Antifragile control systems in neuronal processing: a sensorimotor perspective

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.