Long Term Memory and the Densest K-Subgraph Problem
Long Term Memory and the Densest K-Subgraph Problem
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DOI：
10.4230/lipics.itcs.2018.57
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
R. Legenstein;W. Maass;C. Papadimitriou;S. Vempala
中科院分区：
文献类型：
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作者：
R. Legenstein;W. Maass;C. Papadimitriou;S. Vempala
在最近的一项实验中，人类内侧颞叶（MTL）中编码一种感觉刺激的细胞，在将两种感觉刺激结合起来之后，开始对第二种刺激做出反应。我们开发了一个理论模型，预测具有异常高突触内连通性的细胞组合可以出现，以响应特定的感官体验，编码和抽象该体验。我们还表明，两个这样的集合被修改，以增加他们的交集后，一个感官事件，关联两个相应的刺激。使用的主要技术工具是随机图论和伯努利近似。组装创建必须克服类似于den最密集K-Subgraph问题的计算挑战，即从大量随机且稀疏互连的单元中选择具有异常高密度互连的子集。在我们的模型中，我们确定了有助于实现这一成就的三种机制：(1)简单的两阶段随机算法；(2)突触连接中的“三角形完成偏差”和“生日悖论”；(3)这些连接的强度通过Hebbian可塑性得到增强。
In a recent experiment, a cell in the human medial temporal lobe (MTL) encoding one sensory stimulus starts to also respond to a second stimulus following a combined experience associating the two. We develop a theoretical model predicting that an assembly of cells with exceptionally high synaptic intraconnectivity can emerge, in response to a particular sensory experience, to encode and abstract that experience. We also show that two such assemblies are modified to increase their intersection after a sensory event that associates the two corresponding stimuli. The main technical tools employed are random graph theory, and Bernoulli approximations. Assembly creation must overcome a computational challenge akin to the Densest K-Subgraph problem, namely selecting, from a large population of randomly and sparsely interconnected cells, a subset with exceptionally high density of interconnections. We identify three mechanisms that help achieve this feat in our model: (1) a simple two-stage randomized algorithm, and (2) the "triangle completion bias" in synaptic connectivity and a "birthday paradox", while (3) the strength of these connections is enhanced through Hebbian plasticity.