Simonov A Iu, Pimashkin A S, Kazantsev V B
Biofizika. 2010 Mar-Apr;55(2):317-25.
A three-layer network model of oscillatory associative mermory is proposed. The network is capable to store binary images that can be retrieved if an appropriate stimulus has been applied. Binary images are encoded in the form of the spatial distribution of oscillatory phase clusters in-phase (+1) and anti-phase relative to the base periodic signal. The information is loaded into the network using a set of interlayer connection weights. A condition for error-free pattern retrieval has been obtained, which imposes a certain limitation on the maximal number of patterns to be stored in the memory (storage capacity). It has been shown that the capacity can be significantly increased by the generation of optimal pattern alphabet (basic pattern set). The number of stored patterns can reach values of the network size (the number of oscillators in the layer), which is significantly higher than the capacity of traditional oscillatory memory models. The dynamical and information characteristics of the retrieval process based on the optimal alphabet including the estimations of attraction basins and the admissible input pattern discrepancy for error-free retrieval have been investigated.
提出了一种振荡联想记忆的三层网络模型。该网络能够存储二进制图像，若施加适当刺激，这些图像便可被检索出来。二进制图像以相对于基本周期信号同相（+1）和反相的振荡相位簇的空间分布形式进行编码。信息通过一组层间连接权重加载到网络中。已获得无差错模式检索的条件，这对存储在存储器中的最大模式数量（存储容量）施加了一定限制。研究表明，通过生成最优模式字母表（基本模式集），容量可显著增加。存储模式的数量可达到网络规模的值（层中振荡器的数量），这明显高于传统振荡记忆模型的容量。已研究了基于最优字母表的检索过程的动态和信息特征，包括吸引盆的估计以及无差错检索的可允许输入模式差异。
