The Yin-Yang dataset was developed for research on biologically plausible error backpropagation and deep learning in spiking neural networks. It serves as an alternative to classic deep learning datasets, especially in algorithm- and model-prototyping scenarios, by providing several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for the deployment on neuromorphic chips with limited network sizes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks.
翻译:日阳数据集的开发是为了研究生物上可信的错误反向反向转换和神经网络的深度学习,它通过提供若干优势,替代传统的深层学习数据集,特别是在算法和模型-蛋白假设情景中。第一,它较小,因此学习速度更快,因此更适合在网络规模有限的神经定态芯片上部署。第二,它显示了利用浅度与深层神经网络相比可以实现的隐蔽性之间的非常明显差距。