It has long been believed that the brain is highly modular both in terms of structure and function, although recent evidence has led some to question the extent of both types of modularity. We used artificial neural networks to test the hypothesis that structural modularity is sufficient to guarantee functional specialization, and find that in general, this doesn't necessarily hold except at extreme levels. We then systematically tested which features of the environment and network do lead to the emergence of specialization. We used a simple toy environment, task and network, allowing us precise control, and show that in this setup, several distinct measures of specialization give qualitatively similar results. We further find that (1) specialization can only emerge in environments where features of that environment are meaningfully separable, (2) specialization preferentially emerges when the network is strongly resource-constrained, and (3) these findings are qualitatively similar across different network architectures, but the quantitative relationships depends on the architecture type. Finally, we show that functional specialization varies dynamically across time, and demonstrate that these dynamics depend on both the timing and bandwidth of information flow in the network. We conclude that a static notion of specialization, based on structural modularity, is likely too simple a framework for understanding intelligence in situations of real-world complexity, from biology to brain-inspired neuromorphic systems. We propose that thoroughly stress testing candidate definitions of functional modularity in simplified scenarios before extending to more complex data, network models and electrophysiological recordings is likely to be a fruitful approach.

翻译：长期以来的观点认为，大脑在结构和功能上均具有高度模块性，但近期证据使部分研究者对这两种模块性的程度产生质疑。我们利用人工神经网络验证"结构性模块化足以保证功能性特化"这一假说，发现除极端模块化水平外，该假说通常不成立。进而系统探究环境和网络特征如何促进特化的涌现。通过构建可精确控制的简化环境、任务及网络模型，我们证明在该框架下，多种特化度量指标可获得性质相似的结果。进一步发现：（1）特化仅在环境特征具有可分离意义时涌现；（2）网络面临强资源约束时特化优先出现；（3）上述发现对不同网络架构在性质上保持一致，但量化关系取决于架构类型。最终揭示功能性特化随时间动态变化，且该动态特性取决于网络中信息流的时间与带宽。我们得出结论：基于结构性模块化的静态特化框架，对于理解从生物系统到类脑神经形态系统等真实复杂情境中的智能现象，可能过于简化。我们提出，在将候选的功能性模块化定义推广至更复杂的数据、网络模型及电生理记录之前，先在简化场景中对其施加充分压力测试，或将构成富有成效的研究路径。