发表机构
Sichuan University（四川大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究现代神经网络自适应问题，提出可访问性可塑性原则，通过基于关系的操作实现形式化，建立重用优先层次结构，经序列学习任务验证，可减少能力修改并保持性能，为动态神经网络提供基础。
AI 中文摘要
现代神经网络主要通过在预定义计算结构内修改参数来适应。虽然最近的方法引入了模块化、条件计算和参数高效适应，但它们通常没有将计算能力与计算可访问性作为单独的自适应变量区分开来。这项工作引入了可访问性可塑性，这是一种自适应计算原则，系统不仅可以通过改变存在的计算来适应，还可以通过重新组织现有计算之间的交互和参与来适应。我们通过基于关系的操作实现形式化了可访问性可塑性，并建立了一个重用优先的适应层次结构，其中可访问性修改先于成本更高的能力和结构变化。对序列学习任务的概念验证评估表明，可访问性适应可以在保持可比任务性能的同时减少能力修改。这些结果表明可访问性是一个独特的自适应维度，并为未来计算关系随环境变化而演变的动态神经系统提供了基础。
英文摘要
Modern neural networks primarily adapt through parameter modification within predefined computational structures. While recent methods introduce modularity, conditional computation, and parameter-efficient adaptation, they generally do not distinguish computational capability from computational accessibility as separate adaptive variables. This work introduces Accessibility Plasticity, a principle of adaptive computation in which systems adapt not only by changing what computation exists, but also by reorganizing which existing computations can interact and participate. We formalize Accessibility Plasticity through a relationship-based operational realization and establish a reuse-first hierarchy of adaptation, where accessibility modification precedes more costly capability and structural changes. A proof-of-concept evaluation on sequential learning tasks shows that accessibility adaptation can reduce capability modification while maintaining comparable task performance. These results suggest accessibility as a distinct adaptive dimension and provide a foundation for future dynamic neural systems whose computational relationships evolve with changing environments.
Comments22 pages, 6 figures, 2 tables
