The brain network is notably cost-efficient, while the fundamental physical and dynamic mechanisms underlying its economical optimization in network structure and activity have not been determined. In this study, we investigate the intricate cost-efficient interplay between structure and dynamics in biologically plausible spatial modular neuronal network models. We observe that critical avalanche states from excitation-inhibition balance under modular network topology with less wiring cost can also achieve lower costs in firing but with strongly enhanced response sensitivity to stimuli. We derived mean-field equations that govern the macroscopic network dynamics through a novel approximate theory. The mechanism of low firing cost and stronger response in the form of critical avalanches is explained as a proximity to a Hopf bifurcation of the modules when increasing their connection density. Our work reveals the generic mechanism underlying the cost-efficient modular organization and critical dynamics widely observed in neural systems, providing insights into brain-inspired efficient computational designs.

中文翻译：

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少即是多：布线经济的模块化网络支持自持发射经济的神经雪崩，以实现高效处理

大脑网络具有显着的成本效益，而其在网络结构和活动方面的经济优化背后的基本物理和动态机制尚未确定。在这项研究中，我们研究了生物学上合理的空间模块化神经元网络模型中结构和动力学之间复杂的成本效益相互作用。我们观察到，模块化网络拓扑下激发-抑制平衡的临界雪崩状态具有较少的布线成本，也可以实现较低的发射成本，但对刺激的响应灵敏度大大增强。我们通过一种新的近似理论推导出了控制宏观网络动力学的平均场方程。临界雪崩形式的低触发成本和更强响应的机制被解释为在增加其连接密度时接近模块的 Hopf 分叉。我们的工作揭示了在神经系统中广泛观察到的具有成本效益的模块化组织和关键动力学背后的通用机制，为大脑启发的高效计算设计提供了见解。