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Oscillatory neural associative memories with synapses based on memristor bridges

机译:基于忆阻器桥的具有突触的振荡神经联想记忆

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An approach to the implementation of electronic associative memories with tunable weights based on the resistor bridges containing memristors—a bidirectional associative memory (BAM) and an associative memory based on the Hopfield network—is proposed. These memories we implement as a networks of coupled phase oscillators. The conditions for the use of the operational amplifier in a comparator mode for implementing the step activation function are determined. It is shown how to use the CMOS transistors switches to control the memristance value. The experiments using LTSPICE models show that for the reference binary images with size 3 × 3 the proposed networks converges to the reference images (and, accordingly, to their inversion) with a random uniform distribution of binary pixel values of the input images. In all experiments we have no error states in spite of the number of reference patterns exceeds the classical estimations for traditional BAM and Hopfield networks.
机译:提出了一种基于包含忆阻器的电阻桥实现权重可调的电子关联存储器的方法,即双向关联存储器(BAM)和基于Hopfield网络的关联存储器。我们将这些存储器实现为耦合相位振荡器的网络。确定在比较器模式下使用运算放大器实现阶跃激活功能的条件。显示了如何使用CMOS晶体管开关来控制忆阻值。使用LTSPICE模型进行的实验表明,对于尺寸为3×3的参考二进制图像,所建议的网络收敛于参考图像(并因此转换为参考图像),并且输入图像的二进制像素值随机均匀分布。在所有实验中,尽管参考模式的数量超过了传统BAM和Hopfield网络的经典估计,但我们没有错误状态。

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