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. 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 in this setup (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 the different variations of network architectures that we tested, but that the quantitative relationships depend on the precise architecture. 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，人们普遍认为大脑在结构和功能上都具有高度模块化特性，但近期证据使部分研究者对这两种模块化的程度产生质疑。我们利用人工神经网络检验"结构模块化足以保证功能专业化"这一假设，发现该结论通常不成立。接着，我们系统测试了环境与网络的哪些特征能够促使专业化出现。通过采用简单玩具环境、任务和网络架构实现精确控制，发现该设定下多种专业化度量指标得到定性一致的结果。进一步研究表明：(1)专业化仅能在环境特征具有显著可分性时形成；(2)网络面临强资源约束时更易出现专业化；(3)这些定性结论在我们测试的不同网络架构变体中保持稳定，但定量关系取决于具体架构。最后，我们揭示功能专业化存在时变动态特性，并证明该动态特性受网络信息流动时序与带宽的共同影响。我们认为，基于结构模块化的静态专业化框架过于简单，难以解释从生物智能到类脑神经形态系统等真实复杂性场景中的智能现象。建议在将功能模块化的候选定义推广至复杂数据、网络模型与电生理记录之前，先在简化场景中进行充分压力测试，这将是富有前景的研究路径。