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Updated: Aug 13, 2025
05:01
Inducing Long-Term Plasticity of Intrinsic Neuronal Excitability in Neurons of the Dorsal Lateral Geniculate Nucleus
Published on: September 20, 2024
467
Relating local connectivity and global dynamics in recurrent excitatory-inhibitory networks
1Laboratoire de Neurosciences Cognitives et Computationnelles, INSERM U960, Ecole Normale Superieure-PSL Research University, Paris, France.
Plos Computational Biology
|January 23, 2023
Summary
This study links local neural network connectivity to global network structure, revealing how local statistics predict low-rank connectivity and neural dynamics. It bridges experimental and computational approaches in neuroscience.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Network Science
Background:
- Understanding cortical network connectivity is crucial for explaining neural dynamics and computations.
- Two approaches exist: analyzing local connectivity motifs (biological) and global low-rank structure (artificial networks).
- A direct link between these local and global perspectives is currently missing.
Purpose of the Study:
- To develop a method connecting local connectivity statistics to global low-rank structure.
- To clarify how local statistics and global structure interact to shape low-dimensional neural activity.
- To bridge the gap between biological and artificial neural network perspectives on connectivity.
Main Methods:
- Developed a method to map local connectivity statistics to an approximate global low-rank structure.
- Utilized perturbation theory for random matrices to compute dominant eigenvectors of the connectivity matrix.
- Applied the method to excitatory-inhibitory networks with reciprocal motifs.
Main Results:
- Demonstrated that multi-population networks with local statistics obeying the central limit theorem can be approximated by low-rank connectivity.
- Showed reliable predictions for low-dimensional dynamics and population activity statistics in specific network models.
- Analytically accounted for neuronal activity heterogeneity in specific local connectivity realizations.
Conclusions:
- The developed approach links local connectivity statistics to global network properties and low-dimensional dynamics.
- It disentangles the influence of mean connectivity and reciprocal motifs on recurrent feedback.
- Provides an intuitive framework for understanding how local neural connectivity shapes global network dynamics.
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