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The Yin-Yang dataset
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The Yin-Yang dataset
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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.
Forward citations
Cited by 3 Pith papers
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Intrinsic-Noise Consolidation: A Doob-Barrier-Conditioned Diffusion Turns Analog Device Noise into a Continual-Learning Resource
Conditioning per-synapse weight dynamics on a memory-critical barrier via a Doob h-transform turns intrinsic analog device noise into a non-monotonic consolidation resource for continual learning.
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Fully Trainable Deep Differentiable Logic Gate Networks and Lookup Table Networks
Training connections as well as gate/LUT functions lets deep logic-gate and lookup-table networks match fixed-wiring accuracy with far fewer gates (e.g. 8k vs ~384k on MNIST).
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Physical Neural Networks Need Nonlinearity, Amplification, and Suppression for Learning
Simulations show physical neural networks need nonlinearity, amplification, and suppression for learning, with physically plausible circuit designs presented.
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