从关联网络理论推导出可解释的神经学习规则
Neural Learning Rules from Associative Networks Theory
- 基于多时间尺度能量网络构建生成函数
- 通过记忆依赖生成函数还原赫布学习机制
- 提出含学习的动态记忆框架，适合神经科学与模型可解释性研究
关联网络理论正日益为人工神经网络的更新规则提供解释工具。然而，从坚实理论上推导神经学习规则仍是根本挑战。本文通过考虑连续神经元和突触的广义能量型关联网络，并在多个时间尺度上演化，利用时间尺度分离，推导出一个极限情形：在此情形下，神经激活、系统能量及神经动力学均可由一个生成函数完全确定。当允许该生成函数依赖于记忆时，我们恢复了传统赫布学习中神经元间连接强度的建模方式。最后，我们提出了一个记忆动态机制，使该框架能够纳入学习过程，进一步拓展其理论适用性。
原文摘要 · Abstract (English)
Associative networks theory is increasingly providing tools to interpret update rules of artificial neural networks. At the same time, deriving neural learning rules from a solid theory remains a fundamental challenge. We make some steps in this direction by considering general energy-based associative networks of continuous neurons and synapses that evolve in multiple time scales. We use the separation of these timescales to recover a limit in which the activation of the neurons, the energy of the system and the neural dynamics can all be recovered from a generating function. By allowing the generating function to depend on memories, we recover the conventional Hebbian modeling choice for the interaction strength between neurons. Finally, we propose and discuss a dynamics of memories that enables us to include learning in this framework.
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。