让外部输入直接调控神经突触，提升霍普菲尔德网络在混乱输入下的记忆检索能力。
Input-Driven Dynamics for Robust Memory Retrieval in Hopfield Networks
- 输入直接改变突触连接，重塑能量景观以引导记忆检索。
- 在高度混合输入下仍能准确分类，表现优于传统模型。
- 适合研究神经动力学与鲁棒记忆机制的学者参考。
霍普菲尔德模型为理解人脑记忆存储与检索机制提供了数学上理想化但富有洞察力的框架。该模型激发了四十年来关于学习与检索动态、容量估计以及记忆间序列转换的广泛研究。值得注意的是，外部输入的作用及其影响——从神经动力学到有效记忆检索的促进——长期以来被忽视。为填补这一空白，我们提出一种新型动力学系统框架，其中外部输入直接作用于神经突触，塑造霍普菲尔德模型的能量景观。这一基于可塑性的机制为记忆检索过程提供了清晰的能量解释，并在正确分类高度混合输入方面表现出色。此外，我们将该模型整合进现代霍普菲尔德架构中，借此阐明当前与过去信息如何在检索过程中融合。最后，我们将经典模型与新模型嵌入噪声干扰环境中，比较其在记忆检索中的鲁棒性。
原文摘要 · Abstract (English)
The Hopfield model provides a mathematically idealized yet insightful framework for understanding the mechanisms of memory storage and retrieval in the human brain. This model has inspired four decades of extensive research on learning and retrieval dynamics, capacity estimates, and sequential transitions among memories. Notably, the role and impact of external inputs has been largely underexplored, from their effects on neural dynamics to how they facilitate effective memory retrieval. To bridge this gap, we propose a novel dynamical system framework in which the external input directly influences the neural synapses and shapes the energy landscape of the Hopfield model. This plasticity-based mechanism provides a clear energetic interpretation of the memory retrieval process and proves effective at correctly classifying highly mixed inputs. Furthermore, we integrate this model within the framework of modern Hopfield architectures, using this connection to elucidate how current and past information are combined during the retrieval process. Finally, we embed both the classic and the new model in an environment disrupted by noise and compare their robustness during memory retrieval.
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