Brain-Inspired Computing: Models and Architectures

• Published in: IEEE open journal of circuits and systems Vol. 1; pp. 185 - 204
• Main Authors: Parhi, Keshab K., Unnikrishnan, Nanda K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Artificial neural networks; Biological computing; Biological neural networks; Biological system modeling; Boltzmann machines; Brain; Brain modeling; Computational modeling; convolutional neural network; graph neural networks; Hopfield neural network; Human performance; hyperdimensional computing; Machine learning; Mathematical model; multi-layer perceptron; Multilayers; Neural networks; Neurons; Object recognition; Perceptron; recurrent neural network; Recurrent neural networks; Speech recognition; Spiking; spiking neural networks; Synapses
• ISSN: 2644-1225; 2644-1225
• DOI: 10.1109/OJCAS.2020.3032092

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.