标题： 使用姐妹细胞的关联先验进行推理 显示英文标题

标题： Inference with correlated priors using sisters cells

摘要： 感官处理的一个常见观点是从受体激活中对潜在原因进行概率推理。标准方法通常假设这些原因是先验独立的，但现实世界的生成因素通常是相关的。在神经系统中表示这种结构化的先验提出了架构上的挑战，特别是当代表潜在原因的单元之间直接交互在生物学上不可信或计算昂贵时。 受嗅球结构的启发，我们提出了一种新的电路模式，使得在不需要潜在原因单元之间直接交互的情况下，可以进行相关先验的推理。关键见解在于使用姊妹细胞：接收共享受体输入但连接到局部中间神经元的方式不同。 潜在单元之间所需的相互作用通过它们与姊妹细胞的连接间接实现，从而使得相关连接意味着先验反相关，反之亦然。 我们使用几何论证来构建实现给定先验的连通性，并限制可以构建此类先验的原因数量。 通过模拟，我们证明了这种先验在嘈杂环境中的推理效果，并将其推理动态与实验观察到的结果进行了比较。 最后，我们展示了在潜在表征的某些假设下，可以从前端细胞的激活中推断出使用的先验。 虽然在嗅觉系统中有生物学基础，但我们的机制可以推广到其他自然和人工感官系统，并可能为在相关潜在结构下的高效推理架构设计提供参考。 显示英文摘要

摘要： A common view of sensory processing is as probabilistic inference of latent causes from receptor activations. Standard approaches often assume these causes are a priori independent, yet real-world generative factors are typically correlated. Representing such structured priors in neural systems poses architectural challenges, particularly when direct interactions between units representing latent causes are biologically implausible or computationally expensive. Inspired by the architecture of the olfactory bulb, we propose a novel circuit motif that enables inference with correlated priors without requiring direct interactions among latent cause units. The key insight lies in using sister cells: neurons receiving shared receptor input but connected differently to local interneurons. The required interactions among latent units are implemented indirectly through their connections to the sister cells, such that correlated connectivity implies anti-correlation in the prior and vice versa. We use geometric arguments to construct connectivity that implements a given prior and to bound the number of causes for which such priors can be constructed. Using simulations, we demonstrate the efficacy of such priors for inference in noisy environments and compare the inference dynamics to those experimentally observed. Finally, we show how, under certain assumptions on latent representations, the prior used can be inferred from sister cell activations. While biologically grounded in the olfactory system, our mechanism generalises to other natural and artificial sensory systems and may inform the design of architectures for efficient inference under correlated latent structure.