生物启发的概率内存计算硬件学习与决策：第一部分

中文摘要 AI 辅助

动物的学习与决策常被建模为贝叶斯过程，其中感官证据与先验信念相整合，以在不确定性下引导行为。但产生这种能力的内在神经动力学是什么，又如何在计算系统中复制？本摘要讨论了一个基于生物学的框架，其中噪声神经与突触动力学通过从内部能量函数进行随机采样来执行推理与学习，分别通过神经与突触变异性捕捉潜在状态与模型参数的不确定性。这使得预测编码网络等方法能够通过马尔可夫链蒙特卡洛采样来解释认知不确定性。将生物系统中的内在噪声与新兴概率模拟存储技术中的电噪声进行类比，我们强调模拟内存计算硬件自然成为大规模可扩展且能效高的概率推理解决方案。

英文摘要

Learning and decision-making in animals are often modeled as Bayesian processes, where sensory evidence is integrated with prior beliefs to guide behavior in the face of uncertainty. But what are the inherent neural dynamics that give rise to this ability, and how could they be replicated in computing systems? This abstract discusses a biologically grounded framework in which noisy neural and synaptic dynamics perform inference and learning via stochastic sampling from an internal energy function, capturing uncertainty over latent states and model parameters through neural and synaptic variability, respectively. This enables approaches such as predictive coding networks to account for epistemic uncertainty via Markov chain Monte Carlo sampling. Drawing a parallel between intrinsic noise in biological systems and electrical noise in emerging probabilistic analogue memory technologies, we highlight how analogue in-memory computing hardware naturally emerges as the solution for massively scalable and energy-efficient probabilistic inference.

发表机构

• CEA-List（法国原子能委员会电子与信息技术实验室）
• VERSES AI Research Lab（VERSES AI研究实验室）
• PRAESC AI
• TU Wien（维也纳工业大学）

机构由 AI 辅助整理，请以论文原文为准。