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This document is an editorial published in the journal Frontiers in Neuroscience. It discusses the theoretical advances and practical applications of spiking neural networks (SNNs). The editorial highlights the growing popularity of neuromorphic engineering and the use of SNNs in various domains, such as image processing, biosignal analysis, activity recognition, and audio processing. The articles included in this special topic cover different applications and methodologies for training SNNs. The document emphasizes the wide potential of SNNs and encourages further research in this field, while also cautioning against misconceptions and the need to use SNNs appropriately for specific tasks. [Extracted from the article]

Transfer learning has achieved a lot of success recently in saving training samples. However, most of the existing methods only focus on what and how to transfer, but ignore when is the proper transfer time. In the study, we find that transfer useful knowledge at proper time is also significant for the performance. To address this issue, we propose a dynamic domain adaptation approach based on the particle swarm optimization evolutionary algorithm, which searches transfer opportunity automatically for different data domains and training stages. We evaluate the proposed method on various deep learning network structures, and find that the transfer coefficient has large variance in the first several training epochs, and becomes smaller later. This indicates that the features learned in the first several epochs are not stable and is not suitable for static transfer. In addition, the proposed method is not sensitive to the hyper-parameters generated, and it searches suitable transfer coefficients dynamically and automatically instead of conventional manual way. Extensive experiments conducted on various datasets and network structures demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Spiking neural networks (SNNs) have shown clear advantages over traditional artificial neural networks (ANNs) for low latency and high computational efficiency, due to their event-driven nature and sparse communication. However, the training of deep SNNs is not straightforward. In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning. By studying the equivalence between ANNs and SNNs in the discrete representation space, a primitive network conversion method is introduced that takes full advantage of spike count to approximate the activation value of ANN neurons. To compensate for the approximation errors arising from the primitive network conversion, we further introduce a layer-wise learning method with an adaptive training scheduler to fine-tune the network weights. The progressive tandem learning framework also allows hardware constraints, such as limited weight precision and fan-in connections, to be progressively imposed during training. The SNNs thus trained have demonstrated remarkable classification and regression capabilities on large-scale object recognition, image reconstruction, and speech separation tasks, while requiring at least an order of magnitude reduced inference time and synaptic operations than other state-of-the-art SNN implementations. It, therefore, opens up a myriad of opportunities for pervasive mobile and embedded devices with a limited power budget. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANN) have become the mainstream acoustic modeling technique for large vocabulary automatic speech recognition (ASR). A conventional ANN features a multi-layer architecture that requires massive amounts of computation. The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation. Motivated by their unprecedented energy-efficiency and rapid information processing capability, we explore the use of SNNs for speech recognition. In this work, we use SNNs for acoustic modeling and evaluate their performance on several large vocabulary recognition scenarios. The experimental results demonstrate competitive ASR accuracies to their ANN counterparts, while require only 10 algorithmic time steps and as low as 0.68 times total synaptic operations to classify each audio frame. Integrating the algorithmic power of deep SNNs with energy-efficient neuromorphic hardware, therefore, offer an attractive solution for ASR applications running locally on mobile and embedded devices. [ABSTRACT FROM AUTHOR]

The aggregate-label learning paradigm tackles the long-standing temporary credit assignment (TCA) problem in neuroscience and machine learning, enabling spiking neural networks to learn multimodal sensory clues with delayed feedback signals. However, the existing aggregate-label learning algorithms only work for single spiking neurons, and with low learning efficiency, which limit their real-world applicability. To address these limitations, in this article, we first propose an efficient threshold-driven plasticity algorithm for spiking neurons, namely ETDP. It enables spiking neurons to generate the desired number of spikes that match the magnitude of delayed feedback signals and to learn useful multimodal sensory clues embedded within spontaneous spiking activities. Furthermore, we extend the ETDP algorithm to support multi-layer spiking neural networks (SNNs), which significantly improves the applicability of aggregate-label learning algorithms. We also validate the multi-layer ETDP learning algorithm in a multimodal computation framework for audio-visual pattern recognition. Experimental results on both synthetic and realistic datasets show significant improvements in the learning efficiency and model capacity over the existing aggregate-label learning algorithms. It, therefore, provides many opportunities for solving real-world multimodal pattern recognition tasks with spiking neural networks. [ABSTRACT FROM AUTHOR]

The auditory front-end is an integral part of a spiking neural network (SNN) when performing auditory cognitive tasks. It encodes the temporal dynamic stimulus, such as speech and audio, into an efficient, effective and reconstructable spike pattern to facilitate the subsequent processing. However, most of the auditory front-ends in current studies have not made use of recent findings in psychoacoustics and physiology concerning human listening. In this paper, we propose a neural encoding and decoding scheme that is optimized for audio processing. The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve. We evaluate the perceptual quality of the BAE scheme using PESQ; the performance of the BAE based on sound classification and speech recognition experiments. Finally, we also built and published two spike-version of speech datasets: the Spike-TIDIGITS and the Spike-TIMIT, for researchers to use and benchmarking of future SNN research. [ABSTRACT FROM AUTHOR]

As the third generation of neural networks, Spiking Neural Networks (SNNs) have made great success in pattern recognition fields. However, the existing training methods for SNNs are not efficient enough because of the temporal encoding mechanism. To improve the training efficiency of the supervised SNNs and keep the useful temporal information, the Maximum Points-based Supervised Learning Rule (MPSLR) is proposed in this paper. Three training strategies are adopted in MPSLR to improve the learning performance. Firstly, only the target points and maximum voltage points are trained. By theoretical analyses, we find that the maximum points are effective for the voltage controlling of the non-target points, and the analytic solutions for all maximum voltage points are parallelly obtainable. This improves the training efficiency significantly by avoiding the successive voltage detecting. Secondly, the weight modification for each presynaptic neuron is normalized by a rate function to resizing the output scale. Thirdly, the spiking rates accumulated in a time window are utilized to involve more useful knowledge. Extensive experiments on both synthetic data and four real-world UCI datasets demonstrate that our algorithm achieves significantly better performance and higher efficiency than traditional methods in various situations, including different multi-spike rates and time lengths. Besides, it is more stable to hyper-parameter variations. [ABSTRACT FROM AUTHOR]

Recommender systems have been comprehensively applied in many industries, such as social Web sites, e-commerce, tourism service and so on, although suffering from the data sparsity and cold start problems. Currently, due to the advantage of online social networking, many social network-based recommendation scenarios have been developed to improve the recommendation accuracy, via exploring hidden social relations between users from the social network. In this article, focusing on addressing these problems, a novel social network and preference-based recommendation method—SRMP, is proposed, which incorporates the social network information and user’s preference in matrix factorization for recommendation. In contrast to previous approaches, to improve the recommendation accuracy, SRMP performs recommendation in each independent sub-community, which is derived from the initial social community according to different category tags. The experimental analysis on large real-world datasets demonstrates that the proposed method SRMP outperforms state-of-the-art approaches, especially in recommendation accuracy and solving the cold start problem. [ABSTRACT FROM AUTHOR]

Environmental sounds form part of our daily life. With the advancement of deep learning models and the abundance of training data, the performance of automatic sound classification (ASC) systems has improved significantly in recent years. However, the high computational cost, hence high power consumption, remains a major hurdle for large-scale implementation of ASC systems on mobile and wearable devices. Motivated by the observations that humans are highly effective and consume little power whilst analyzing complex audio scenes, we propose a biologically plausible ASC framework, namely SOM-SNN. This framework uses the unsupervised self-organizing map (SOM) for representing frequency contents embedded within the acoustic signals, followed by an event-based spiking neural network (SNN) for spatiotemporal spiking pattern classification. We report experimental results on the RWCP environmental sound and TIDIGITS spoken digits datasets, which demonstrate competitive classification accuracies over other deep learning and SNN-based models. The SOM-SNN framework is also shown to be highly robust to corrupting noise after multi-condition training, whereby the model is trained with noise-corrupted sound samples. Moreover, we discover the early decision making capability of the proposed framework: an accurate classification can be made with an only partial presentation of the input. [ABSTRACT FROM AUTHOR]

The spiking neural networks (SNNs) are the third generation of neural networks and perform remarkably well in cognitive tasks such as pattern recognition. The spike emitting and information processing mechanisms found in biological cognitive systems motivate the application of the hierarchical structure and temporal encoding mechanism in spiking neural networks, which have exhibited strong computational capability. However, the hierarchical structure and temporal encoding approach require neurons to process information serially in space and time respectively, which reduce the training efficiency significantly. For training the hierarchical SNNs, most existing methods are based on the traditional back-propagation algorithm, inheriting its drawbacks of the gradient diffusion and the sensitivity on parameters. To keep the powerful computation capability of the hierarchical structure and temporal encoding mechanism, but to overcome the low efficiency of the existing algorithms, a new training algorithm, the Normalized Spiking Error Back Propagation (NSEBP) is proposed in this paper. In the feedforward calculation, the output spike times are calculated by solving the quadratic function in the spike response model instead of detecting postsynaptic voltage states at all time points in traditional algorithms. Besides, in the feedback weight modification, the computational error is propagated to previous layers by the presynaptic spike jitter instead of the gradient decent rule, which realizes the layer-wised training. Furthermore, our algorithm investigates the mathematical relation between the weight variation and voltage error change, which makes the normalization in the weight modification applicable. Adopting these strategies, our algorithm outperforms the traditional SNN multi-layer algorithms in terms of learning efficiency and parameter sensitivity, that are also demonstrated by the comprehensive experimental results in this paper. [ABSTRACT FROM AUTHOR]