arXiv: 2607.14000

具短暫突觸記憶神經元網絡中自靜默狀態之活動再生

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

瞬態突觸記憶已被提出作為一種潛在機制，可在持續神經活動缺失的情況下維持短期資訊。然而，仍不明確的是，在活動停止後，僅憑隱藏的突觸狀態是否包含足夠資訊來預測神經網路未來的演化。在此，我們引入一個具有有限壽命突觸的最小神經網路模型，並探討在完全神經靜默後自發性活動再生之機制。我們證明，在第一個靜默狀態下的殘餘突觸配置，已能決定網路活動是在單一活化週期後終止，還是自發性再生一個額外週期。通過分析此突觸記憶快照，我們識別出潛在興奮性招募（LER）能力——以新鮮興奮性神經元的累積數量量化——作為近乎完美的預測指標，可在無需繼續後續網路模擬的情況下預測多週期動態。值得注意的是，這些截然不同的動態結果出現在一個原本均質的神經網路中，顯示僅憑瞬態突觸記憶便足以產生多樣的未來動態。我們的研究成果為從殘餘突觸狀態進行活動再生提供了機制性解釋，並暗示短期記憶不僅編碼於持續的神經活動中，也編碼於潛在的突觸配置中，該配置保留了網路招募新神經元組合的能力。更廣泛而言，所提出的基於快照的框架為預測並潛在控制神經網路的未來演化提供了新視角。

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.