ReviewProgress in neurobiology2019
The roles of supervised machine learning in systems neuroscience.
Review in Progress in neurobiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 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
51 citing papers in PubMed.
- Variations of global brain asymmetry are associated with aging and related diseases.Science advances · 2026Article
- Electrophysiological signatures predict the therapeutic window of deep brain stimulation electrode contacts.NPJ digital medicine · 2025Article
- AI-Empowered Electrochemical Sensors for Biomedical Applications: Technological Advances and Future Challenges.Biosensors · 2025Review
- The NERVE-ML (neural engineering reproducibility and validity essentials for machine learning) checklist: ensuring machine learning advances neural engineeringJournal of neural engineering · 2025Review
- Master classes of the tenth international brain-computer interface meeting: showcasing the research of BCI trainees.Journal of neural engineering · 2025Article
- Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.Frontiers in medicine · 2025Review
- Exploring internal representations of self-supervised networks: few-shot learning abilities and comparison with human semantics and recognition of objects.Frontiers in computational neuroscience · 2025Article
- Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis.Brain and behavior · 2024Article
- Neural decoding and feature selection methods for closed-loop control of avoidance behavior.Journal of neural engineering · 2024Article
- A topological deep learning framework for neural spike decoding.Biophysical journal · 2024Article
- Higher-order interactions between hippocampal CA1 neurons are disrupted in amnestic mice.Nature neuroscience · 2024Article
- System-level time computation and representation in the suprachiasmatic nucleus revealed by large-scale calcium imaging and machine learning.Cell research · 2024Article
- Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.bioRxiv : the preprint server for biology · 2024Article
- Revealing neural dynamical structure ofiScience · 2024Article
- Article
- Machine learning reveals differential effects of depression and anxiety on reward and punishment processing.Scientific reports · 2024Article
- Multi-level feature fusion network for neuronal morphology classification.Frontiers in neuroscience · 2024Article
- Unlocking the neural mechanisms of consumer loan evaluations: an fNIRS and ML-based consumer neuroscience study.Frontiers in human neuroscience · 2024Article
- Transforming medicine: artificial intelligence integration in the peripheral nervous system.Frontiers in neurology · 2024Review
- Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes.PloS one · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: (1) creating solutions to engineering problems, (2) identifying predictive variables, (3) setting benchmarks for simple models of the brain, and (4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.
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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.