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Fluctuation-response relation unifies dynamical behaviors in neural fields.

C C Alan Fung1, K Y Michael Wong1, Hongzi Mao1

  • 1Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China.

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Neural fields use anticipation to overcome delays in processing dynamic information. This study unifies predictions of tracking behavior by linking it to intrinsic neural field dynamics using fluctuation-response relations.

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Area of Science:

  • Computational neuroscience
  • Theoretical neuroscience
  • Neural dynamics

Background:

  • Neural fields process dynamic information but face challenges due to transmission and processing delays.
  • Anticipatory mechanisms, like inhibitory feedback, are crucial for neural fields to accurately track time-varying stimuli.
  • Existing models often focus on specific mechanisms, lacking a unified theoretical framework.

Purpose of the Study:

  • To develop a unified theoretical framework for understanding anticipation in neural fields.
  • To derive generic fluctuation-response relations applicable to mobile network states.
  • To link the tracking capabilities of neural fields under external stimuli to their intrinsic dynamics.

Main Methods:

  • Derivation of fluctuation-response relations based on the translational symmetry of mobile network states.
  • Analysis of intrinsic neural field dynamics in the absence of external stimuli.
  • Modeling of inhibitory feedback mechanisms contributing to anticipation.

Main Results:

  • Unified predictions for the tracking behavior of neural fields were established.
  • The study provides a direct link between tracking performance and intrinsic neural field dynamics.
  • The derived relations are general and applicable to various anticipatory mechanisms.

Conclusions:

  • Anticipation is a key strategy for neural fields to compensate for inherent delays.
  • The developed fluctuation-response relations offer a powerful tool for analyzing neural field dynamics and predictive capabilities.
  • This work provides a unified theoretical understanding of how neural fields track dynamic information.