Sparse coding with memristor networks
Sparse coding with memristor networks
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DOI：
10.1038/nnano.2017.83
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发表时间：
2017-08-01
影响因子：
38.3
通讯作者：
Lu, Wei D.
中科院分区：
文献类型：
--
作者：
Sheridan, Patrick M.;Cai, Fuxi;Lu, Wei D.
信息的稀疏表示为对高维数据进行特征提取提供了一种强有力的手段，在信号处理、计算机视觉、目标识别和神经生物学等应用中受到广泛关注。稀疏编码也被认为是生物神经系统能够在消耗极少能量的情况下高效处理大量复杂感官数据的关键机制。在此，我们报告了采用模拟忆阻器构成的32×32交叉阵列，以一种受生物启发的方式对稀疏编码算法进行实验性实现。该网络能够高效地实现模式匹配和侧向神经元抑制，并允许利用神经元活动和存储的字典元素对输入数据进行稀疏编码。根据输入信号的性质，可以在同一系统中训练和存储不同的字典集。利用稀疏编码算法，我们还基于学习到的字典进行自然图像处理。
Sparse representation of information provides a powerful means to perform feature extraction on high-dimensional data and is of broad interest for applications in signal processing, computer vision, object recognition and neurobiology. Sparse coding is also believed to be a key mechanism by which biological neural systems can efficiently process a large amount of complex sensory data while consuming very little power. Here, we report the experimental implementation of sparse coding algorithms in a bio-inspired approach using a 32 x 32 crossbar array of analog memristors. This network enables efficient implementation of pattern matching and lateral neuron inhibition and allows input data to be sparsely encoded using neuron activities and stored dictionary elements. Different dictionary sets can be trained and stored in the same system, depending on the nature of the input signals. Using the sparse coding algorithm, we also perform natural image processing based on a learned dictionary.