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 intelligent systems in situations of real-world complexity. 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：长期以来人们普遍认为大脑在结构和功能上具有高度模块化特性，尽管近期证据使部分学者对两种模块化的程度产生质疑。我们采用人工神经网络验证"结构模块化足以保证功能专门化"的假设，发现除极端情况外，该假设通常不成立。随后我们系统检验了环境和网络特征中哪些因素促使专门化的产生。通过使用可精确控制的简易玩具环境、任务与网络，我们发现该设置下多个不同专门化度量指标呈现定性相似的结果。研究进一步表明：(1)专门化仅能在环境特征具有可分离性的条件下出现；(2)当网络面临强资源约束时专门化更易形成；(3)虽不同网络架构的定性结果相似，但定量关系取决于架构类型。最后我们证实功能专门化随时间动态变化，并证明这种动态性取决于网络中信息流的时间节奏与带宽。我们认为基于结构模块化的静态专门化概念，在理解真实世界复杂场景下的智能系统时可能过于简单化。提出在向更复杂数据、网络模型及电生理记录推广前，先在简化场景中对功能模块化的候选定义进行充分压力测试，可能是产生丰硕成果的研究路径。