ArticleFrontiers in neuroscience2019
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing.
Article in Frontiers in neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- A compressed sensing neuromorphic processor for sparse signal classification.Frontiers in neuroscience · 2026Article
- Encrypted Spiking Neural Networks Based on Adaptive Differential Privacy Mechanism.Entropy (Basel, Switzerland) · 2025Article
- Memcapacitor Crossbar Array with Charge Trap NAND Flash Structure for Neuromorphic Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator.Frontiers in neuroscience · 2023Article
- [A review of brain-like spiking neural network and its neuromorphic chip research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Review
- Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity.Scientific reports · 2021Article
- A Cost-Efficient High-Speed VLSI Architecture for Spiking Convolutional Neural Network Inference Using Time-Step Binary Spike Maps.Sensors (Basel, Switzerland) · 2021Article
- BlocTrain: Block-Wise Conditional Training and Inference for Efficient Spike-Based Deep Learning.Frontiers in neuroscience · 2021Article
- End-to-End Implementation of Various Hybrid Neural Networks on a Cross-Paradigm Neuromorphic Chip.Frontiers in neuroscience · 2021Article
- Article
- A Heterogeneous Spiking Neural Network for Unsupervised Learning of Spatiotemporal Patterns.Frontiers in neuroscience · 2020Article
- Toward Scalable, Efficient, and Accurate Deep Spiking Neural Networks With Backward Residual Connections, Stochastic Softmax, and Hybridization.Frontiers in neuroscience · 2020Article
- Exploring the Connection Between Binary and Spiking Neural Networks.Frontiers in neuroscience · 2020Article
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Abstract
In this work, we propose ReStoCNet, a residual stochastic multilayer convolutional Spiking Neural Network (SNN) composed of binary kernels, to reduce the synaptic memory footprint and enhance the computational efficiency of SNNs for complex pattern recognition tasks. ReStoCNet consists of an input layer followed by stacked convolutional layers for hierarchical input feature extraction, pooling layers for dimensionality reduction, and fully-connected layer for inference. In addition, we introduce residual connections between the stacked convolutional layers to improve the hierarchical feature learning capability of deep SNNs. We propose Spike Timing Dependent Plasticity (STDP) based probabilistic learning algorithm, referred to as Hybrid-STDP (HB-STDP), incorporating Hebbian and anti-Hebbian learning mechanisms, to train the binary kernels forming ReStoCNet in a layer-wise unsupervised manner. We demonstrate the efficacy of ReStoCNet and the presented HB-STDP based unsupervised training methodology on the MNIST and CIFAR-10 datasets. We show that residual connections enable the deeper convolutional layers to self-learn useful high-level input features and mitigate the accuracy loss observed in deep SNNs devoid of residual connections. The proposed ReStoCNet offers >20 × kernel memory compression compared to full-precision (32-bit) SNN while yielding high enough classification accuracy on the chosen pattern recognition tasks.
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