哥伦比亚大学Ashok Litwin-Kumar小组在研究中取得进展。他们开发出连接体约束递归网络中神经活动的预测。相关论文于2025年10月27日发表在《自然—神经科学》杂志上。

在这项研究中，该课题组研究人员开发了一种连接体约束神经网络理论，其中一个“学生”网络被训练来重现一个真实的“老师”的活动，代表一个连接体可用的神经系统。与无约束连接的标准范式不同，这两个网络具有相同的突触权重，但生物物理参数不同，反映了神经元和突触特性的不确定性。研究小组发现，连接组通常不会实质上限制循环网络的动态，这说明了仅从连接推断功能的困难。

然而，来自一小部分神经元的记录可以消除这种退化，在学生中产生与老师一致的动态。他们的理论表明，连接体约束和非约束模型的解空间在性质上是不同的，这决定了何时可以很好地预测这些网络中的活动。它还可以优先记录哪些神经元，以最有效地为此类预测提供信息。

据介绍，最近的技术进步使得测量大型神经回路或整个大脑的突触接线图或“连接组”成为可能。然而，这些数据在多大程度上约束了神经动力学和功能模型是有争议的。

附：英文原文

Title: Prediction of neural activity in connectome-constrained recurrent networks

Author: Beiran, Manuel, Litwin-Kumar, Ashok

Issue&Volume: 2025-10-27

Abstract: Recent technological advances have enabled measurement of the synaptic wiring diagram, or ‘connectome’, of large neural circuits or entire brains. However, the extent to which such data constrain models of neural dynamics and function is debated. In this study, we developed a theory of connectome-constrained neural networks in which a ‘student’ network is trained to reproduce the activity of a ground truth ‘teacher’, representing a neural system for which a connectome is available. Unlike standard paradigms with unconstrained connectivity, the two networks have the same synaptic weights but different biophysical parameters, reflecting uncertainty in neuronal and synaptic properties. We found that a connectome often does not substantially constrain the dynamics of recurrent networks, illustrating the difficulty of inferring function from connectivity alone. However, recordings from a small subset of neurons can remove this degeneracy, producing dynamics in the student that agree with the teacher. Our theory demonstrates that the solution spaces of connectome-constrained and unconstrained models are qualitatively different and determines when activity in such networks can be well predicted. It can also prioritize which neurons to record to most effectively inform such predictions.

DOI: 10.1038/s41593-025-02080-4

Source: https://www.nature.com/articles/s41593-025-02080-4