Hybrid ANN-SNN Pipeline with Local Plasticity
This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to enable high-performance spiking neural networks (SNNs). The architecture couples a pretrained EfficientNet encoder with a CoLaNET spiking classifier. We convert the encoder's activations into spike trains via rate-coding and train the subsequent SNN classifier using local, biologically inspired learning rules, bypassing end-to-end gradient propagation. This approach achieves 99.09% accuracy on a 64-class ImageNet benchmark, demonstrating performance on par with conventional deep networks. The work presents a biologically plausible and efficient fra...
| Authors | Denis Larionov, Khairutin Shtanchaev, Mikhail Kiselev et al. |
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| Primary category | cs.NE |
| Published | 18 June 2026 |
| Last updated at source | 19 August 2026 |
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