SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence

Recently, artificial neural networks (ANNs), such as convolutional neural networks (CNNs)[1], recurrent neural networks (RNNs)[2] and transformers[3], have defeated most other methods and even surpassed the average ability levels of humans in some areas, including image classification [1, 4, 5], object detection [6, 7, 8], machine translation [9, 10, 11, 3], speech recognition [12, 13], and gaming [14, 15]. These achievements are computer-science-oriented because ANNs are mainly driven by gradient-based numerical optimization methods[16, 17], big data[18, 19] and massively parallel computing with graphics processing units (GPUs) [20, 21]. Although neuroscience plays a diminished role in ANNs[22], insights from neuroscience are critical for building general human-level artificial intelligence (AI) systems [23, 24]. The human brain is one of the most intelligent systems, possessing overwhelming advantages over any other artificial system in cognition and learning tasks such as transfer learning and continual learning[24]. The neuroscientific community has been exploring biologically plausible computational paradigms to understand, mimic, and exploit the impressive feats of the human brain.