Memristor is a nanoscale circuit element with nonvolatile, binary, multilevel and analog states. Its conductance (resistance) plasticity is similar to biological synapses. Information sparse coding is considered as the key mechanism of biological neural systems to process mass complex perception data, which is applied in the fields of signal processing, computer vision and so on. This paper proposes a soft-threshold adaptive sparse coding algorithm named MMN-SLCA based on the memristor, neural network and sparse coding theory. Specifically, the memristor crossbar array is used to realize the dictionary set. And by leveraging its unique vector–matrix operation advantages and biological synaptic characteristic, two key compositions of the sparse coding, namely, pattern matching and lateral neuronal inhibition are realized conveniently and efficiently. Besides, threshold variability further enhances the adaptive ability of the intelligent sparse coding. Furthermore, a hardware implementation framework of the sparse coding algorithm is designed to provide feasible solutions for hardware acceleration, real-time processing and embedded applications. Finally, the application of MMN-SLCA in image super-resolution reconstruction is discussed. Experimental simulations and result analysis verify the effectiveness of the proposed scheme and show its superior potentials in large-scale low-power intelligent information coding and processing.

中文翻译：

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基于忆阻神经网络的自适应稀疏编码及其应用。

忆阻器是具有非易失，二进制，多电平和模拟状态的纳米级电路元件。它的电导（电阻）可塑性类似于生物突触。信息稀疏编码被认为是生物神经系统处理大量复杂感知数据的关键机制，它应用于信号处理，计算机视觉等领域。本文基于忆阻器，神经网络和稀疏编码理论，提出了一种软阈值自适应稀疏编码算法MMN-SLCA。具体而言，忆阻器交叉开关阵列用于实现字典集。通过利用其独特的矢量矩阵操作优势和生物学突触特性，稀疏编码的两个关键组成部分即：模式匹配和横向神经元抑制可​​方便，有效地实现。此外，阈值可变性进一步增强了智能稀疏编码的自适应能力。此外，设计了稀疏编码算法的硬件实现框架，以为硬件加速，实时处理和嵌入式应用提供可行的解决方案。最后，讨论了MMN-SLCA在图像超分辨率重建中的应用。实验仿真和结果分析验证了该方案的有效性，并显示了其在大规模低功耗智能信息编码和处理中的巨大潜力。设计了稀疏编码算法的硬件实现框架，为硬件加速，实时处理和嵌入式应用提供可行的解决方案。最后，讨论了MMN-SLCA在图像超分辨率重建中的应用。实验仿真和结果分析验证了该方案的有效性，并显示了其在大规模低功耗智能信息编码和处理中的巨大潜力。设计了稀疏编码算法的硬件实现框架，为硬件加速，实时处理和嵌入式应用提供可行的解决方案。最后，讨论了MMN-SLCA在图像超分辨率重建中的应用。实验仿真和结果分析验证了该方案的有效性，并显示了其在大规模低功耗智能信息编码和处理中的巨大潜力。