AI 中文总结
研究具有有限寿命突触的最小神经网络模型中，完全沉默后自发活动再生机制。通过分析残余突触配置确定LER能力预测多周期动力学，表明瞬态突触记忆可产生多样未来动力学，为活动再生提供解释并提供预测控制神经网络新视角。
AI 中文摘要
瞬态突触记忆已成为即使在没有持续神经元活动的情况下维持短期信息的潜在机制。然而，尚不清楚仅隐藏的突触状态是否包含足够信息来预测活动停止后神经网络的未来演变。在此，我们引入具有有限寿命突触的最小神经网络模型，研究完全神经元沉默后自发活动再生的机制。我们表明，第一个沉默状态下的残余突触配置已经决定了网络活动在单个激活周期后是终止还是自发再生额外周期。通过分析这个突触记忆快照，我们确定了潜在兴奋性募集（LER）能力，以新鲜兴奋性神经元的累积数量量化，作为多周期动力学的近乎完美预测器，而无需继续后续网络模拟。值得注意的是，这些不同的动力学结果出现在其他方面均匀的神经网络中，表明仅瞬态突触记忆就足以产生多样的未来动力学。我们的发现为从残余突触状态再生活动提供了机制解释，并表明短期记忆不仅编码在正在进行的神经元活动中，还编码在保留网络招募新神经元组件能力的潜在突触配置中。更广泛地说，所提出的基于快照的框架为预测和潜在控制神经网络的未来演变提供了新视角。
英文摘要
Transient synaptic memory has emerged as a potential mechanism for maintaining short-term information even in the absence of persistent neuronal activity. However, it remains unclear whether the hidden synaptic state alone contains sufficient information to predict the future evolution of neuronal networks after activity has ceased. Here, we introduce a minimal neuronal network model with finite-lifetime synapses and investigate the mechanism underlying spontaneous activity regeneration following complete neuronal silence. We show that the residual synaptic configuration at the first silent state already determines whether network activity terminates after a single activation cycle or spontaneously regenerates an additional cycle. By analyzing this synaptic-memory snapshot, we identify the Latent Excitatory Recruitment (LER) capacity, quantified by the cumulative number of fresh excitatory neurons, as a near-perfect predictor of multi-cycle dynamics without continuing the subsequent network simulation. Remarkably, these distinct dynamical outcomes emerge in an otherwise homogeneous neuronal network, demonstrating that transient synaptic memory alone is sufficient to generate diverse future dynamics. Our findings provide a mechanistic explanation for activity regeneration from a residual synaptic state and suggest that short-term memory is encoded not only in ongoing neuronal activity but also in the latent synaptic configuration that preserves the network's capacity to recruit new neuronal assemblies. More broadly, the proposed snapshot-based framework offers a new perspective for predicting and potentially controlling the future evolution of neuronal networks.
Comments16 pages, 8 figures. Source code and representative datasets are available on GitHub
