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Spike-VisNet: A novel framework for visual recognition with FocusLayer-STDP learning
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DOI：10.1016/j.neunet.2024.106918.png)
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Current vision-inspired spiking neural networks (SNNs) face key challenges due to their model structures typically focusing on single mechanisms and neglecting the integration of multiple biological features. These limitations, coupled with limited synaptic plasticity, hinder their ability to implement biologically realistic visual processing. To address these issues, we propose Spike-VisNet, a novel retina-inspired framework designed to enhance visual recognition capabilities. This framework simulates both the functional and layered structure of the retina. To further enhance this architecture, we integrate the FocusLayer-STDP learning rule, allowing Spike-VisNet to dynamically adjust synaptic weights in response to varying visual stimuli. This rule combines channel attention, inhibition mechanisms, and competitive mechanisms with spike-timing- dependent plasticity (STDP), significantly improving synaptic adaptability and visual recognition performance. Comprehensive evaluations on benchmark datasets demonstrate that Spike-VisNet outperforms other STDPbased SNNs, achieving precision scores of 98.6% on MNIST, 93.29% on ETH-80, and 86.27% on CIFAR-10. These results highlight its effectiveness and robustness, showcasing Spike-VisNet's potential to simulate human visual processing and its applicability to complex real-world visual challenges.
Keyword:
Spiking neural networks
Biomimetic vision
Unsupervised learning
Visual recognition
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6.3
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分享收藏A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule使用对称STDP规则的尖峰神经网络的生物学合理监督学习方法.png)
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