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Input-dependent wave attenuation in a critically-balanced model of cortex.

Xiao-Hu Yan1, Marcelo O Magnasco

  • 1Laboratory of Mathematical Physics, Rockefeller University, New York, New York, United States of America.

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Summary
This summary is machine-generated.

Brain activity exhibits critical system properties. This study shows that a critically-balanced cortical network model naturally integrates sensory information over distances that depend on input strength, aligning with experimental findings.

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience
  • Theoretical Neuroscience

Background:

  • Critical systems theory is increasingly applied to understand brain activity dynamics.
  • The origin of long-range correlations characteristic of criticality in biological neural networks remains unclear.
  • Local connectivity structures are hypothesized to underlie emergent large-scale brain dynamics.

Purpose of the Study:

  • To investigate how long-range susceptibilities arise in a critically-poised cortical model from local connectivity.
  • To determine the effect of external input on spatial correlation range in this model.
  • To explore the implications for input-dependent spatial integration strategies in the brain.

Main Methods:

  • Development and analysis of a dynamically critically-poised computational model of the cortex.
  • Mathematical derivation of susceptibility properties in the absence and presence of input.
  • Comparison of model predictions with experimental data on local field potential activity.

Main Results:

  • The model exhibits infinite long-range susceptibility without external input.
  • Upon input presentation, susceptibility range decreases exponentially with distance, attenuating faster for stronger inputs.
  • This input-dependent spatial integration mechanism mirrors observed sensory processing strategies.

Conclusions:

  • A critically-balanced cortical network model naturally generates input-strength-dependent spatial integration.
  • This mechanism explains how local neural structures can support flexible, input-modulated information processing.
  • The findings suggest criticality is a fundamental principle for adaptive neural computation.