Antifragile control systems in neuronal processing: A sensorimotor perspective

TL;DR

This paper proposes a new framework based on antifragility principles to analyze and design neuronal control systems that benefit from uncertainty and volatility, with potential applications in neuromorphic engineering.

Contribution

It introduces antifragile control as a novel conceptual approach for neuronal processing and provides design principles for neuromorphic systems that leverage this property.

Findings

Antifragility can be integrated into neuronal circuits like Homeostatic Regulation and Hebbian learning.

The framework explains how neural systems can benefit from uncertainty and volatility.

Design principles for antifragile neuromorphic systems are proposed.

Abstract

The stability--robustness--resilience--adaptiveness continuum in neuronal processing follows a hierarchical structure that explains interactions and information processing among the different time scales. Interestingly, using "canonical" neuronal computational circuits, such as Homeostatic Activity Regulation, Winner-Take-All, and Hebbian Temporal Correlation Learning, one can extend the behaviour spectrum towards antifragility. Cast already in both probability theory and dynamical systems, antifragility can explain and define the interesting interplay among neural circuits, found, for instance, in sensorimotor control in the face of uncertainty and volatility. This perspective proposes a new framework to analyse and describe closed-loop neuronal processing using principles of antifragility, targeting sensorimotor control. Our objective is two-fold. First, we introduce antifragile control as a conceptual framework to quantify closed-loop neuronal network behaviours that gain from uncertainty and volatility. Second, we introduce neuronal network design principles, opening the path to neuromorphic implementations and transfer to technical systems.