用里德堡气体模拟神经网络临界性和资源动态
Simulating neural network criticality and resource dynamics with Rydberg gases
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中文总结 AI 辅助
该研究利用超冷里德堡气体模拟神经网络临界性及资源动态，通过强调激发传播与突触连接相似性探索临界性标准，实施受控增益机制，发现临界点和活跃相的相关现象，确立其为研究神经网络相关特性的平台。
中文摘要 AI 辅助
神经网络的高效运行与其潜在的非平衡激发动力学中的临界性有关。然而，由于生物系统中控制有限和采样不足，获得这一猜想的实验证据仍然具有挑战性。在此，我们使用超冷里德堡气体作为高度可控的模拟器，对神经网络临界性进行实验探索。我们强调了通过里德堡促进的激发传播与神经元尖峰活动的突触连接的相似性，产生了不同的吸收相和活跃相。我们系统地探索并解决了临界性标准，包括激发雪崩的幂律缩放和通用雪崩形状坍塌的出现。关键的是，我们实施了一种受控增益机制来补偿原子损失，模拟代谢资源补充并将系统稳定在受控的非平衡稳态。我们在临界点发现了峰值时间相关性，在活跃相中发现了带有龙王雪崩的随机振荡，这与对接近临界性的系统的预测一致。我们的工作将促进型里德堡气体确立为研究神经网络临界性、资源动态和突发振荡的平台。
英文摘要
Efficient operation of neural networks has been linked to criticality in their underlying non-equilibrium excitation dynamics. However, obtaining experimental evidence of this conjecture remains challenging due to limited control and undersampling in biological systems. Here, we experimentally explore neural network criticality using an ultracold Rydberg gas as a highly controllable simulator. We highlight the similarity of the excitation spreading via Rydberg facilitation and the synaptic connection of spiking activity of neurons, giving rise to distinct absorbing and active phases. We systematically explore and resolve criticality criteria, including power-law scaling of excitation avalanches and the emergence of universal avalanche shape collapse. Crucially, we implement a controlled gain mechanism to compensate for atom loss, mimicking metabolic resource replenishment and stabilizing the system in a controlled non-equilibrium steady state. We find peak temporal correlations at the critical point and stochastic oscillations with dragon king avalanches in the active phase, consistent with predictions for systems orbiting criticality. Our work establishes facilitated Rydberg gases as a platform for investigating criticality, resource dynamics, and emergent oscillations in neural networks.
