Editorial: Spiking Neural Network Learning, Benchmarking, Programming and Executing

Abstract

Editorial on the Research Topic Spiking Neural Network Learning, Benchmarking, Programming and Executing A spiking neural network (SNN), a type of brain-inspired neural network, mimics the biological brain, specifically, its neural codes, neuro-dynamics, and circuitry. SNNs have garnered great interest in both Artificial Intelligence (AI) and neuroscience communities given its great potential in biologically realistic modeling of human cognition and development of energy efficient, event-driven machine learning hardware (Pei et al., 2019; Roy et al., 2019). Significant progress has been made across a wide spectrum of AI fields, such as image processing, speech recognition, and machine translation. They are largely driven by the advance in Artificial Neural Networks (ANN) in systematic learning theories, explicit benchmarks with various tasks and data sets, friendly programming tools [e.g., TensorFlow (Abadi et al., 2016) and Pytorch (Paszke et al., 2019) machine learning tools], and efficient processing platforms [e.g., graphics processing unit (GPU) and tensor processing unit (TPU) (Jouppi et al., 2017)]. In comparison, SNNs are still at an early stage in these aspects. To further exploit the advantages of SNNs and attract more researchers to contribute in this field, we proposed a Research Topic in Frontiers in Neuroscience to discuss the main challenges and future prospects of SNNs, emphasizing on its “Learning algorithms, Benchmarking, Programming, and Executing.” We are confident that SNNs will play a critical role in the development of energy efficient machine learning devices through algorithm-hardware co-design. This Research Topic brings together researchers of different disciplines in order to present their recent work in SNNs. We received 22 submissions worldwide and accepted 15 papers. The scope of the accepted papers covers learning algorithms, model efficiency, programming tools, and neuromorphic hardware. Learning algorithms play perhaps the most important role in AI techniques. Machine learning algorithms, in particular those for deep neural networks (DNN), have become the standard bearer in a wide spectrum of AI tasks. Some of the more common learning algorithms include backpropagation (Hecht-Nielsen, 1992), stochastic gradient descent (SGD) (Bottou, 2012), and ADAM optimization (Kingma and Ba, 2014). Other techniques such as batch normalization (Ioffe and Szegedy, 2015) and distributed training (Dean et al., 2012) facilitate learning in DNNs and enable them to be applied in various real-world applications. In comparison, there are relatively fewer SNN learning algorithms and techniques. Existing SNN learning algorithms fall into three categories: unsupervised learning algorithms such as the original spike timing dependent plasticity (STDP) (Querlioz et al., 2013; Diehl and Cook, 2015; Kheradpisheh et al., 2016), indirect supervised learning such as ANN-to-SNN conversion (O'Connor et al., 2013; Pérez-Carrasco et al., 2013; Diehl et al., 2015; Sengupta et al., 2019), and direct supervised learning such as spatiotemporal backpropagation (Wu et al., 2018, 2019a,b). We note that progress in STDP research includes introducing a reward or supervision signal such as spike timing which, in combination with this third factor, dictates the weight changes (Paugam-Moisy et al., 2006; Franosch et al., 2013). Despite the progress made, no algorithm can yet train a very deep SNN efficiently, which has become almost the holy grail of our field. Below, we briefly summarize the accepted algorithm papers in this Research Topic. Inspired by the mammalian olfactory system, Borthakur and Cleland develop an SNN model trained using STDP for signal restoration and identification. It is broadly applicable to sensor array inputs. Luo et al. propose a new weight update mechanism that adjusts synaptic weights, leading to the first wrong output spike-timing to classify input spike trains with time-sensitive information accurately. He et al. divide the learning (weight training) process into two phases: the structure formation phase using Hebb's rule, and the parameter training phase using STDP and reinforcement learning, so as to form an SNN-based associative memory system. In contrary to just training synaptic weights, Wang et al. propose training both the synaptic weights and delays using gradient descent so as to achieve better performance. Based on a structurally fixed small SNN with sparse recurrent connections, Ponghiran et al. use Q-learning to train only its output layer so as to achieve human-level performance on complex reinforcement learning tasks such as Atari games. Their research demonstrates that a small random recurrent SNN is able to provide a computationally efficient alternative to state-of-art deep reinforcement learning networks with several layers of trainable parameters. The above works have made good progress toward better performing SNN learning algorithms. We believe that further progress will be made in this field in the future. In recent years, hardware oriented DNN compression techniques have been proposed that offer significant memory saving and hardware acceleration (Han et al., 2015a, 2016; Zhang et al., 2016; Huang et al., 2017; Aimar et al., 2018). At present, many compression techniques are proposed that provide a trade-off between processing efficiency and application accuracy (Han et al., 2015b; Novikov et al., 2015; Zhou et al., 2016). Such an approach has also caught on in the design of SNN accelerators (Deng et al., 2019), with the following contribution in this Research Topic. Afshar et al. investigate how a hardware-efficient variant of STDP may be used for event-based feature extraction. Using a rigorous testing framework, a range of spatio-temporal kernels with different surface decaying methods, decay functions, receptive field sizes, feature numbers, and backend classifiers are evaluated. This...