The Yin-Yang dataset
This dataset addresses the need for efficient prototyping tools in biologically inspired neural network research, though it is incremental as an alternative to existing datasets.
The authors introduced the Yin-Yang dataset to facilitate research on biologically plausible error backpropagation and deep learning in spiking neural networks, offering advantages such as faster learning and clear accuracy gaps between shallow and deep networks.
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 early-stage prototyping scenarios for both network models and hardware platforms, for which it provides several advantages. First, it is smaller and therefore faster to learn, thereby being better suited for small-scale exploratory studies in both software simulations and hardware prototypes. Second, it exhibits a very clear gap between the accuracies achievable using shallow as compared to deep neural networks. Third, it is easily transferable between spatial and temporal input domains, making it interesting for different types of classification scenarios.