalable, Lightweight, Integrated and Quick-to-Assemble (SLIQ) Hyperdrives for Functional Circuit Dissection
Name
Scalable, lightweight.pdf
Size
4.39 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Liang, Li
Oline, Stefan N.
Kirk, Justin C.
Schmitt, Lukas Ian
Remondes, Miguel
Halassa, Michael M.
Komorowski, Robert
Date Issued
February 2017
Journal
Frontiers in Neural Circuits
Publisher
Frontiers Research Foundation
Citation
Liang, Li; Oline, Stefan N.; Kirk, Justin C.; Schmitt, Lukas Ian; Komorowski, Robert W.; Remondes, Miguel and Halassa, Michael M. “Scalable, Lightweight, Integrated and Quick-to-Assemble (SLIQ) Hyperdrives for Functional Circuit Dissection.” Frontiers in Neural Circuits 11 (February 2017): 8 © 2017 Liang, Oline, Kirk, Schmitt, Komorowski, Remondes and Halassa
Version
Final published version
Abstract
Independently adjustable multielectrode arrays are routinely used to interrogate neuronal circuit function, enabling chronic in vivo monitoring of neuronal ensembles in freely behaving animals at a single-cell, single spike resolution. Despite the importance of this approach, its widespread use is limited by highly specialized design and fabrication methods. To address this, we have developed a Scalable, Lightweight, Integrated and Quick-to-assemble multielectrode array platform. This platform additionally integrates optical fibers with independently adjustable electrodes to allow simultaneous single unit recordings and circuit-specific optogenetic targeting and/or manipulation. In current designs, the fully assembled platforms are scalable from 2 to 32 microdrives, and yet range 1–3 g, light enough for small animals. Here, we describe the design process starting from intent in computer-aided design, parameter testing through finite element analysis and experimental means, and implementation of various applications across mice and rats. Combined, our methods may expand the utility of multielectrode recordings and their continued integration with other tools enabling functional dissection of intact neural circuits.
MIT Department
Picower Institute for Learning and Memory
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/fncir.2017.00008
