In our brain, information is exchanged among neurons in the form of spikes where both the space (which neuron fires) and time (when the neuron fires) contain relevant information. Every neuron is connected to other neurons by synapses, which are continuously created, updated, and stimulated to enable information processing and learning. Realizing the brain-like neuron/synapse network in silicon would enable artificial autonomous agents capable of learning, adaptation, and interaction with the environment. Toward this aim, the conventional microelectronic technology, which is based on complementary metal–oxide–semiconductor transistors and the von Neumann computing architecture, does not provide the desired energy efficiency and scaling potential. A generation of emerging memory devices, including resistive switching random access memory (RRAM) also known as the memristor, can offer a wealth of physics-enabled processing capabilities, including multiplication, integration, potentiation, depression, and time-decaying stimulation, which are suitable to recreate some of the fundamental phenomena of the human brain in silico. This work provides an overview about the status and the most recent updates on brain-inspired neuromorphic computing devices. After introducing the RRAM device technologies, we discuss the main computing functionalities of the human brain, including neuron integration and fire, dendritic filtering, and short- and long-term synaptic plasticity. For each of these processing functions, we discuss their proposed implementation in terms of materials, device structure, and brain-like characteristics. The rich device physics, the nano-scale integration, the tolerance to stochastic variations, and the ability to process information in situ make the emerging memory devices a promising technology for future brain-like hardware intelligence.

中文翻译：

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通过存储设备物理进行类脑计算

在我们的大脑中，信息以尖峰的形式在神经元之间交换，其中空间（神经元触发）和时间（神经元触发）都包含相关信息。每个神经元都通过突触与其他神经元相连，突触不断地产生、更新和刺激，以实现信息处理和学习。在硅中实现类似大脑的神经元/突触网络将使人工自主代理能够学习、适应并与环境交互。为了实现这一目标，基于互补金属氧化物半导体晶体管和冯诺依曼计算架构的传统微电子技术无法提供所需的能源效率和扩展潜力。一代新兴的存储设备，电脑模拟。这项工作概述了受大脑启发的神经形态计算设备的状态和最新更新。在介绍了 RRAM 设备技术之后，我们讨论了人脑的主要计算功能，包括神经元集成和火、树突过滤以及短期和长期突触可塑性。对于这些处理功能中的每一个，我们讨论了它们在材料、设备结构和类脑特性方面的建议实现。丰富的设备物理、纳米级集成、对随机变化的容忍度以及原位处理信息的能力使新兴的存储设备成为未来类脑硬件智能的有前途的技术。