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- W4225985364 abstract "SNNs have achieved great attention in recent years as they contain neurons more like those in the brain and use spikes to encode and transmit information efficiently among neurons with lower energy consumption. A Retina-LGN-V1 structure-like spiking neuron network (RLVSL-SNN) is proposed in this paper. It is inspired by the structure of mammalian primary visual pathway, and simulates different biological structures of Retina, LGN and V1. Noise reduction circuit and light adaptation circuit are also simulated for enhancing the robustness of its extracted features. RLVSL-SNN is a bio-plausible neuron network as it has firing rates of neurons in each layer that are similar to those of biological experiments. Besides, a full-connected SNN (FC SNN) is implemented following RLVSL-SNN for classification to evaluate the extracted features. The additive spiking timing dependent plasticity (STDP) learning rules and the ANN-to-SNN conversion method are utilized to train RLVSL-SNN and FC SNN, respectively. The experiments on MNIST dataset have verified that RLVSL-SNN is comparable to AlexNet for classification by features from spikes." @default.
- W4225985364 created "2022-05-05" @default.
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- W4225985364 date "2021-12-22" @default.
- W4225985364 modified "2023-09-26" @default.
- W4225985364 title "A Retina-LGN-V1 Structure-like Spiking Neuron Network for Image Feature Extraction" @default.
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- W4225985364 doi "https://doi.org/10.1145/3511176.3511197" @default.
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