Spatial information in large-scale neural recordings

Cybulski TR, Glaser JI, Marblestone AH, Zamft BM, Boyden ES, Church GM, Kording KP (2015) Spatial information in large-scale neural recordings, Frontiers in Computational Neuroscience 8:172.

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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.

Project

Tools for recording brain signaling dynamics

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