具有对称序列处理的前馈分层神经网络模型中的周期二循环
Period-two cycles in a feed-forward layered neural network model with symmetric sequence processing
摘要：
在一个精确可解的前馈分层神经网络模型中研究了主导顺序相互作用的影响，该模型由一个Hebbian部分和一个对称顺序项组成。得到了定态的相图，并发现了周期为2的循环关联态的新相位，对于弱Hebbian项，与凝聚模式的数目$c$无关。
英文摘要：
The effects of dominant sequential interactions are investigated in an exactly solvable feed-forward layered neural network model of binary units and patterns near saturation in which the interaction consists of a Hebbian part and a symmetric sequential term. Phase diagrams of stationary states are obtained and a new phase of cyclic correlated states of period two is found for a weak Hebbian term, independently of the number of condensed patterns $c$.
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。
