提出可解分子开关模型，实现稳定时序信息处理。
A Solvable Molecular Switch Model for Stable Temporal Information Processing
- 基于线性状态-非线性输入的可解微分方程建模
- 具备收敛性和遗忘记忆特性，支持稳定序列学习
- 适合构建类脑计算中的深层或循环架构
本文研究一种输入驱动的一阶微分方程模型，该模型最初用于实验验证的动态分子开关，其行为类似大脑突触。该模型在状态上线性、输入上非线性，具有解析可解性，并证明其具备收敛性与遗忘记忆等数学性质，使非线性动力系统能够稳定处理时变输入。因此，该模型同时具备生物启发行为与稳定学习所需的数学特性。结果为将动态分子开关作为计算单元，应用于深度级联/分层前馈与递归架构，以及其他更通用结构的类脑计算提供了理论支持。该方法也可启发更多可解模型，以拟合任意物理器件，实现类脑行为与输入信号的稳定计算。
原文摘要 · Abstract (English)
This paper studies an input-driven one-state differential equation model initially developed for an experimentally demonstrated dynamic molecular switch that switches like synapses in the brain do. The linear-in-the-state and nonlinear-in-the-input model is exactly solvable, and it is shown that it also possesses mathematical properties of convergence and fading memory that enable stable processing of time-varying inputs by nonlinear dynamical systems. Thus, the model exhibits the co-existence of biologically-inspired behavior and desirable mathematical properties for stable learning on sequential data. The results give theoretical support for the use of the dynamic molecular switches as computational units in deep cascaded/layered feedforward and recurrent architectures as well as other more general structures for neuromorphic computing. They could also inspire more general exactly solvable models that can be fitted to emulate arbitrary physical devices which can mimic brain-inspired behaviour and perform stable computation on input signals.
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。