Deep Unsupervised Learning Using Spike-Timing-Dependent Plasticity
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Abstract (arXiv)
Spike-Timing-Dependent Plasticity (STDP) is an unsupervised learning mechanism for Spiking Neural Networks (SNNs) that has received significant attention from the neuromorphic hardware community. However, scaling such local learning techniques to deeper networks and large-scale tasks has remained elusive. In this work, we investigate a Deep-STDP framework where a rate-based convolutional network, that can be deployed in a neuromorphic setting, is trained in tandem with pseudo-labels generated by the STDP clustering process on the network outputs. We achieve 24.56% higher accuracy and 3.5× faster convergence speed at iso-accuracy on a 10-class subset of the Tiny ImageNet dataset in contrast to a k-means clustering approach.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 54 references that are in the map. First posted on arXiv 8 Jul 2023.
Builds on
22 of 54 references.
References · 22
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10 more
- 2018Universal Statistics of Fisher Information in Deep Neural Networks: Mean Field Approachcited by 89
- 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 70
- 2018BindsNET: A machine learning-oriented spiking neural networks library in Pythoncited by 42
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- 2020Exploring the Connection Between Binary and Spiking Neural Networkscited by 33
- 2020EqSpike: Spike-driven Equilibrium Propagation for Neuromorphic Implementationscited by 29
- 2022Exploring Temporal Information Dynamics in Spiking Neural Networkscited by 23
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- 2020Neuromorphic Nearest-Neighbor Search Using Intel's Pohoiki Springscited by 12
- 2022Sequence Learning Using Equilibrium Propagationcited by 4
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Citing papers per year: 2024: 1; 2025: 0; 2026: 1.
2 papers.