Retrieving infinite numbers of patterns in a spin-glass model of immune networks
Retrieving infinite numbers of patterns in a spin-glass model of immune networks
复制标题
DOI：
10.1209/0295-5075/117/28003
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发表时间：
2017-01-01
期刊：
影响因子：
1.8
通讯作者：
Tantari, D.
中科院分区：
文献类型：
--
作者：
Agliari, E.;Annibale, A.;Tantari, D.
神经网络和(适应性)免疫网络之间的相似性早在几十年前就已为人所知，但到目前为止，我们还不清楚免疫系统与联想神经网络不同，它允许免疫系统并行回忆和执行大量记忆中的防御策略的机制。事实证明，原因在于网络拓扑。神经元通常与大量其他神经元相互作用，而免疫网络中淋巴细胞之间的相互作用是非常特殊的，并用有限连通性的图来描述。在本文中，我们使用复制技术来求解一个具有“协调器分支”(T-细胞)和“效应器分支”(B-细胞)的统计机械免疫网络模型，并展示了有限的连通性如何使协调器能够同时管理大量的效应器，甚至超过逾渗阈值(其中克隆串扰是不可忽略的)。其基本的拓扑稀疏性的结果是，适应性免疫系统只表现出弱的遍历破坏，因此也存在自发的开关样效应，即双稳定：后者可能在维持免疫动态平衡方面发挥重要作用。版权所有(C)Epla，2017
The similarity between neural and (adaptive) immune networks has been known for decades, but so far we did not understand the mechanism that allows the immune system, unlike associative neural networks, to recall and execute a large number of memorized defense strategies in parallel. The explanation turns out to lie in the network topology. Neurons interact typically with a large number of other neurons, whereas interactions among lymphocytes in immune networks are very specific, and described by graphs with finite connectivity. In this paper we use replica techniques to solve a statistical mechanical immune network model with "coordinator branches" (T-cells) and "effector branches" (B-cells), and show how the finite connectivity enables the coordinators to manage an extensive number of effectors simultaneously, even above the percolation threshold (where clonal cross-talk is not negligible). A consequence of its underlying topological sparsity is that the adaptive immune system exhibits only weak ergodicity breaking, so that also spontaneous switch-like effects as bi-stabilities are present: the latter may play a significant role in the maintenance of immune homeostasis. Copyright (C) EPLA, 2017