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Updated: Jun 16, 2025
08:42
Monocular Visual Deprivation and Ocular Dominance Plasticity Measurement in the Mouse Primary Visual Cortex
Published on: February 8, 2020
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State-dependent complexity of the local field potential in the primary visual cortex.
Rafael M Jungmann1, Thaís Feliciano1, Leandro A A Aguiar1,2,3
1Departamento de Física, <a href="https://ror.org/047908t24">Universidade Federal de Pernambuco</a>, 50670-901 Recife-PE, Brazil.
Physical Review. E
|August 20, 2024
Summary
This study reveals that local field potential (LFP) statistical complexity reflects cortical states and layers in rodent brains. This complexity measure offers insights into neuronal activity and behavioral states.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Information Theory
Background:
- Local field potential (LFP) is a key measure of neural activity in brain circuits.
- Understanding LFP dynamics across different cortical states over time remains a challenge.
- Existing biophysical models for LFP in cortical circuits are established but lack temporal state-dependent analysis.
Purpose of the Study:
- To investigate the relationship between LFP statistical complexity and cortical states.
- To explore the sensitivity of LFP complexity to neuronal spiking variability and cortical layers.
- To apply information quantifiers to characterize behavioral states in freely moving animals.
Main Methods:
- Utilized a symbolic information approach to analyze LFP data.
- Employed Jensen disequilibrium measure and Shannon entropy to quantify statistical complexity.
- Recorded LFP from the primary visual cortex (V1) of urethane-anesthetized rats and freely moving mice.
- Correlated LFP complexity with measures of neuronal spiking variability and cortical layers.
Main Results:
- Established consistent relations between LFP recordings and measures of cortical states at the neuronal level.
- Demonstrated that LFP statistical complexity is sensitive to cortical state, specifically spiking variability.
- Showed that LFP complexity also varies significantly across different cortical layers.
- Found indirect relationships between behavioral states of freely moving mice and neuronal spiking variability using these quantifiers.
Conclusions:
- LFP statistical complexity, analyzed via information theory, provides a sensitive marker for cortical states and layers.
- This approach offers a novel way to link macroscopic LFP signals to underlying neuronal dynamics.
- The findings contribute to a deeper understanding of brain function and state transitions in both anesthetized and behaving animals.
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