aiqbench analysis
This research introduces a hybrid architecture combining pretrained artificial neural networks with spiking neural networks, achieving near-state-of-the-art accuracy on ImageNet while utilizing biologically plausible local learning rules. By decoupling the encoder from the classifier to avoid end-to-end gradient propagation, the study offers a promising pathway for energy-efficient neuromorphic computing that bridges conventional deep learning with event-driven systems.
Hybrid ANN-SNN Pipeline with Local Plasticity
arXiv:2606.20151v2 Announce Type: replace-cross Abstract: 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 framework for adapting powerful pretrained encoders to downstream spiking neural network tasks.
Abstract: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 framework for adapting powerful pretrained encoders to downstream spiking neural network tasks.
Neural and Evolutionary Computing (cs.NE); Artificial Intelligence (cs.AI)
arXiv:2606.20151 [cs.NE]
(or arXiv:2606.20151v2 [cs.NE] for this version)
https://doi.org/10.48550/arXiv.2606.20151 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 199 words
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