Artificial Neural Networks (ANNs)-based techniques have dominated state-of-the-art results in most problems related to computer vision, audio recognition, and natural language processing in the past few years, resulting in strong industrial adoption from all leading technology companies worldwide. One of the major obstacles that have historically delayed large-scale adoption of ANNs is the huge computational and power costs associated with training and testing (deploying) them. In the mean-time, Neuromorphic Computing platforms have recently achieved remarkable performance running the bio-realistic Spiking Neural Networks at high throughput and very low power consumption making them a natural alternative to ANNs. Here, we propose using the Random Neural Network, a spiking neural network with both theoretical and practical appealing properties, as a general purpose classifier that can match the classification power of ANNs on a number of tasks while enjoying all the features of being a spiking neural network. This is demonstrated on a number of real-world classification datasets.

中文翻译：

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使用尖峰随机神经网络进行准确、节能的分类

在过去的几年中，基于人工神经网络 (ANN) 的技术在与计算机视觉、音频识别和自然语言处理相关的大多数问题中占据了最先进的结果，导致所有领先技术公司在工业上的广泛采用全世界。历史上延迟大规模采用人工神经网络的主要障碍之一是与训练和测试（部署）它们相关的巨大计算和电力成本。与此同时，神经形态计算平台最近在以高吞吐量和极低功耗运行仿生脉冲神经网络时取得了显着的性能，使其成为人工神经网络的自然替代品。在这里，我们建议使用随机神经网络，这是一种具有理论和实践吸引力的尖峰神经网络，作为一种通用分类器，它可以在许多任务上与 ANN 的分类能力相匹配，同时享受作为尖峰神经网络的所有特征。这在许多真实世界的分类数据集上得到了证明。