Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912229588205568 |
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| author | Xie, Wei |
| author_facet | Xie, Wei |
| contents | This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approximately 40\% learning accuracy with just 10 training stimuli, where each category is exposed to the model only once during training (one-shot learning). With larger training samples, the accuracy increases up to 87\%, maintaining negligible ambiguity. Notably, with only 10 hidden neurons, the model reaches 89\% accuracy with around 10\% ambiguity. This proposed method offers a robust and efficient alternative to traditional backpropagation-based supervised learning techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_16524 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition Xie, Wei Computer Vision and Pattern Recognition Neurons and Cognition This study introduces a novel supervised learning approach for spiking neural networks that does not rely on traditional backpropagation. Instead, it employs spike-timing-dependent plasticity (STDP) within a supervised framework for image recognition tasks. The effectiveness of this method is demonstrated using the MNIST dataset. The model achieves approximately 40\% learning accuracy with just 10 training stimuli, where each category is exposed to the model only once during training (one-shot learning). With larger training samples, the accuracy increases up to 87\%, maintaining negligible ambiguity. Notably, with only 10 hidden neurons, the model reaches 89\% accuracy with around 10\% ambiguity. This proposed method offers a robust and efficient alternative to traditional backpropagation-based supervised learning techniques. |
| title | Supervised Learning without Backpropagation using Spike-Timing-Dependent Plasticity for Image Recognition |
| topic | Computer Vision and Pattern Recognition Neurons and Cognition |
| url | https://arxiv.org/abs/2410.16524 |