The connection patterns of neural circuits in the brain form a complex network. Collective signalling within the network manifests as patterned neural activity and is thought to support human cognition and adaptive behaviour. Recent technological advances permit macroscale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging and use reservoir computing to implement connectomes as artificial neural networks. We then train these neuromorphic networks to learn a memory-encoding task. We show that biologically realistic neural architectures perform best when they display critical dynamics. We find that performance is driven by network topology and that the modular organization of intrinsic networks is computationally relevant. We observe a prominent interaction between network structure and dynamics throughout, such that the same underlying architecture can support a wide range of memory capacity values as well as different functions (encoding or decoding), depending on the dynamical regime the network is in. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity.

大脑中神经回路的连接模式形成了一个复杂的网络。网络内的集体信号表现为模式化的神经活动，被认为支持人类认知和适应性行为。最近的技术进步允许对生物大脑网络进行大规模重建。这些称为连接组的地图显示了多种非随机的架构特征，包括重尾度分布、隔离社区和密集互连的核心。然而，计算和功能专业化如何从网络架构中出现仍然未知。在这里，我们使用体内扩散加权成像重建人脑连接组，并使用储层计算将连接组实现为人工神经网络。然后我们训练这些神经形态网络来学习记忆编码任务。我们表明，生物学上逼真的神经架构在显示关键动态时表现最佳。我们发现性能是由网络拓扑驱动的，并且内在网络的模块化组织在计算上是相关的。我们观察到网络结构和动态之间的显着交互，因此相同的底层架构可以支持广泛的内存容量值以及不同的功能（编码或解码），具体取决于网络所处的动态状态。这项工作为发现大脑的网络组织如何优化认知能力开辟了新的机会。我们发现性能是由网络拓扑驱动的，并且内在网络的模块化组织在计算上是相关的。我们观察到网络结构和动态之间的显着交互，因此相同的底层架构可以支持广泛的内存容量值以及不同的功能（编码或解码），具体取决于网络所处的动态状态。这项工作为发现大脑的网络组织如何优化认知能力开辟了新的机会。我们发现性能是由网络拓扑驱动的，并且内在网络的模块化组织在计算上是相关的。我们观察到网络结构和动态之间的显着交互，因此相同的底层架构可以支持广泛的内存容量值以及不同的功能（编码或解码），具体取决于网络所处的动态状态。这项工作为发现大脑的网络组织如何优化认知能力开辟了新的机会。