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Updated: Apr 17, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Spatial information in large-scale neural recordings.

Thaddeus R Cybulski¹, Joshua I Glaser¹, Adam H Marblestone²

¹Department of Physical Medicine and Rehabilitation, Rehabilitation Institute of Chicago, Northwestern University Chicago, IL, USA.

Frontiers in Computational Neuroscience

|February 6, 2015

Summary

This summary is machine-generated.

This study introduces a Fisher information framework to assess neural recording technology feasibility. It establishes theoretical bounds for precisely localizing neural activity, crucial for designing scalable neural recording systems.

Keywords:

electrical recording extracellular recording fisher information neural recording optics resolution statistics technology design

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Published on: October 19, 2011

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Last Updated: Apr 17, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

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Published on: July 1, 2018

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

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Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat

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Published on: October 19, 2011

Area of Science:

Neuroscience
Biophysics
Information Theory

Background:

Accurate neural recordings require separating individual neuron activity from neighboring cells.
Existing technologies face challenges in achieving high spatial resolution for neural signal isolation.

Purpose of the Study:

To develop a theoretical framework for evaluating the feasibility of neural recording technologies.
To establish criteria for determining if a technology can uniquely localize neural activity.

Main Methods:

Utilized a Fisher information-based approach.
Combined measurable point spread functions and noise distributions.
Derived theoretical bounds on the precision of neural activity localization.

Main Results:

The framework provides theoretical bounds on the precision of neural recording.
Demonstrated the framework's application through model experiments.
Identified conditions under which neural recording technologies are feasible.

Conclusions:

The developed framework offers an information-theoretic perspective on neural recording capabilities.
This method is generalizable to various spatial resolution recording devices.
The approach is valuable for designing next-generation scalable neural recording systems.