mlsanityData to 7 Oct 2026

Machine learning for neural decoding

arXiv 1708.00909 ↗First posted 2 Aug 2017q-bio.NC cs.LG stat.MLcited by 45

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Abstract (arXiv)

Despite rapid advances in machine learning tools, the majority of neural decoding approaches still use traditional methods. Modern machine learning tools, which are versatile and easy to use, have the potential to significantly improve decoding performance. This tutorial describes how to effectively apply these algorithms for typical decoding problems. We provide descriptions, best practices, and code for applying common machine learning methods, including neural networks and gradient boosting. We also provide detailed comparisons of the performance of various methods at the task of decoding spiking activity in motor cortex, somatosensory cortex, and hippocampus. Modern methods, particularly neural networks and ensembles, significantly outperform traditional approaches, such as Wiener and Kalman filters. Improving the performance of neural decoding algorithms allows neuroscientists to better understand the information contained in a neural population and can help advance engineering applications such as brain machine interfaces.

Key facts. Cited by 45 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2017.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 8; 2023: 9; 2024: 4; 2025: 11; 2026: 10.

45 papers.

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  1. 2023A Unified, Scalable Framework for Neural Population Decodingcited by 59
  2. 2021Neural Latents Benchmark '21: Evaluating latent variable models of neural population activitycited by 57
  3. 2023Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)cited by 20
  4. 2024Neural decoding from stereotactic EEG: accounting for electrode variability across subjectscited by 17
  5. 2025Neural Encoding and Decoding at Scalecited by 17
  6. 2022An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interfacecited by 8
  7. 2025Generalizable, real-time neural decoding with hybrid state-space modelscited by 8
  8. 2022A Topological Deep Learning Framework for Neural Spike Decodingcited by 6
  9. 2022Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIscited by 5
  10. 2025The Alpha-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performancecited by 5
  11. 2025Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologiescited by 5
  12. 2025LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignmentcited by 4
33 more
  1. 2023Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfacescited by 3
  2. 2024Alternators For Sequence Modelingcited by 3
  3. 2024Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognitioncited by 3
  4. 2022Compressed Predictive Information Codingcited by 2
  5. 2022A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Datacited by 2
  6. 2022Repairing Brain-Computer Interfaces with Fault-Based Data Acquisitioncited by 1
  7. 2023Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?cited by 1
  8. 2023Inferring Population Dynamics in Macaque Cortexcited by 1
  9. 2024Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentationcited by 1
  10. 2026Leveraging unlabelled data for generalizable neural population decodingcited by 1
  11. 2021Overcoming the Domain Gap in Contrastive Learning of Neural Action Representationscited by 0
  12. 2021Overcoming the Domain Gap in Neural Action Representationscited by 0
  13. 2022Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysiscited by 0
  14. 2022Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populationscited by 0
  15. 2023Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classificationcited by 0
  16. 2023A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfacescited by 0
  17. 2023Latent Dynamical Implicit Diffusion Processescited by 0
  18. 2023Deep Learning for real-time neural decoding of graspcited by 0
  19. 2025An Algorithm Board in Neural Decodingcited by 0
  20. 2025Intra-neuronal attention within language models Relationships between activation and semanticscited by 0
  21. 2025Polysemy of Synthetic Neurons Towards a New Type of Explanatory Categorical Vector Spacescited by 0
  22. 2025BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfacescited by 0
  23. 2025VoxelFormer: Parameter-Efficient Multi-Subject Visual Decoding from fMRIcited by 0
  24. 2025A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networkscited by 0
  25. 2026RPNT: Robust Pre-trained Neural Transformer -- A Pathway for Generalized Motor Decodingcited by 0
  26. 2026CalM: A Self-Supervised Foundation Model for Population Dynamics in Calcium Imaging Datacited by 0
  27. 2026Inverting Foundation Models of Brain Function with Simulation-Based Inferencecited by 0
  28. 2026Nonlocal operator learning for fMRI encoding and decoding taskscited by 0
  29. 2026Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learningcited by 0
  30. 2026NeuroPB: Scaling Neural Decoding with Pretrained Behavioral Representationscited by 0
  31. 2026Brain-Conditioned Action Policies for Neural Motor Decodingcited by 0
  32. 2026From Neurons to Conversation: Speech Brain-Computer Interfacescited by 0
  33. 2026Association profile conditioning in a set-temporal transformer for cross-session intracortical motor decodingcited by 0