神经元分层联想网络中的稀疏和密集编码
Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons
摘要：
synfire链是一种简单的神经网络模型，它可以传播被称为脉冲包的稳定同步尖峰，并被广泛研究。然而，synfire链如何在一个网络中共存仍有待阐明。我们已经研究了一个分层的Leaky Integrate-and-Fire神经元的联想网络的活动，在这种连接中，我们通过Hebbian学习嵌入记忆模式。我们用Fokker-Planck方法分析了它们的活性。在我们之前的报告中，当一半的神经元属于每种记忆模式（记忆模式率$F=0.5$）时，网络活动的时间分布在一定的输入条件下被分成时间上聚集的组，称为子格。在这项研究中，我们表明，当网络是稀疏连接（$F<0.5$），同步发射的记忆模式促进。相反，密集连接的网络（$F>0.5$）禁止同步触发。稀疏度和密集度也会影响嵌入记忆模式的吸引域和存储容量。我们证明了稀疏（密集）连接的网络扩大（缩小）了吸引的基础，增加（减少）了存储容量。
英文摘要：
A synfire chain is a simple neural network model which can propagate stable synchronous spikes called a pulse packet and widely researched. However how synfire chains coexist in one network remains to be elucidated. We have studied the activity of a layered associative network of Leaky Integrate-and-Fire neurons in which connection we embed memory patterns by the Hebbian Learning. We analyzed their activity by the Fokker-Planck method. In our previous report, when a half of neurons belongs to each memory pattern (memory pattern rate $F=0.5$), the temporal profiles of the network activity is split into temporally clustered groups called sublattices under certain input conditions. In this study, we show that when the network is sparsely connected ($F<0.5$), synchronous firings of the memory pattern are promoted. On the contrary, the densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness and denseness also effect the basin of attraction and the storage capacity of the embedded memory patterns. We show that the sparsely(densely) connected networks enlarge(shrink) the basion of attraction and increase(decrease) the storage capacity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
