arXiv: 2607.14000

具有瞬态突触记忆的神经元网络中从静默状态的活动再生

Activity Regeneration from Silent States in Neuronal Networks with Transient Synaptic Memory

July 15, 2026

作者: Mozhgan Khanjanianpak, Alireza Valiadeh

q-bio.NCq-bio.NCcond-mat.dis-nncond-mat.stat-mech

摘要

瞬时突触记忆已成为一种潜在机制，可在缺乏持续神经元活动的情况下维持短期信息。然而，尚不清楚仅凭隐藏突触状态是否包含足够信息，以预测神经元网络在活动停止后的未来演化。本文引入一个具有有限寿命突触的最小神经元网络模型，并探究完全神经元静默后自发活动再生的机制。我们证明，首次静默状态下的残留突触构型已能决定网络活动是在单个激活周期后终止，还是自发再生额外周期。通过分析这一突触记忆快照，我们识别出潜伏兴奋性募集（LER）容量（以新鲜兴奋性神经元的累积数量量化），将其作为近乎完美的预测因子，无需继续后续网络模拟即可预测多周期动力学。值得注意的是，这些截然不同的动力学结果出现在原本均质的神经元网络中，表明仅凭瞬时突触记忆就足以产生多样的未来动力学。我们的发现为残留突触状态下的活动再生提供了机制性解释，并提示短期记忆不仅编码于持续的神经元活动中，也编码于保存网络募集新神经元组合能力的潜在突触构型中。更广泛而言，所提出的基于快照的框架为预测并可能控制神经元网络的未来演化提供了新视角。

English

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.