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Updated: Jun 23, 2026
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Targeted Labeling of Neurons in a Specific Functional Micro-domain of the Neocortex by Combining Intrinsic Signal and Two-photon Imaging
Published on: December 12, 2012
Intrinsic chaos control in cortical circuits: A minimal E-I-M rate model for primary visual cortex
Mehdi Borjkhani1, Morteza A Sharif2, Hadi Borjkhani3
1International Centre for Translational Eye Research (ICTER), Institute of Physical Chemistry, Polish Academy of Sciences, Warsaw, Poland. mborjkhani@ichf.edu.pl.
Journal of Computational Neuroscience
|June 22, 2026
Summary
Cortical circuits operate near chaos, controlled by feedback mechanisms. This study models how feedback stabilizes neural activity, enabling flexible yet reliable brain function.
Area of Science:
- Computational neuroscience
- Systems neuroscience
- Neural dynamics
Background:
- Cortical circuits display variable yet bounded activity, hinting at operation near chaotic regimes.
- Understanding the mechanisms controlling neural dynamics is crucial for explaining brain function.
Purpose of the Study:
- To develop a minimal model of primary visual cortex (V1) circuits that explains how feedback mechanisms control neural chaos.
- To investigate the role of specific biological modifications in stabilizing V1 activity.
Main Methods:
- Developed a three-variable rate model based on the Lotka-Volterra system.
- Incorporated biologically motivated modifications: E→I feedback, homeostatic regulation, and orientation-tuned input.
- Analyzed model dynamics, compared results to V1 phenomena, and performed parameter space analysis.
Main Results:
- The model transformed chaotic dynamics into controlled limit cycles, reducing variance by 93%.
- Successfully reproduced V1 phenomena: orientation selectivity, stimulus-induced variability quenching, and realistic spiking irregularity.
- Feedback mechanisms were shown to stabilize activity across a wide parameter range.
Conclusions:
- Canonical feedback motifs in cortical circuits actively suppress intrinsic chaotic capacity.
- Brain function is positioned at an edge of instability, balancing computational flexibility with reliable processing.
- Specific disinhibition nonlinearities are key to achieving chaos-bounded dynamics.
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