ArticleFrontiers in computational neuroscience2018
Unsupervised Feature Learning With Winner-Takes-All Based STDP.
Article in Frontiers in computational neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 87 citations in OpenAlex.
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- Temporal single spike coding for effective transfer learning in spiking neural networks.Scientific reports · 2025Article
- Neuromorphic algorithms for brain implants: a review.Frontiers in neuroscience · 2025Review
- Neuron Circuit Based on a Split-gate Transistor with Nonvolatile Memory for Homeostatic Functions of Biological Neurons.Biomimetics (Basel, Switzerland) · 2024Article
- Selective area doping for Mott neuromorphic electronics.Science advances · 2023Article
- BIDL: a brain-inspired deep learning framework for spatiotemporal processing.Frontiers in neuroscience · 2023Article
- Stochastic binary synapses having sigmoidal cumulative distribution functions for unsupervised learning with spike timing-dependent plasticity.Scientific reports · 2021Article
- Learning Invariant Object and Spatial View Representations in the Brain Using Slow Unsupervised Learning.Frontiers in computational neuroscience · 2021Review
- BlocTrain: Block-Wise Conditional Training and Inference for Efficient Spike-Based Deep Learning.Frontiers in neuroscience · 2021Article
- SSTDP: Supervised Spike Timing Dependent Plasticity for Efficient Spiking Neural Network Training.Frontiers in neuroscience · 2021Article
- Helix Matrix Transformation Combined With Convolutional Neural Network Algorithm for Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry-Based Bacterial Identification.Frontiers in microbiology · 2020Article
- Bio-Inspired Techniques in a Fully Digital Approach for Lifelong Learning.Frontiers in neuroscience · 2020Article
- A Hardware-Deployable Neuromorphic Solution for Encoding and Classification of Electronic Nose Data.Sensors (Basel, Switzerland) · 2019Article
- SpykeTorch: Efficient Simulation of Convolutional Spiking Neural Networks With at Most One Spike per Neuron.Frontiers in neuroscience · 2019Article
- A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications.Frontiers in neuroscience · 2019Article
- ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing.Frontiers in neuroscience · 2019Article
- Training Deep Spiking Convolutional Neural Networks With STDP-Based Unsupervised Pre-training Followed by Supervised Fine-Tuning.Frontiers in neuroscience · 2018Article
- SpiLinC: Spiking Liquid-Ensemble Computing for Unsupervised Speech and Image Recognition.Frontiers in neuroscience · 2018Article
- BindsNET: A Machine Learning-Oriented Spiking Neural Networks Library in Python.Frontiers in neuroinformatics · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All (WTA) framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process. This learning process allows us to train multi-layer architectures of convolutional sparse features. We apply our method to extract features from the MNIST, ETH80, CIFAR-10, and STL-10 datasets and show that these features are relevant for classification. We finally compare these results with several other state of the art unsupervised learning methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.