Spatial information in large-scale neural recordings
Name
Cybulski-2014-Spatial information.pdf
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6.09 MB
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Author(s)
Cybulski, Thaddeus R.
Glaser, Joshua I.
Zamft, Bradley M.
Kording, Konrad P.
Marblestone, Adam Henry
Church, George M
Boyden, Edward
Date Issued
January 2015
Journal
Frontiers in Computational Neuroscience
Publisher
Frontiers Research Foundation
Citation
Cybulski, Thaddeus R., Joshua I. Glaser, Adam H. Marblestone, Bradley M. Zamft, Edward S. Boyden, George M. Church, and Konrad P. Kording. “Spatial Information in Large-Scale Neural Recordings.” Frontiers in Computational Neuroscience 8 (January 21, 2015).
Version
Final published version
Abstract
To record from a given neuron, a recording technology must be able to separate the activity of that neuron from the activity of its neighbors. Here, we develop a Fisher information based framework to determine the conditions under which this is feasible for a given technology. This framework combines measurable point spread functions with measurable noise distributions to produce theoretical bounds on the precision with which a recording technology can localize neural activities. If there is sufficient information to uniquely localize neural activities, then a technology will, from an information theoretic perspective, be able to record from these neurons. We (1) describe this framework, and (2) demonstrate its application in model experiments. This method generalizes to many recording devices that resolve objects in space and should be useful in the design of next-generation scalable neural recording systems.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Massachusetts Institute of Technology. Media Laboratory
McGovern Institute for Brain Research at MIT
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3389/fncom.2014.00172
