# Machine learning for neural decoding

arXiv: https://arxiv.org/abs/1708.00909 · HTML: https://mlsanity.com/paper/1708.00909/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2017.

## Built on by (most cited first, 30 of 45)

- [A Unified, Scalable Framework for Neural Population Decoding](https://mlsanity.com/paper/2310.16046/) (2023)
- [Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity](https://mlsanity.com/paper/2109.04463/) (2021)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Neural Encoding and Decoding at Scale](https://mlsanity.com/paper/2504.08201/) (2025)
- [Neural decoding from stereotactic EEG: accounting for electrode variability across subjects](https://mlsanity.com/paper/2411.10458/) (2024)
- [Generalizable, real-time neural decoding with hybrid state-space models](https://mlsanity.com/paper/2506.05320/) (2025)
- [An Energy-Efficient Spiking Neural Network for Finger Velocity Decoding for Implantable Brain-Machine Interface](https://mlsanity.com/paper/2210.06287/) (2022)
- [A Topological Deep Learning Framework for Neural Spike Decoding](https://mlsanity.com/paper/2212.05037/) (2022)
- [Challenges and Opportunities of Edge AI for Next-Generation Implantable BMIs](https://mlsanity.com/paper/2204.02362/) (2022)
- [The Alpha-Alternator: Dynamic Adaptation To Varying Noise Levels In Sequences Using The Vendi Score For Improved Robustness and Performance](https://mlsanity.com/paper/2502.04593/) (2025)
- [Embracing Trustworthy Brain-Agent Collaboration as Paradigm Extension for Intelligent Assistive Technologies](https://mlsanity.com/paper/2510.22095/) (2025)
- [LLMs Help Alleviate the Cross-Subject Variability in Brain Signal and Language Alignment](https://mlsanity.com/paper/2501.02621/) (2025)
- [Combining SNNs with Filtering for Efficient Neural Decoding in Implantable Brain-Machine Interfaces](https://mlsanity.com/paper/2312.15889/) (2023)
- [Alternators For Sequence Modeling](https://mlsanity.com/paper/2405.11848/) (2024)
- [Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition](https://mlsanity.com/paper/2410.11868/) (2024)
- [Compressed Predictive Information Coding](https://mlsanity.com/paper/2203.02051/) (2022)
- [A Spiking Neural Network based on Neural Manifold for Augmenting Intracortical Brain-Computer Interface Data](https://mlsanity.com/paper/2204.05132/) (2022)
- [Repairing Brain-Computer Interfaces with Fault-Based Data Acquisition](https://mlsanity.com/paper/2203.10677/) (2022)
- [Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU?](https://mlsanity.com/paper/2301.11688/) (2023)
- [Inferring Population Dynamics in Macaque Cortex](https://mlsanity.com/paper/2304.06040/) (2023)
- [Conditionally-Conjugate Gaussian Process Factor Analysis for Spike Count Data via Data Augmentation](https://mlsanity.com/paper/2405.11683/) (2024)
- [Leveraging unlabelled data for generalizable neural population decoding](https://mlsanity.com/paper/2607.14086/) (2026)
- [Overcoming the Domain Gap in Contrastive Learning of Neural Action Representations](https://mlsanity.com/paper/2111.14595/) (2021)
- [Overcoming the Domain Gap in Neural Action Representations](https://mlsanity.com/paper/2112.01176/) (2021)
- [Deep Direct Discriminative Decoders for High-dimensional Time-series Data Analysis](https://mlsanity.com/paper/2205.10947/) (2022)
- [Integrating Statistical and Machine Learning Approaches to Identify Receptive Field Structure in Neural Populations](https://mlsanity.com/paper/2208.12025/) (2022)
- [Tree-Based Learning on Amperometric Time Series Data Demonstrates High Accuracy for Classification](https://mlsanity.com/paper/2302.02650/) (2023)
- [A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces](https://mlsanity.com/paper/2303.04391/) (2023)
- [Latent Dynamical Implicit Diffusion Processes](https://mlsanity.com/paper/2306.07077/) (2023)
- [Deep Learning for real-time neural decoding of grasp](https://mlsanity.com/paper/2311.01061/) (2023)

## 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.