Osako Yuma, Buschman Timothy J, Sur Mriganka
Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
Laboratory for Haptic Perception and Cognitive Physiology, RIKEN Center for Brain Science, Saitama 351-0198, Japan.
Res Sq. 2025 Sep 19:rs.3.rs-7420993. doi: 10.21203/rs.3.rs-7420993/v1.
Complex behaviors are thought to be built by combining simpler cognitive components. Computational modeling has shown that artificial neural networks can perform a variety of tasks by flexibly combining small functional modules of neurons, each specialized for a specific computation, to construct a complex task. However, empirical evidence for such reusable modular networks in the brain has been lacking. Here, we show that mice performing a delayed match-to-sample with delayed report (DMS-dr) task reuse subspaces of neural activity that were specialized for stimulus processing and memory maintenance. These subspaces were reused during the task to represent new stimulus inputs and different types of memories, respectively. Clustering analyses showed each subspace was supported by a functionally distinct cluster of neurons in medial prefrontal cortex (mPFC) and posterior parietal cortex (PPC). Recurrent neural networks trained to capture the observed neural dynamics demonstrated that silencing specific clusters disrupted specific computations, highlighting the modular and reusable organization of these networks. By bridging theoretical predictions with empirical evidence, our findings suggest that the brain can flexibly reuse computational components to perform a complex cognitive task.
复杂行为被认为是由更简单的认知成分组合而成。计算模型表明，人工神经网络可以通过灵活组合神经元的小功能模块来执行各种任务，每个模块专门用于特定的计算，从而构建复杂任务。然而，大脑中这种可重复使用的模块化网络的经验证据一直缺乏。在这里，我们表明，执行延迟样本匹配与延迟报告（DMS-dr）任务的小鼠会重复使用专门用于刺激处理和记忆维持的神经活动子空间。这些子空间在任务期间被重复使用，分别代表新的刺激输入和不同类型的记忆。聚类分析表明，每个子空间由内侧前额叶皮层（mPFC）和顶叶后皮层（PPC）中功能不同的神经元簇支持。经过训练以捕捉观察到的神经动力学的循环神经网络表明，沉默特定簇会破坏特定计算，突出了这些网络的模块化和可重复使用的组织。通过将理论预测与经验证据联系起来，我们的研究结果表明，大脑可以灵活地重复使用计算成分来执行复杂的认知任务。
