ArticleFrontiers in neuroscience2018
Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- BIASNN: a biologically inspired attention mechanism in spiking neural networks for image classification.Scientific reports · 2025Article
- A frugal Spiking Neural Network for unsupervised multivariate temporal pattern classification and multichannel spike sorting.Nature communications · 2025Article
- Noise and Dynamical Synapses as Optimization Tools for Spiking Neural Networks.Entropy (Basel, Switzerland) · 2025Article
- Convolutional spiking neural networks for intent detection based on anticipatory brain potentials using electroencephalogram.Scientific reports · 2024Article
- Brain-guided manifold transferring to improve the performance of spiking neural networks in image classification.Journal of computational neuroscience · 2023Article
- Spiking Recurrent Neural Networks Represent Task-Relevant Neural Sequences in Rule-Dependent Computation.Cognitive computation · 2023Article
- Supervised Learning Algorithm Based on Spike Train Inner Product for Deep Spiking Neural Networks.Brain sciences · 2023Article
- Efficient training of spiking neural networks with temporally-truncated local backpropagation through time.Frontiers in neuroscience · 2023Article
- Gradient-based feature-attribution explainability methods for spiking neural networks.Frontiers in neuroscience · 2023Article
- NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications.Frontiers in neuroscience · 2023Article
- EXODUS: Stable and efficient training of spiking neural networks.Frontiers in neuroscience · 2023Article
- Spike Timing-Dependent Plasticity with Enhanced Long-Term Depression Leads to an Increase of Statistical Complexity.Entropy (Basel, Switzerland) · 2022Article
- Robust Spike-Based Continual Meta-Learning Improved by Restricted Minimum Error Entropy Criterion.Entropy (Basel, Switzerland) · 2022Article
- Memristive Devices Based on Two-Dimensional Transition Metal Chalcogenides for Neuromorphic Computing.Nano-micro letters · 2022Review
- MONETA: A Processing-In-Memory-Based Hardware Platform for the Hybrid Convolutional Spiking Neural Network With Online Learning.Frontiers in neuroscience · 2022Article
- Neuroevolution Guided Hybrid Spiking Neural Network Training.Frontiers in neuroscience · 2022Article
- Spike-Based Approximate Backpropagation Algorithm of Brain-Inspired Deep SNN for Sonar Target Classification.Computational intelligence and neuroscience · 2022Article
- Voltage-dependent synaptic plasticity: Unsupervised probabilistic Hebbian plasticity rule based on neurons membrane potential.Frontiers in neuroscience · 2022Article
- [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
- Exploring Optimized Spiking Neural Network Architectures for Classification Tasks on Embedded Platforms.Sensors (Basel, Switzerland) · 2021Article
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4 authors.
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Abstract
Spiking Neural Networks (SNNs) are fast becoming a promising candidate for brain-inspired neuromorphic computing because of their inherent power efficiency and impressive inference accuracy across several cognitive tasks such as image classification and speech recognition. The recent efforts in SNNs have been focused on implementing deeper networks with multiple hidden layers to incorporate exponentially more difficult functional representations. In this paper, we propose a pre-training scheme using biologically plausible unsupervised learning, namely Spike-Timing-Dependent-Plasticity (STDP), in order to better initialize the parameters in multi-layer systems prior to supervised optimization. The multi-layer SNN is comprised of alternating convolutional and pooling layers followed by fully-connected layers, which are populated with leaky integrate-and-fire spiking neurons. We train the deep SNNs in two phases wherein, first, convolutional kernels are pre-trained in a layer-wise manner with unsupervised learning followed by fine-tuning the synaptic weights with spike-based supervised gradient descent backpropagation. Our experiments on digit recognition demonstrate that the STDP-based pre-training with gradient-based optimization provides improved robustness, faster (~2.5 ×) training time and better generalization compared with purely gradient-based training without pre-training.
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