标题： 神经表示一致性从概率神经-行为表示对齐中浮现 显示英文标题

标题： Neural Representational Consistency Emerges from Probabilistic Neural-Behavioral Representation Alignment

摘要： 个体大脑表现出显著的结构和生理异质性，然而神经回路可以在个体之间产生相当一致的功能特性，在神经科学中这是一个明显的悖论。 尽管最近的研究通过跨受试者的手动对齐观察到了运动皮层中保存的神经表征，但这种保存的零样本验证及其推广到更多皮层仍未被探索。 我们在此介绍PNBA（概率神经-行为表征对齐），这是一种新的框架，利用概率建模来解决跨试验、跨会话和跨受试者层次变异性的问题，并通过生成约束防止表征退化。 通过建立可靠的跨模式表征对齐，PNBA通过零样本验证揭示了恒河猴初级运动皮层（M1）和背外侧前运动皮层（PMd）中稳健的保存神经表征。 我们进一步在小鼠初级视觉皮层（V1）中建立了类似的表征保存，反映了普遍的神经基础。 这些发现通过建立跨皮层和物种的零样本保存神经表征解决了神经异质性的悖论，丰富了神经编码的见解，并实现了零样本行为解码。 显示英文摘要

摘要： Individual brains exhibit striking structural and physiological heterogeneity, yet neural circuits can generate remarkably consistent functional properties across individuals, an apparent paradox in neuroscience. While recent studies have observed preserved neural representations in motor cortex through manual alignment across subjects, the zero-shot validation of such preservation and its generalization to more cortices remain unexplored. Here we present PNBA (Probabilistic Neural-Behavioral Representation Alignment), a new framework that leverages probabilistic modeling to address hierarchical variability across trials, sessions, and subjects, with generative constraints preventing representation degeneration. By establishing reliable cross-modal representational alignment, PNBA reveals robust preserved neural representations in monkey primary motor cortex (M1) and dorsal premotor cortex (PMd) through zero-shot validation. We further establish similar representational preservation in mouse primary visual cortex (V1), reflecting a general neural basis. These findings resolve the paradox of neural heterogeneity by establishing zero-shot preserved neural representations across cortices and species, enriching neural coding insights and enabling zero-shot behavior decoding.