ArticleFrontiers in neuroscience2021
Revisiting Batch Normalization for Training Low-Latency Deep Spiking Neural Networks From Scratch.
Article in Frontiers in neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed.
- An Explainable Approach to Parkinson's Diagnosis Using the Contrastive Explanation Method-CEM.Diagnostics (Basel, Switzerland) · 2025Article
- Neuromorphic computing for robotic vision: algorithms to hardware advances.Communications engineering · 2025Review
- Temporal pavlovian conditioning of a model spiking neural network for discrimination sequences of short time intervals.Journal of computational neuroscience · 2025Article
- Optimizing event-driven spiking neural network with regularization and cutoff.Frontiers in neuroscience · 2025Article
- Direct training high-performance deep spiking neural networks: a review of theories and methods.Frontiers in neuroscience · 2024Review
- Sparse-firing regularization methods for spiking neural networks with time-to-first-spike coding.Scientific reports · 2023Article
- Learnable Leakage and Onset-Spiking Self-Attention in SNNs with Local Error Signals.Sensors (Basel, Switzerland) · 2023Article
- Energy-Efficient Spiking Segmenter for Frame and Event-Based Images.Biomimetics (Basel, Switzerland) · 2023Article
- Critically synchronized brain waves form an effective, robust and flexible basis for human memory and learning.Scientific reports · 2023Article
- Direct learning-based deep spiking neural networks: a review.Frontiers in neuroscience · 2023Review
- Spiking neural networks fine-tuning for brain image segmentation.Frontiers in neuroscience · 2023Article
- Efficient and generalizable cross-patient epileptic seizure detection through a spiking neural network.Frontiers in neuroscience · 2023Article
- CodnNet: A lightweight CNN architecture for detection of COVID-19 infection.Applied soft computing · 2022Article
- Lead federated neuromorphic learning for wireless edge artificial intelligence.Nature communications · 2022Article
- Backpropagation with biologically plausible spatiotemporal adjustment for training deep spiking neural networks.Patterns (New York, N.Y.) · 2022Article
- Rethinking the Role of Normalization and Residual Blocks for Spiking Neural Networks.Sensors (Basel, Switzerland) · 2022Article
- Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning.Frontiers in neuroscience · 2022Article
- ACE-SNN: Algorithm-Hardware Co-design of Energy-Efficient & Low-Latency Deep Spiking Neural Networks for 3D Image Recognition.Frontiers in neuroscience · 2022Article
- Visual explanations from spiking neural networks using inter-spike intervals.Scientific reports · 2021Article
- Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms.Sensors (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
Spiking Neural Networks (SNNs) have recently emerged as an alternative to deep learning owing to sparse, asynchronous and binary event (or spike) driven processing, that can yield huge energy efficiency benefits on neuromorphic hardware. However, SNNs convey temporally-varying spike activation through time that is likely to induce a large variation of forward activation and backward gradients, resulting in unstable training. To address this training issue in SNNs, we revisit Batch Normalization (BN) and propose a temporal Batch Normalization Through Time (BNTT) technique. Different from previous BN techniques with SNNs, we find that varying the BN parameters at every time-step allows the model to learn the time-varying input distribution better. Specifically, our proposed BNTT decouples the parameters in a BNTT layer along the time axis to capture the temporal dynamics of spikes. We demonstrate BNTT on CIFAR-10, CIFAR-100, Tiny-ImageNet, event-driven DVS-CIFAR10 datasets, and Sequential MNIST and show near state-of-the-art performance. We conduct comprehensive analysis on the temporal characteristic of BNTT and showcase interesting benefits toward robustness against random and adversarial noise. Further, by monitoring the learnt parameters of BNTT, we find that we can do temporal early exit. That is, we can reduce the inference latency by ~5 - 20 time-steps from the original training latency. The code has been released at https://github.com/Intelligent-Computing-Lab-Yale/BNTT-Batch-Normalization-Through-Time.
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