ReviewNature reviews. Neuroscience2021
If deep learning is the answer, what is the question?
Review in Nature reviews. Neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 133 papers, 2 of them syntheses that pooled it.
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
133 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Decoding the application of deep learning in neuroscience: a bibliometric analysis.Frontiers in computational neuroscience · 2024Pooled it
- Pooled it
- The neuroscience of transformers.Cognitive neurodynamics · 2026Article
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Spontaneous emergence of context-dependent statistical learning in humans and neural networks.iScience · 2026Article
- Brain-AI convergence: Generative world models and hierarchical attention for human intelligence.Patterns (New York, N.Y.) · 2026Review
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Article
- Article
- Recognising dog movement with behaviour-specific machine learning models: bout length as a biologically relevant parameter for window size.BMC veterinary research · 2026Article
- A mathematical theory for understanding when abstract representations emerge in neural networks.ArXiv · 2026Article
- Understanding human metacontrol and its pathologies using deep neural networks.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Shared sensitivity to data distribution during learning in humans and transformer networks.Nature human behaviour · 2026Article
- A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.Nature communications · 2026Article
- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
- Data-driven ANN-based visual decoding enables unsupervised functional alignment.Communications biology · 2026Article
- Exploring synergies: Advancing neuroscience with machine learning.Signal processing · 2026Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- WhenAPL bioengineering · 2025Review
- Digital dementia and testing of cognitive intervention for degenerating neural networks.NPJ systems biology and applications · 2025Article
73 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
3 authors.
Funding
Abstract
Neuroscience research is undergoing a minor revolution. Recent advances in machine learning and artificial intelligence research have opened up new ways of thinking about neural computation. Many researchers are excited by the possibility that deep neural networks may offer theories of perception, cognition and action for biological brains. This approach has the potential to radically reshape our approach to understanding neural systems, because the computations performed by deep networks are learned from experience, and not endowed by the researcher. If so, how can neuroscientists use deep networks to model and understand biological brains? What is the outlook for neuroscientists who seek to characterize computations or neural codes, or who wish to understand perception, attention, memory and executive functions? In this Perspective, our goal is to offer a road map for systems neuroscience research in the age of deep learning. We discuss the conceptual and methodological challenges of comparing behaviour, learning dynamics and neural representations in artificial and biological systems, and we highlight new research questions that have emerged for neuroscience as a direct consequence of recent advances in machine learning.
Indexed as
Identifiers
33199854What 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.