Spatial information in large-scale neural recordings

Thaddeus R. Cybulski*, Joshua I. Glaser, Adam H. Marblestone, Bradley M. Zamft, Edward S. Boyden, George M. Church, Konrad P. Kording

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1-16
Number of pages16
JournalFrontiers in Computational Neuroscience
Volume8
Issue numberJAN
DOIs
StatePublished - Jan 21 2015

Keywords

  • Electrical recording
  • Extracellular recording
  • Fisher information
  • Neural recording
  • Optics
  • Resolution
  • Statistics
  • Technology design

ASJC Scopus subject areas

  • Neuroscience (miscellaneous)
  • Cellular and Molecular Neuroscience

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