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Antifragile control systems in neuronal processing: a sensorimotor perspective.

Cristian Axenie1

  • 1Department of Computer Science and Center for Artificial Intelligence, Technische Hochschule Nürnberg Georg Simon Ohm, Keßlerplatz 12, 90489, Nuremberg, Germany. cristian.axenie@th-nuernberg.de.

Biological Cybernetics
|February 15, 2025
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Summary

This study introduces antifragile control, a framework where neural circuits gain from uncertainty. Canonical circuits like Homeostatic Activity Regulation can achieve antifragility, enhancing sensorimotor control.

Keywords:
AntifragilityControl systemsNeuronal networksUncertaintyVolatility

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Control Theory

Background:

  • Neuronal processing exhibits a stability-robustness-resilience-adaptiveness continuum across hierarchical time scales.
  • Canonical neuronal circuits (Homeostatic Activity Regulation, Winner-Take-All, Hebbian Learning) can be extended towards antifragility.
  • Antifragility, rooted in probability theory and dynamical systems, explains neural circuit interplay under uncertainty.

Purpose of the Study:

  • To introduce antifragile control as a framework for quantifying closed-loop neuronal network behaviors that benefit from uncertainty and volatility.
  • To propose neuronal network design principles for implementing antifragility in neuromorphic systems and technical applications.
  • To analyze and describe closed-loop neuronal processing within sensorimotor control using antifragility principles.

Main Methods:

  • Conceptual framework development for antifragile control.
  • Analysis of canonical neuronal computational circuits (Homeostatic Activity Regulation, Winner-Take-All, Hebbian Temporal Correlation Learning).
  • Application of probability theory and dynamical systems principles to model antifragility in neural circuits.

Main Results:

  • Demonstration that canonical neuronal circuits can exhibit antifragile properties.
  • Establishment of antifragility as a framework to understand neural circuit behavior under uncertainty and volatility.
  • Identification of design principles for antifragile neuronal networks.

Conclusions:

  • Antifragile control offers a novel perspective for analyzing neuronal processing, particularly in sensorimotor control.
  • The proposed framework facilitates the design of neuromorphic systems and technical control systems that benefit from uncertainty.
  • Extending canonical circuits towards antifragility enhances their performance in volatile environments.