用能量动力学模型实现高效神经计算，提升系统鲁棒性与能效。
Energy-Based Dynamical Models for Neurocomputation, Learning, and Optimization
- 基于能量梯度流构建神经计算模型，模拟生物与人工系统的动态行为
- 在高容量存储与大规模优化任务中表现优于传统方法
- 适合对低功耗、可扩展神经计算系统感兴趣的研究者
控制理论、神经科学与机器学习的交叉进展揭示了动态系统执行计算的新机制。这些进展涵盖广泛的概念、数学与计算思想，应用于模型学习与训练、记忆检索、数据驱动控制及优化。本教程聚焦于受神经科学启发的计算方法，旨在提升各类任务中的可扩展性、鲁棒性与能效，弥合人工与生物系统间的差距。重点在于通过梯度流与能量景观编码信息的能量基动力学模型。从连续时间霍普菲尔德网络与玻尔兹曼机等经典形式出发，延伸至现代发展：用于高容量存储的密集关联记忆模型，用于大规模优化的振荡器网络，以及用于复合与约束重构的近端下降动力学。教程展示控制理论如何指导下一代神经计算系统的设计，推动人工智能研究超越传统的前馈与反向传播范式。
原文摘要 · Abstract (English)
Recent advances at the intersection of control theory, neuroscience, and machine learning have revealed novel mechanisms by which dynamical systems perform computation. These advances encompass a wide range of conceptual, mathematical, and computational ideas, with applications for model learning and training, memory retrieval, data-driven control, and optimization. This tutorial focuses on neuro-inspired approaches to computation that aim to improve scalability, robustness, and energy efficiency across such tasks, bridging the gap between artificial and biological systems. Particular emphasis is placed on energy-based dynamical models that encode information through gradient flows and energy landscapes. We begin by reviewing classical formulations, such as continuous-time Hopfield networks and Boltzmann machines, and then extend the framework to modern developments. These include dense associative memory models for high-capacity storage, oscillator-based networks for large-scale optimization, and proximal-descent dynamics for composite and constrained reconstruction. The tutorial demonstrates how control-theoretic principles can guide the design of next-generation neurocomputing systems, steering the discussion beyond conventional feedforward and backpropagation-based approaches to artificial intelligence.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。