Antifragile control systems in neuronal processing: A sensorimotor perspective

TL;DR

The paper tackles how neuronal processing in sensorimotor loops can benefit from uncertainty and volatility, beyond traditional robustness and resilience. It proposes antifragile control as a framework anchored in canonical neural circuits HAR, WTA, and HL to quantify and design closed-loop behavior across multiple timescales. Key contributions include defining intrinsic, inherited, and induced antifragility, linking them to neuron- and population-level mechanisms, and outlining pathways to neuromorphic implementations. The work offers a conceptual foundation for analyzing neural systems under uncertainty with practical implications for engineering robust, adaptive, and proactive controllers.

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.

Figures (6)

• Figure 1: Intrinsic antifragility core mechanism based on Homeostatic Activity Regulation (HAR). The other within-population dynamics (i.e. Winner-Takes-All) and between-population (i.e. Correlation Learning) impact local dynamics, such that the neuron activation is a superposition of multiple sources with inherent own noise, distribution, and reliability properties.
• Figure 2: Intrinsic antifragility: feedback control loop for intrinsic antifragility components. The Controller implements computational mechanisms specific to Homeostatic Activity Regulation (HAR) and its interaction with the single-neuron model dynamics.
• Figure 3: Inherited antifragility core based on Winner-Take-All within-population dynamics. Tuning curve modulation determines capacity building and sensorimotor input data distribution used in the competition and cooperation mechanisms acting between the neurons, whose activity is HAR controlled.
• Figure 4: Inherited antifragility: feedback control loop for inherited antifragility components. The Controller implements computational mechanisms specific to neural population competition and cooperation (i.e. Winner-Take-All circuit) and its interaction within a neural population.
• Figure 5: Induced antifragility core based on Hebbian Correlation Learning dynamics that capture and exploit temporal correlation of the sensorimotor input streams coded in the two interacting neuronal populations coding for sensory or motor quantities.