Neural-DISCO：以来源为条件的神经群体活动反事实编辑
Neural-DISCO: Source-Conditioned Counterfactual Editing of Neural Population Activity
arXiv:2610.33145v1 公告类型：cross 摘要：理解神经活动如何编码行为与感觉变量是系统神经科学的一项关键挑战。这需要模型不仅能够从标签预测活动，还要支持以源为条件的反事实编辑：给定一段记录到的源响应，我们应当能够编辑某一个已测量的行为或刺激标签，并预测神经活动将如何变化，同时保留其他已观测标签以及标签无法解释的源特异性变异。在此，我们提出\textbf{Neural-DISCO}，一个用于神经活动反事实建模的解耦表示学习框架。Neural-DISCO 将潜空间划分为若干标签特异性成分，每个成分与一个已观测的行为或刺激标签相关联，并另有一个残差潜变量，用于捕捉未通过已观测标签提供的那部分变异。在模拟与真实神经记录数据集上，我们表明该结构能够对已测量标签进行选择性编辑，从而生成反事实神经响应，同时保留原始活动的其余内容。此外，为深入理解行为与刺激标签如何在单神经元分辨率上被编码，我们将该解耦框架与特征归因方法相结合，以识别哪些神经元对每个标签最为重要。我们发现，在合成数据与真实数据中，解耦均提升了已知功能细胞角色的恢复效果。综上，这些结果支持 Neural-DISCO 作为神经活动反事实建模的框架，并为探究神经群体如何编码行为与感觉信息提供了一个实用接口。代码见 https://github.com/TangLab-UBC/Neural-DISCO-neurips。
把神经活动拆成标签相关成分与残差成分，使研究者能单独改动一个行为或刺激标签并预测群体活动的变化。
arXiv:2610.33145v1 Announce Type: cross Abstract: Understanding how neural activity encodes behavioral and sensory variables is a key challenge in systems neuroscience. This requires models that go beyond predicting activity from labels and instead support source-conditioned counterfactual editing: given a recorded source response, we should be able to edit one measured behavioral or stimulus label and predict how neural activity would change while preserving other observed labels and source-specific variability not explained by the labels. Here, we introduce \textbf{Neural-DISCO}, a disentangled representation learning framework for counterfactual modeling of neural activity. Neural-DISCO partitions the latent space into label-specific components, each associated with an observed behavioral or stimulus label, together with a residual latent that captures variability not supplied through observed labels. Across simulated and real neural recording datasets, we show that this structure enables selective editing of measured labels to generate counterfactual neural responses while preserving the remaining content of the original activity. Further, to gain insight into how behavioral and stimulus labels are encoded at single-neuron resolution, we pair this disentangled framework with feature attribution methods that identify which neurons are most important for each label. We find that disentanglement enhances the recovery of known functional cell roles in both synthetic and real data. Together, these results support Neural-DISCO as a framework for counterfactual modeling of neural activity and provide a practical interface for probing how neural populations encode behavior and sensory information. Code is available at https://github.com/TangLab-UBC/Neural-DISCO-neurips.
中文翻译还没有生成，先显示原文。
来源：arXiv q-bio.NC 预印本 · arxiv.org