Instituto de Telecomunicações, Physics of Information and Quantum Technologies Group, Portugal.
Department of Physical Chemistry, University of the Basque Country UPV/EHU, Apartado 644, E-48080 Bilbao, Spain.
Neural Netw. 2020 Feb;122:273-278. doi: 10.1016/j.neunet.2019.10.013. Epub 2019 Nov 2.
Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for a network in which both the synapses and the neurons are implemented using memristors has been proposed so far. In the present work we introduce models for single and multilayer perceptrons based exclusively on memristors. We adapt the delta rule to the memristor-based single-layer perceptron and the backpropagation algorithm to the memristor-based multilayer perceptron. Our results show that both perform as expected for perceptrons, including satisfying Minsky-Papert's theorem. As a consequence of the Universal Approximation Theorem, they also show that memristors are universal function approximators. By using memristors for both the neurons and the synapses, our models pave the way for novel memristor-based neural network architectures and algorithms. A neural network based on memristors could show advantages in terms of energy conservation and open up possibilities for other learning systems to be adapted to a memristor-based paradigm, both in the classical and quantum learning realms.
忆阻器，一种具有记忆功能的电阻器，其输出取决于输入的历史，已成功应用于神经形态架构中，特别是作为突触和非易失性存储器。然而，据我们所知，到目前为止，还没有提出一种使用忆阻器同时实现突触和神经元的网络模型。在本工作中，我们引入了仅基于忆阻器的单层感知器和多层感知器的模型。我们将 delta 规则适应于基于忆阻器的单层感知器，将反向传播算法适应于基于忆阻器的多层感知器。我们的结果表明，这两种模型都符合感知器的预期，包括满足明斯基-帕珀特定理。由于通用逼近定理，它们还表明忆阻器是通用的函数逼近器。通过在神经元和突触中都使用忆阻器，我们的模型为新型的基于忆阻器的神经网络架构和算法铺平了道路。基于忆阻器的神经网络在节能方面可能具有优势，并为其他学习系统适应基于忆阻器的范例开辟了可能性，无论是在经典还是量子学习领域。
