首个类皮层结构的脉冲-注意力视频分类模型，模拟大脑工作机制。
Hybrid Spiking Neural Network -- Transformer Video Classification Model
- 融合脉冲神经网络与Transformer的类脑混合架构
- 提出多种编码方法并设计端到端训练流程
- 开源实现，适合脑启发计算与时序建模研究者
近年来，脉冲神经网络（SNNs）因其对时间信息的处理能力受到广泛关注。本文首次提出一种受大脑皮层柱结构启发的混合架构，用于时序数据分类任务，结合了SNN与Transformer的优势。研究引入多种编码方法以适配该模型，并开发了在训练数据集上进行训练的完整流程。为降低使用门槛，所有代码均已公开发布。
原文摘要 · Abstract (English)
In recent years, Spiking Neural Networks (SNNs) have gathered significant interest due to their temporal understanding capabilities. This work introduces, to the best of our knowledge, the first Cortical Column like hybrid architecture for the Time-Series Data Classification Task that leverages SNNs and is inspired by the brain structure, inspired from the previous hybrid models. We introduce several encoding methods to use with this model. Finally, we develop a procedure for training this network on the training dataset. As an effort to make using these models simpler, we make all the implementations available to the public.
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