Recurrent neural networks (RNNs) trained on compositional tasks can exhibit functional modularity, in which neurons can be clustered by activity similarity and participation in shared computational subtasks. Unlike brains, these RNNs do not exhibit anatomical modularity, in which functional clustering is correlated with strong recurrent coupling and spatial localization of functional clusters. Contrasting with functional modularity, which can be ephemerally dependent on the input, anatomically modular networks form a robust substrate for solving the same subtasks in the future. To examine whether it is possible to grow brain-like anatomical modularity, we apply a recent machine learning method, brain-inspired modular training (BIMT), to a network being trained to solve a set of compositional cognitive tasks. We find that functional and anatomical clustering emerge together, such that functionally similar neurons also become spatially localized and interconnected. Moreover, compared to standard $L_1$ or no regularization settings, the model exhibits superior performance by optimally balancing task performance and network sparsity. In addition to achieving brain-like organization in RNNs, our findings also suggest that BIMT holds promise for applications in neuromorphic computing and enhancing the interpretability of neural network architectures.

翻译：递归神经网络（RNN）在组合性任务训练中可展现功能模块性，即神经元可根据活动相似性及参与共享计算子任务的能力进行聚类。但与大脑不同，这些RNN不具备解剖模块性——即功能聚类与强递归连接及功能簇空间定位的耦合特性。相较于可能随输入短暂变化的功能模块性，解剖模块化网络能为未来解决相同子任务提供稳健基础。为探究能否培育出类似大脑的解剖模块性，我们采用最新机器学习方法——脑启发模块化训练（BIMT），应用于接受组合性认知任务训练的神经网络。研究发现功能与解剖聚类同步涌现：功能相似神经元同时实现空间定位与互联。此外，相较标准$L_1$正则化或无正则化设置，模型通过最优平衡任务性能与网络稀疏性展现出更优表现。该工作不仅实现了RNN的类脑组织架构，更表明BIMT在神经形态计算及增强神经网络架构可解释性方面具有应用潜力。