ReviewCurrent opinion in neurobiology2022
Toward the explainability, transparency, and universality of machine learning for behavioral classification in neuroscience.
Review in Current opinion in neurobiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
What it found
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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.
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Who cites it
39 citing papers in PubMed.
- Development and validation of venous thrombosis risk prediction model and scale for lung cancer patients in intensive care unit based on interpretable machine learning.Journal of thoracic disease · 2026Article
- Autobehaver: An AI-Based Pipeline for Animal Behavior Analysis.bioRxiv : the preprint server for biology · 2026Article
- An interpretable machine learning model for predicting emergence agitation in children: a multicenter development and validation study.BMC anesthesiology · 2026Article
- Attack Repertoires in Outbred Male CD1-Mice Are Associated with Nucleus Accumbens Neuroligin-2.eNeuro · 2026Article
- Early risk stratification of acute kidney injury severity in patients with acute myocardial infarction and diabetes using machine learning.Scientific reports · 2026Article
- Can multiparametric FDG-PET/MRI analysis really enhance the prediction of myocardial recovery after CTO revascularization? A machine learning study.Zeitschrift fur medizinische Physik · 2026Article
- An explainable machine learning framework utilizing ultrasound radiomics for the preoperative differentiation between granulomatous lobular mastitis and breast cancer.Frontiers in oncology · 2026Article
- Deep neural networks and stochastic methods for cognitive modeling of rat behavioral dynamics inCognitive neurodynamics · 2025Article
- Rapid, Open-Source, and Automated Quantification of the Head Twitch Response in C57BL/6J Mice Using DeepLabCut and Simple Behavioral Analysis.ACS pharmacology & translational science · 2025Article
- Rapid, open-source, and automated quantification of the head twitch response in C57BL/6J mice using DeepLabCut and Simple Behavioral Analysis.bioRxiv : the preprint server for biology · 2025Article
- Prediction of postpartum depression in women: development and validation of multiple machine learning models.Journal of translational medicine · 2025Article
- Optimized machine learning framework for cardiovascular disease diagnosis: a novel ethical perspective.BMC cardiovascular disorders · 2025Article
- Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression.Nature communications · 2025Article
- Article
- Identifying major depressive disorder among US adults living alone using stacked ensemble machine learning algorithms.Frontiers in public health · 2025Article
- Toward a framework for risk mitigation of potential misuse of artificial intelligence in biomedical research.Nature machine intelligence · 2024Article
- Pupillometry as a biomarker of postural control: Deep-learning models reveal side-specific pupillary responses to increased intensity of balance tasks.Psychophysiology · 2024Article
- Classification of psychedelics and psychoactive drugs based on brain-wide imaging of cellular c-Fos expression.bioRxiv : the preprint server for biology · 2024Article
- Machine learning in predicting postoperative complications in Crohn's disease.World journal of gastrointestinal surgery · 2024Article
- Simple Behavioral Analysis (SimBA) as a platform for explainable machine learning in behavioral neuroscience.Nature neuroscience · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field toward a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive toward behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a diagnostic resource toward explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.
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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.