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Transition to chaos in random networks with cell-type-specific connectivity
Johnatan Aljadeff1, Merav Stern2, Tatyana Sharpee1
1Computational Neurobiology Laboratory, The Salk Institute for Biological Studies, La Jolla, California 92037, USA and Center for Theoretical Biological Physics and Department of Physics, University of California, San Diego 92093, USA.
Physical Review Letters
|March 14, 2015
Summary
Neural circuit dynamics transition between silent and chaotic activity based on cell-type specific connections. This finding enhances understanding of neural network computation and random matrix theory.
Area of Science:
- Computational neuroscience
- Theoretical neuroscience
- Neuroscience
Background:
- Neural circuit connectivity is governed by statistical rules that vary with cell-type.
- Understanding these cell-type-specific rules is crucial for deciphering neural network dynamics.
Purpose of the Study:
- To investigate the dynamics of neural networks with cell-type-specific connectivity.
- To explore the phase transition between silent and chaotic activity in such networks.
- To derive new results in random matrix theory related to connectivity matrices.
Main Methods:
- Extension of the dynamic mean-field method to model neural networks with cell-type-specific connectivity.
- Analysis of the phase transition locus.
- Application of random matrix theory to analyze the spectral radius of block-structured random matrices.
Main Results:
- Neural networks with cell-type-specific connectivity exhibit a phase transition between silent and chaotic activity.
- A new result in random matrix theory concerning the spectral radius of block-structured variance matrices was derived.
- A small population of hyperexcitable neurons can drive the network into the chaotic regime, increasing computational capacity.
Conclusions:
- Cell-type-specific connectivity plays a critical role in determining neural network dynamics and computational capacity.
- The study provides a theoretical framework linking neural circuit properties to network activity regimes.
- Findings have implications for understanding how specific neuronal populations can modulate network function.
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