ArticleeNeuro
Machine Learning for Neural Decoding.
Article in eNeuro. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 139 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
139 citing papers in PubMed.
- Using transient, effector-specific neural responses to gate decoding for brain-computer interfaces.Journal of neural engineering · 2025Trial
- [Theoretical system of brain-computer interface for basic research].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Generalizable neural decoding for clinically scalable brain-computer interfaces.npj biomedical innovations · 2026Review
- Article
- Article
- Amygdalostriatal transition zone neurons encode sustained cue responses to guide defensive behaviors.Neuron · 2026Article
- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- Towards neuromorphic neurotechnologies: integrating brain-inspired computing with brain-computer interfaces.npj biomedical innovations · 2026Review
- Inferring brain-wide interactions using data-constrained recurrent neural network models.Neuron · 2026Article
- Neuropixels 1536 Channel Quad Base probe reveals brain-wide communication underlying flexible sensorimotor sequences.bioRxiv : the preprint server for biology · 2026Article
- Repetitive neuronal activation regulates cellular maturation state via nuclear reprogramming.Nature communications · 2026Article
- NMDA receptor-dependent Hebbian plasticity refines hippocampal spatial representations during two-dimensional navigation learning.bioRxiv : the preprint server for biology · 2026Article
- Transformations of the spatial activity manifold convey aversive information in CA3.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Entorhinal cortex represents task-relevant remote locations independently of CA1.Nature neuroscience · 2026Article
- Multi-area activity in mouse motor cortex associated with one- and two-handed oromanual dexterity.bioRxiv : the preprint server for biology · 2026Article
- Review of Recent Advances in Implantable Brain-Computer Interfaces for the Restoration of Motor Function in Patients With Paralysis.Medical science monitor : international medical journal of experimental and clinical research · 2026Review
- Neuroscience-Inspired Deep Learning Brain-Machine Interface Decoder.Bioengineering (Basel, Switzerland) · 2026Article
- Decodanda: a Python toolbox for best-practice decoding and geometric analysis of neural representations.bioRxiv : the preprint server for biology · 2026Article
- Limb state accounts for differences between motor imagery and action in motor cortex.medRxiv : the preprint server for health sciences · 2026Article
- Integrating living biomaterials into neuroelectronic systems.Biomedical engineering letters · 2026Review
79 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 to advance engineering applications such as brain-machine interfaces. Our code package is available at github.com/kordinglab/neural_decoding.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.