标题： 衰减因子考虑了神经网络中包含的活动，而无需分层或模块化组织 显示英文标题

标题： A decaying factor accounts for contained activity in neuronal networks with no need of hierarchical or modular organization

摘要： 在由网络表示的系统中，导致活动竞争的机制在各种现象中至关重要，例如影响神经元网络的癫痫疾病，以及社交网络中的信息传播。 最早用于解释受控活动的模型包括触发和抑制过程，但它们无法应用于抑制过程明显缺失的社交网络。 一个最近的模型表明，只要网络被划分为模块（社区），就可以在不需要抑制过程的情况下实现受控活动。 在本文中，我们引入了一个受海布理论启发的新概念，通过结合基于随机游走机制的衰减活动动态来实现活动竞争，该机制优先于节点活动。 在选择适当的衰减系数范围内，我们在所有测试的网络中观察到了持续的活动，即随机网络、巴比阿利-阿尔伯特网络和地理网络。 通过展示如果基于整合与发放动态的动态包含衰减因子，则模块性不再需要，证实了这一发现的普遍性。 综上所述，这些结果提供了一个原理证明，即在没有任何特定拓扑结构的情况下，持久且受控的网络激活可能发生。 这可能是即使不存在特殊的拓扑组织，神经元活动也不会扩散到整个神经元网络的原因。 显示英文摘要

摘要： The mechanisms responsible for contention of activity in systems represented by networks are crucial in various phenomena, as in diseases such as epilepsy that affects the neuronal networks, and for information dissemination in social networks. The first models to account for contained activity included triggering and inhibition processes, but they cannot be applied to social networks where inhibition is clearly absent. A recent model showed that contained activity can be achieved with no need of inhibition processes provided that the network is subdivided in modules (communities). In this paper, we introduce a new concept inspired in the Hebbian theory through which activity contention is reached by incorporating a dynamics based on a decaying activity in a random walk mechanism preferential to the node activity. Upon selecting the decay coefficient within a proper range, we observed sustained activity in all the networks tested, viz. random, Barabasi-Albert and geographical networks. The generality of this finding was confirmed by showing that modularity is no longer needed if the dynamics based on the integrate-and-fire dynamics incorporated the decay factor. Taken together, these results provide a proof of principle that persistent, restrained network activation might occur in the absence of any particular topological structure. This may be the reason why neuronal activity does not outspread to the entire neuronal network, even when no special topological organization exists.