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，人们认为大脑在结构和功能上都具有高度模块化特征，但近期证据使部分学者对这两种模块化的程度提出质疑。我们利用人工神经网络检验了"结构模块化足以保证功能特化"这一假设，发现除极端情况外，该假设通常不成立。随后我们系统探究了环境和网络特征如何催生特化现象。通过使用简易的玩具环境、任务和网络以实现精确控制，我们证明在该设置下，多种不同的特化度量指标得出的定性结果具有相似性。进一步研究发现：(1) 特化现象仅出现在环境特征具有显著可分性的场景中；(2) 网络面临强资源约束时更易产生特化；(3) 这些发现虽在不同网络架构中定性相似，但定量关系取决于架构类型。最后我们证明功能特化随时间动态变化，并揭示这些动态取决于网络信息流的时间特性与带宽。由此得出结论：基于结构模块化的静态特化框架，对于理解从生物学到脑启发神经形态系统等现实复杂情境中的智能现象可能过于简化。我们认为，在扩展到更复杂的数据、网络模型及电生理记录之前，先在简化场景中对功能模块化的候选定义进行彻底的压力测试，可能是富有成效的研究路径。