mlsanityData to 7 Oct 2026

Artificial neural networks for neuroscientists: A primer

arXiv 2006.01001 ↗First posted 1 Jun 2020q-bio.NC cs.LGcited by 26

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Abstract (arXiv)

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn increasing attention in neuroscience. Besides offering powerful techniques for data analysis, ANNs provide a new approach for neuroscientists to build models for complex behaviors, heterogeneous neural activity and circuit connectivity, as well as to explore optimization in neural systems, in ways that traditional models are not designed for. In this pedagogical Primer, we introduce ANNs and demonstrate how they have been fruitfully deployed to study neuroscientific questions. We first discuss basic concepts and methods of ANNs. Then, with a focus on bringing this mathematical framework closer to neurobiology, we detail how to customize the analysis, structure, and learning of ANNs to better address a wide range of challenges in brain research. To help the readers garner hands-on experience, this Primer is accompanied with tutorial-style code in PyTorch and Jupyter Notebook, covering major topics.

Key facts. Cited by 26 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.

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Citing papers per year: 2021: 2; 2022: 3; 2023: 4; 2024: 7; 2025: 8; 2026: 2.

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  9. 2025A mathematical theory for understanding when abstract representations emerge in neural networkscited by 3
  10. 2021Slow manifolds in recurrent networks encode working memory efficiently and robustlycited by 1
  11. 2022Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifiercited by 1
  12. 2022Scalar Invariant Networks with Zero Biascited by 1
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  4. 2024FPGA Deployment of LFADS for Real-time Neuroscience Experimentscited by 0
  5. 2024Network Representation Learning for Biophysical Neural Network Analysiscited by 0
  6. 2024Skewed Neuronal Heterogeneity Enhances Efficiency On Various Computing Systemscited by 0
  7. 2025Serious Games: Human-AI Interaction, Evolution, and Coevolutioncited by 0
  8. 2025Flexible inference for animal learning rules using neural networkscited by 0
  9. 2025Pursuit of biomarkers of brain diseases: Beyond cohort comparisonscited by 0
  10. 2025Emergence of hybrid computational dynamics through reinforcement learningcited by 0
  11. 2025Mechanistic Interpretability of RNNs emulating Hidden Markov Modelscited by 0
  12. 2025AVM: Towards Structure-Preserving Neural Response Modeling in the Visual Cortex Across Stimuli and Individualscited by 0
  13. 2026A dimensional R2 regression metriccited by 0
  14. 2026Effective pruning of task-trained recurrent neural networks using noisy fluctuations and connection rescalingcited by 0