Hierarchical, modular and sparse information processing are signature characteristics of biological neural networks. These aspects have been the backbone of several artificial neural network designs of the brain-like networks, including Hierarchical Temporal Memory (HTM). The main contribution of this work is showing that Convolutional Neural Network (CNN) in combination with Long short term memory (LSTM) can be a good alternative for implementing the hierarchy, modularity and sparsity of information processing. To demonstrate this, we draw a comparison of CNN-LSTM and HTM performance on a face recognition problem with a small training set. We also present the analog CMOS-memristor circuit blocks required to implement such a scheme. The presented memristive implementations of the CNN-LSTM architecture are easier to i mplement, train and offer higher recognition performance than the HTM. The study also includes memristor variability and failure analysis.

中文翻译：

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谁是赢家？忆阻CMOS混合模块：CNN-LSTM与HTM。

分层，模块化和稀疏信息处理是生物神经网络的特征。这些方面已成为大脑类似网络（包括分层时间记忆（HTM））的几种人工神经网络设计的基础。这项工作的主要贡献在于，将卷积神经网络（CNN）与长期短期记忆（LSTM）相结合可以很好地替代实现信息处理的层次结构，模块化和稀疏性。为了证明这一点，我们将CNN-LSTM和HTM性能在训练集较少的面部识别问题上进行了比较。我们还介绍了实现这种方案所需的模拟CMOS忆阻器电路块。所展示的CNN-LSTM体系结构的忆阻实现更易于实现，训练并提供比HTM更高的识别性能。该研究还包括忆阻器可变性和故障分析。