To acquire statistical regularities from the world, the brain must reliably process, and learn from, spatio-temporally structured information. Although an increasing number of computational models have attempted to explain how such sequence learning may be implemented in the neural hardware, many remain limited in functionality or lack biophysical plausibility. If we are to harvest the knowledge within these models and arrive at a deeper mechanistic understanding of sequential processing in cortical circuits, it is critical that the models and their findings are accessible, reproducible, and quantitatively comparable. Here we illustrate the importance of these aspects by providing a thorough investigation of a recently proposed sequence learning model. We re-implement the modular columnar architecture and reward-based learning rule in the open-source NEST simulator, and successfully replicate the main findings of the original study. Building on these, we perform an in-depth analysis of the model's robustness to parameter settings and underlying assumptions, highlighting its strengths and weaknesses. We demonstrate a limitation of the model consisting in the hard-wiring of the sequence order in the connectivity patterns, and suggest possible solutions. Finally, we show that the core functionality of the model is retained under more biologically-plausible constraints.

中文翻译：

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走向可重复的序列学习模型：基于奖励学习的模块化尖峰网络的复制和分析

为了从世界中获取统计规律，大脑必须可靠地处理时空结构化信息并从中学习。尽管越来越多的计算模型试图解释如何在神经硬件中实现这种序列学习，但许多模型在功能上仍然有限或缺乏生物物理学的合理性。如果我们要收获这些模型中的知识，并对皮层回路的顺序处理有更深入的机械理解，那么模型及其研究结果的可访问性、可重复性和定量可比性至关重要。在这里，我们通过对最近提出的序列学习模型进行彻底研究来说明这些方面的重要性。我们在开源 NEST 模拟器中重新实现了模块化柱状架构和基于奖励的学习规则，并成功复制了原始研究的主要发现。在此基础上，我们对模型对参数设置和基本假设的稳健性进行了深入分析，突出了其优点和缺点。我们证明了该模型的局限性在于连接模式中序列顺序的硬连线，并提出了可能的解决方案。最后，我们表明模型的核心功能在更符合生物学的约束下得以保留。突出其优点和缺点。我们证明了该模型的局限性在于连接模式中序列顺序的硬连线，并提出了可能的解决方案。最后，我们表明模型的核心功能在更符合生物学的约束下得以保留。突出其优点和缺点。我们证明了该模型的局限性在于连接模式中序列顺序的硬连线，并提出了可能的解决方案。最后，我们表明模型的核心功能在更符合生物学的约束下得以保留。