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Related Experiment Video

Updated: May 3, 2026

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Examining Local Network Processing using Multi-contact Laminar Electrode Recording

Published on: September 8, 2011

Local active information storage as a tool to understand distributed neural information processing.

Michael Wibral¹, Joseph T Lizier², Sebastian Vögler³

¹MEG Unit, Brain Imaging Center, Goethe University Frankfurt am Main, Germany.

Frontiers in Neuroinformatics

|February 7, 2014

Summary

This summary is machine-generated.

Researchers measured local active information storage (LAIS) in cat brains, finding it reflects neural properties like stimulus preference and surprise. This new measure offers insights into cortical function and predictive coding theories.

Keywords:

complex systems distributed computation information storage local information dynamics neural dynamics predictive coding visual system voltage sensitive dye imaging

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Published on: June 22, 2017

Area of Science:

Neuroscience
Computational Neuroscience
Information Theory

Background:

Information processing in neural systems is fundamental but lacks precise mathematical definitions for operations like storage, transfer, and modification.
Recent advances defined these operations locally in space and time, enabling application to artificial neural systems.
Measuring these dynamics in biological neural data, however, remained an open challenge.

Purpose of the Study:

To measure the space-time dynamics of local active information storage (LAIS) in biological neural data for the first time.
To investigate how LAIS relates to neural properties and cognitive concepts in the visual cortex.
To assess the utility of LAIS as a tool for testing theories of cortical function.

Main Methods:

Utilized voltage-sensitive dye imaging data from area 18 of the cat visual cortex.
Applied newly defined mathematical framework for local active information storage to neural recordings.
Analyzed the relationship between measured LAIS and stimulus properties, including unexpected changes.

Main Results:

Local active information storage was successfully measured in vivo in cat neural data.
LAIS demonstrated correlation with neural stimulus preferences and responses to surprising stimulus changes.
In area 18, LAIS reflected the perception of a continuous stimulus despite local randomness.

Conclusions:

Local active information storage is a measurable quantity in biological neural systems.
LAIS provides a novel window into neural information processing, reflecting stimulus-specific and abstract representations.
This metric holds promise for advancing theories of cortical computation, such as predictive coding.