  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.
