mlsanityData to 7 Oct 2026

The Yin-Yang dataset

arXiv 2102.08211 ↗First posted 16 Feb 2021cs.AI cs.NE q-bio.NCcited by 10

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 12 of its 29 references that are in the map. First posted on arXiv 16 Feb 2021.

Builds on

12 of 29 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2016Equilibrium Propagation: Bridging the Gap Between Energy-Based Models and Backpropagationcited by 251
  4. 2018Dendritic cortical microcircuits approximate the backpropagation algorithmcited by 96
  5. 2016Towards deep learning with segregated dendritescited by 71
  6. 2020Event-Based Backpropagation can compute Exact Gradients for Spiking Neural Networkscited by 59
  7. 2019Fast and energy-efficient neuromorphic deep learning with first-spike timescited by 52
  8. 2020Surrogate gradients for analog neuromorphic computingcited by 38
  9. 2017Neuromorphic Hardware In The Loop: Training a Deep Spiking Network on the BrainScaleS Wafer-Scale Systemcited by 19
  10. 2019Versatile emulation of spiking neural networks on an accelerated neuromorphic substratecited by 15
  11. 2017An Accelerated Analog Neuromorphic Hardware System Emulating NMDA- and Calcium-Based Non-Linear Dendritescited by 4
  12. 2016Precise neural network computation with imprecise analog devicescited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 2; 2026: 4.

10 papers.

  1. 2024DelGrad: Exact event-based gradients for training delays and weights on spiking neuromorphic hardwarecited by 9
  2. 2022Learning efficient backprojections across cortical hierarchies in real timecited by 8
  3. 2024Stable Learning Using Spiking Neural Networks Equipped With Affine Encoders and Decoderscited by 5
  4. 2025Causal pieces: analysing and improving spiking neural networks piece by piececited by 2
  5. 2026A feedback control optimizer for online and hardware-aware training of Spiking Neural Networkscited by 2
  6. 2026Training event-based neural networks with exact gradients via Differentiable ODE Solving in JAXcited by 2
  7. 2025Koopman Autoencoders Learn Neural Representation Dynamicscited by 1
  8. 2024Layer Ensemble Averaging for Improving Memristor-Based Artificial Neural Network Performancecited by 0
  9. 2026Implicit Hypothesis Testing and Divergence Preservation in Neural Network Representationscited by 0
  10. 2026Mixed-signal implementation of feedback-control optimizer for single-layer Spiking Neural Networkscited by 0