Spatial information in large-scale neural recordings
Author(s)Cybulski, Thaddeus R.; Glaser, Joshua I.; Zamft, Bradley M.; Church, George M.; Kording, Konrad P.; Boyden, Edward Stuart; Marblestone, Adam Henry; ... Show more Show less
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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.
DepartmentMassachusetts 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)
Frontiers in Computational Neuroscience
Frontiers Research Foundation
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).
Final published version