将对比预测编码与脉冲神经网络结合，实现更符合生物机制的时序数据建模。
Integration of Contrastive Predictive Coding and Spiking Neural Networks
- 用脉冲信号处理输入输出，提升模型生物合理性。
- 在MNIST上区分序列与非序列样本准确率高。
- 兼具分类与编码能力，适合类脑计算研究者。
本研究探讨了对比预测编码（CPC）与脉冲神经网络（SNN）的融合。CPC通过学习数据的预测结构生成有意义表征，而SNN则模拟生物神经系统的动态计算过程。本文旨在构建一种更具生物合理性的预测编码模型，通过脉冲系统处理输入与输出。所提模型在MNIST数据集上测试，成功区分正向序列样本与非序列负样本，表现出高分类性能。结果表明，基于SNN的分类模型可同时作为有效编码机制。项目代码与详细结果见GitHub：https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN
原文摘要 · Abstract (English)
This study examines the integration of Contrastive Predictive Coding (CPC) with Spiking Neural Networks (SNN). While CPC learns the predictive structure of data to generate meaningful representations, SNN mimics the computational processes of biological neural systems over time. In this study, the goal is to develop a predictive coding model with greater biological plausibility by processing inputs and outputs in a spike-based system. The proposed model was tested on the MNIST dataset and achieved a high classification rate in distinguishing positive sequential samples from non-sequential negative samples. The study demonstrates that CPC can be effectively combined with SNN, showing that an SNN trained for classification tasks can also function as an encoding mechanism. Project codes and detailed results can be accessed on our GitHub page: https://github.com/vnd-ogrenme/ongorusel-kodlama/tree/main/CPC_SNN
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