Brain-inspired computing (BIC) is an emerging research field that aims to build fundamental theories, models, hardware architectures, and application systems toward more general artificial intelligence (AI) by learning from the information processing mechanisms or structures/functions of biological nervous systems. It is regarded as one of the most promising research directions for future intelligent computing in the post-Moore era. In the past few years, various new schemes in this field have sprung up to explore more general AI. These works are quite divergent in the aspects of modeling/algorithm, software tool, hardware platform, and benchmark data since BIC is an interdisciplinary field that consists of many different domains, including computational neuroscience, AI, computer science, statistical physics, material science, and microelectronics. This situation greatly impedes researchers from obtaining a clear picture and getting started in the right way. Hence, there is an urgent requirement to do a comprehensive survey in this field to help correctly recognize and analyze such bewildering methodologies. What are the key issues to enhance the development of BIC? What roles do the current mainstream technologies play in the general framework of BIC? Which techniques are truly useful in real-world applications? These questions largely remain open. To address the above issues, in this survey, we first clarify the biggest challenge of BIC: how can AI models benefit from the recent advancements in computational neuroscience? With this challenge in mind, we will focus on discussing the concept of BIC and summarize four components of BIC infrastructure development: 1) modeling/algorithm; 2) hardware platform; 3) software tool; and 4) benchmark data. For each component, we will summarize its recent progress, main challenges to resolve, and future trends. Based on these studies, we present a general framework for the real-world applications of BIC systems, which is promising to benefit both AI and brain science. Finally, we claim that it is extremely important to build a research ecology to promote prosperity continuously in this field.

中文翻译：

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类脑计算：系统调查和未来趋势


类脑计算（BIC）是一个新兴的研究领域，旨在通过学习生物神经系统的信息处理机制或结构/功能，构建面向更通用人工智能（AI）的基础理论、模型、硬件架构和应用系统。它被认为是后摩尔时代未来智能计算最有前途的研究方向之一。过去几年，该领域各种新方案如雨后春笋般涌现，探索更通用的人工智能。这些工作在建模/算法、软件工具、硬件平台和基准数据方面存在很大差异，因为 BIC 是一个跨学科领域，由许多不同的领域组成，包括计算神经科学、人工智能、计算机科学、统计物理学、材料科学、和微电子学。这种情况极大地阻碍了研究人员获得清晰的认识并以正确的方式开始。因此，迫切需要对该领域进行全面的调查，以帮助正确认识和分析这些令人困惑的方法论。推动BIC发展的关键问题是什么？目前主流技术在BIC的总体框架中扮演什么角色？哪些技术在实际应用中真正有用？这些问题在很大程度上仍然悬而未决。为了解决上述问题，在本次调查中，我们首先阐明了 BIC 的最大挑战：AI 模型如何从计算神经科学的最新进展中受益？考虑到这一挑战，我们将重点讨论 BIC 的概念，并总结 BIC 基础设施开发的四个组成部分：1）建模/算法； 2）硬件平台； 3）软件工具； 4) 基准数据。 对于每个组成部分，我们将总结其近期进展、需要解决的主要挑战以及未来趋势。基于这些研究，我们提出了 BIC 系统实际应用的通用框架，这有望使人工智能和脑科学受益。最后，我们认为，构建研究生态对于促进该领域的持续繁荣极为重要。