Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines

One of the main properties of any intelligent system is that it has the capacity to learn. This holds for biological systems, ranging from bacteria and fungi to plants and animals [17, 21, 42, 50], as well as for engineered systems designed by artificial intelligence (AI) researchers [59, 29, 9]. Modern intelligent systems, such as those used in machine learning, typically rely on gradient descent for learning by minimizing error gradients [32, 65, 49]. While gradient-based methods have driven significant advances in AI [29], their reliance on exact gradients, centralized updates, and complex information pathways limits their applicability in biological and neuromorphic systems In contrast, biological learning likely relies on different mechanisms, as organisms often lack the exact gradient information and centralized control that gradient descent requires [31, 68]. Neuromorphic computing, inspired by these principles, aims to replicate the distributed, energyefficient learning of biological systems [38, 40]. However, integrating traditional gradient-based methods into neuromorphic hardware has proven challenging, highlighting a critical gap: the need for gradient-free learning mechanisms that exclusively rely on operations that are local in space and time [24, 12]. To address this, alternative learning principles to gradient descent have been proposed for both rate-based [45, 7, 51, 5, 67] and spike-based models [36, 4, 22, 46]. A class of methods that leverages inherent noise present in biological systems to facilitate learning is perturbation-based methods [57, 69, 66], which adjust the system's parameters based on noise effects and global reinforcement signals, offering gradient-free, local learning suitable for biological or neuromorphic