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Updated: May 28, 2025
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3D Modeling of Dendritic Spines with Synaptic Plasticity
Published on: May 18, 2020
6.8K
Exploring the role of synaptic plasticity in the frequency-dependent complexity domain
Monserrat Pallares Di Nunzio1, Juan Martín Tenti2, Marcelo Arlego1,3
1Instituto de Física de La Plata (IFLP), Universidad Nacional de La Plata, CONICET CCT-La Plata, La Plata 1900, Argentina.
Chaos (Woodbury, N.Y.)
|February 11, 2025
Summary
This study reveals hidden patterns in neuronal plasticity using information theory and computational models. Understanding these dynamics aids in diagnosing and treating cognitive disorders like Alzheimer's disease.
Area of Science:
- Neuroscience
- Computational Biology
- Information Theory
Background:
- Neocortical memory relies on neuronal plasticity, crucial for learning and long-term memory.
- Understanding plasticity mechanisms is vital for diagnosing and treating cognitive disorders (e.g., Parkinson's, epilepsy, Alzheimer's).
Purpose of the Study:
- To explore neuronal dynamics underlying plasticity using an expanded neuronal model.
- To uncover hidden patterns in neuronal activity and their relation to cognitive function.
Main Methods:
- Utilized information-theoretic measures: Bandt-Pompe's entropy-complexity (H×C) and Fisher entropy-information (H×F) planes.
- Applied the Hénon map to model nonlinear neural behaviors and firing patterns.
- Integrated local field potential (LFP) and intracranial electroencephalogram (iEEG) data across multiple frequency bands.
Main Results:
- Revealed hidden patterns in neuronal activity indicative of plasticity dynamics.
- Demonstrated the trade-off between stability and unpredictability in neural networks.
- Connected computational models with experimental data, including higher-order interactions like action potential triplets.
Conclusions:
- The study advances the understanding of synaptic adjustments and their role in neuronal complexity.
- Provides novel insights into the computational mechanisms of memory and cognitive disorders.
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