Memory is an important cognitive function for humans. How a brain with such a small power can complete such a complex memory function, the working mechanism behind this is undoubtedly fascinating. Engram theory views memory as the co-activation of specific neuronal clusters. From the perspective of graph theory, nodes represent neurons, and directed edges represent synapses. Then the memory engram is the connected subgraph formed between the activated nodes. In this paper, we use subgraphs as physical carriers of information and propose a parallel distributed information storage algorithm based on node scale in active-directed graphs. An active-directed graph is defined as a graph in which each node has autonomous and independent behavior and relies only on information obtained within the local field of view to make decisions. Unlike static directed graphs used for recording facts, active-directed graphs are decentralized like biological neuron networks and do not have a super manager who has a global view and can control the behavior of each node. Distinct from traditional algorithms with a global field of view, this algorithm is characterized by nodes collaborating globally on resource usage through their limited local field of view. While this strategy may not achieve global optimality as well as algorithms with a global field of view, it offers better robustness, concurrency, decentralization, and bioviability. Finally, it was tested in network capacity, fault tolerance, and robustness. It was found that the algorithm exhibits a larger network capacity in a more sparse network structure because the subgraph generated by a single sample is not a whole but consists of multiple weakly connected components. In this case, the network capacity can be understood as the number of permutations of several weakly connected components in the network.

翻译：记忆是人类重要的认知功能。大脑以如此微小的功耗完成如此复杂的记忆功能，其背后的工作机制无疑令人着迷。印迹理论将记忆视为特定神经元集群的共激活。从图论角度看，节点代表神经元，有向边代表突触，那么记忆印迹就是激活节点之间形成的连通子图。本文以子图作为信息的物理载体，提出一种基于节点规模的主动有向图并行分布式信息存储算法。主动有向图定义为每个节点具有自主独立行为、仅依赖局部视野内获取的信息进行决策的图。与用于记录事实的静态有向图不同，主动有向图像生物神经网络一样去中心化，不存在拥有全局视野并能控制每个节点行为的超级管理者。与传统具有全局视野的算法不同，该算法的特点是节点通过有限的局部视野在资源使用上实现全局协作。虽然该策略在全局最优性上可能不及具有全局视野的算法，但它具有更好的鲁棒性、并发性、去中心化和生物可行性。最后，在网络容量、容错性和鲁棒性方面进行了测试。研究发现，该算法在更稀疏的网络结构中表现出更大的网络容量，因为单个样本生成的子图并非整体，而是由多个弱连通分量组成。此时，网络容量可理解为网络中若干弱连通分量的排列数。