With an exponential increase in the amount of data collected per day, the fields of artificial intelligence and machine learning continue to progress at a rapid pace with respect to algorithms, models, applications, and hardware. In particular, deep neural networks have revolutionized these fields by providing unprecedented human-like performance in solving many real-world problems such as image or speech recognition. There is also significant research aimed at unraveling the principles of computation in large biological neural networks and, in particular, biologically plausible spiking neural networks. This article presents an overview of the brain-inspired computing models starting with the development of the perceptron and multi-layer perceptron followed by convolutional neural networks (CNNs) and recurrent neural networks (RNNs). This article also briefly reviews other neural network models such as Hopfield neural networks and Boltzmann machines. Other models such as spiking neural networks (SNNs) and hyperdimensional computing are then briefly reviewed. Recent advances in these neural networks and graph related neural networks are then described.

中文翻译：

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灵感来自大脑的计算：模型和体系结构

随着每天收集的数据量呈指数级增长，就算法，模型，应用程序和硬件而言，人工智能和机器学习领域继续以迅猛的速度发展。特别是，深层神经网络通过在解决许多现实世界中的问题（例如图像或语音识别）方面提供前所未有的类人性能，彻底改变了这些领域。也有大量研究旨在揭示大型生物神经网络，尤其是生物学上可行的尖峰神经网络的计算原理。本文概述了脑启发计算模型始于感知器和多层感知器的发展，然后是卷积神经网络（CNN）和递归神经网络（RNN）。本文还简要回顾了其他神经网络模型，例如Hopfield神经网络和Boltzmann机器。然后简要回顾了诸如尖峰神经网络（SNN）和超维计算等其他模型。然后描述了这些神经网络和图相关神经网络的最新进展。