标题： 具有分层内在兴奋性调制的循环脉冲网络用于模式学习 显示英文标题

标题： A Recurrent Spiking Network with Hierarchical Intrinsic Excitability Modulation for Schema Learning

摘要： 模式是一种促进迁移学习的结构化知识形式，在神经科学和人工智能（AI）中正引起越来越多的关注。当前神经计算中的模式研究主要局限于单一的行为范式，并且严重依赖缺乏神经合理性和生物解释性的循环神经网络（RNNs）。为解决这些限制，本工作首先构建了一个通用的行为范式框架用于模式学习，并引入了三个新的认知任务，从而支持全面的模式探索。其次，我们提出了一种使用具有分层内在兴奋性调制的循环脉冲神经网络的新模型（HM-RSNNs）。该模型的顶层根据任务特定需求选择兴奋性特性，而底层则对这些特性进行微调以解决任务内问题。最后，进行了广泛的HM-RSNNs可视化分析，以展示其计算优势，跟踪模式学习过程中内在兴奋性的演变，并检查不同任务间的神经协调差异。受生物启发的损伤研究进一步揭示了模式内内在兴奋性的任务特定分布。实验结果表明，HM-RSNNs在所有任务中均显著优于RSNN基线，并在三个新的认知任务中超过了RNNs。此外，HM-RSNNs为模式学习背后的神经动力学提供了更深入的见解。 显示英文摘要

摘要： Schema, a form of structured knowledge that promotes transfer learning, is attracting growing attention in both neuroscience and artificial intelligence (AI). Current schema research in neural computation is largely constrained to a single behavioral paradigm and relies heavily on recurrent neural networks (RNNs) which lack the neural plausibility and biological interpretability. To address these limitations, this work first constructs a generalized behavioral paradigm framework for schema learning and introduces three novel cognitive tasks, thus supporting a comprehensive schema exploration. Second, we propose a new model using recurrent spiking neural networks with hierarchical intrinsic excitability modulation (HM-RSNNs). The top level of the model selects excitability properties for task-specific demands, while the bottom level fine-tunes these properties for intra-task problems. Finally, extensive visualization analyses of HM-RSNNs are conducted to showcase their computational advantages, track the intrinsic excitability evolution during schema learning, and examine neural coordination differences across tasks. Biologically inspired lesion studies further uncover task-specific distributions of intrinsic excitability within schemas. Experimental results show that HM-RSNNs significantly outperform RSNN baselines across all tasks and exceed RNNs in three novel cognitive tasks. Additionally, HM-RSNNs offer deeper insights into neural dynamics underlying schema learning.