发表机构
McGovern Medical School, UTHealth; Texas Institute for Restorative Neurotechnologies, UTHealth（德克萨斯大学休斯顿健康科学中心麦戈文医学院; 德克萨斯大学休斯顿健康科学中心德克萨斯修复性神经技术研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出神经可容许性纲领（NAP），用语言代数性质约束神经机制，并设计Meld结合操作，在模拟中保持所有不变量并恢复层级结构，从而改变神经实现评估标准。
AI 中文摘要
一个神经系统必须具备什么能力才能实现语言？当前的研究用语言变量标注刺激，并测试哪些电极、体素或语言模型层能预测神经活动。然而，预测成功仍使机制受到欠约束。在此，我们表明语言的代数性质指定了机制必须保持不变的不变量：非结合的分层分组、交换性、递归闭包、对子结构的访问以及结构化工作区转换。我们将此称为神经可容许性纲领（NAP）。句法结构构建被代数地分析，并为每项要求提供了候选机制：内容可寻址的工作区记忆、调度结构构建操作的图结构瞬态动力学（例如稳定异宿通道），以及记录分组的相位耦合密封操作。模拟表明，修正的Marcolli-Berwick熵优化结合门仅在狭窄的承诺带内保持分组。作为替代，我们提出了一种新颖的神经结合操作，称为“Meld”：两个组成群体通过共享突触汇聚，亚线性整合并饱和。据我们所知，Meld是神经上最接近句法合并的合理组成法则。它保持每个NAP不变量，使用已知的皮层操作，并在每个测试深度和温度下恢复层级结构。它预测有效群体维度分离替代括号化，且复合体取决于组成成分的不一致。重要的是，最准确解码括号化的法则先验地不可容许，表明仅解码准确性无法在机制之间做出裁决。通过指定神经动力学如何保持对语言结构的忠实，NAP改变了评估认知的神经实现的标准。
英文摘要
What must a neural system be capable of to implement language? Current research annotates stimuli with linguistic variables and tests which electrodes, voxels, or language-model layers predict neural activity. Yet predictive success leaves mechanisms under-constrained. Here, we show that algebraic properties of language specify invariants that mechanisms must preserve: non-associative hierarchical grouping, commutativity, recursive closure, access to substructures, and structured workspace transitions. We term this the Neural Admissibility Program (NAP). Syntactic structure building is analyzed algebraically, with candidate mechanisms offered for each requirement: content-addressable workspace memory, graph-structured transient dynamics scheduling structure-building operations (e.g. stable heteroclinic channels), and a phase-coupled sealing operation recording grouping. Simulations show that a corrected Marcolli-Berwick entropy-optimized binding gate preserves grouping only within a narrow commitment band. As an alternative, we propose a novel neural binding operation we term 'Meld': two constituent populations converge through shared synapses, integrate sublinearly, and saturate. Meld is, to our knowledge, the closest neurally plausible composition law to syntactic Merge. It preserves every NAP invariant, uses known cortical operations, and recovers hierarchical structure at every tested depth and temperature. It predicts that effective population dimensionality separates alternative bracketings and that the composite depends on constituent disagreement. Importantly, the laws decoding bracketing most accurately are a priori inadmissible, showing that decoding accuracy alone cannot adjudicate between mechanisms. By specifying how neural dynamics can remain faithful to linguistic structure, the NAP changes the criterion by which neural implementations of cognition are evaluated.
