神经元处理中的反脆弱控制系统：感觉运动视角
Antifragile control systems in neuronal processing: A sensorimotor perspective
AI总结：
本文从感觉运动视角提出反脆弱控制框架，利用反脆弱性原理分析闭环神经元网络在不确定性和波动性中获益的行为，并引入网络设计原则以推动神经形态实现与技术转移。
摘要：
神经元处理中的稳定性-鲁棒性-弹性-适应性连续体遵循分层结构，解释了不同时间尺度间的交互与信息处理。有趣的是，利用“规范”神经元计算回路，如稳态活动调节、胜者通吃和Hebbian时间相关性学习，可将行为谱扩展至反脆弱性。反脆弱性已在概率论和动力系统中得到阐述，能够解释并定义神经回路间有趣的相互作用，例如在面临不确定性和波动性时的感觉运动控制。本视角提出了一个新框架，利用反脆弱性原理分析和描述以感觉运动控制为目标的闭环神经元处理。我们的目标有两方面。首先，我们将反脆弱控制作为概念框架引入，用于量化从不确定性和波动性中获益的闭环神经元网络行为。其次，我们引入神经元网络设计原则，为神经形态实现和向技术系统转移开辟道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
