# Hybrid ANN-SNN Pipeline with Local Plasticity

Source: [Hybrid ANN-SNN Pipeline with Local Plasticity](<https://www.sota2.com/research/paper/6a34cd4d206542251d7c61a2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-06-20T05:00:13.953Z

Authors: Denis Larionov, Khairutin Shtanchaev, Mikhail Kiselev, Mikhail Korovin, Ivan Tugoy

Published: 2026-06-18T12:13:19.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2606.20151>)

[PDF](<https://arxiv.org/pdf/2606.20151.pdf>)