ReviewNature neuroscience2019
A deep learning framework for neuroscience.
Review in Nature neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 413 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
413 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A bibliometric analysis of studies on artificial intelligence in neuroscience.Frontiers in neurology · 2025Pooled it
- Decoding the application of deep learning in neuroscience: a bibliometric analysis.Frontiers in computational neuroscience · 2024Pooled it
- The neuroscience of transformers.Cognitive neurodynamics · 2026Article
- Instructive signals drive learning through behavioral timescale synaptic plasticity in the hippocampus and beyond.Current research in neurobiology · 2026Article
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Review
- Deep learning without weight symmetry.PNAS nexus · 2026Article
- A DEEP NEURAL NETWORK FOR PREDICTING CONTINUOUS HUMAN EEG ACROSS THE AUDITORY PATHWAY IN RESPONSE TO SOUND.ArXiv · 2026Article
- Article
- Article
- De novo motor learning as controller synthesis: a conceptual framework.Biological cybernetics · 2026Article
- Selective perturbation of mirror and non-mirror neurons in an in silico model of the action observation network.iScience · 2026Article
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Learning shapes neural geometry in the primate prefrontal cortex.Nature neuroscience · 2026Article
- Generative AI as a transformational logic for cognitive neuroscience.Communications biology · 2026Review
- Hierarchical optimization predicts plasticity in the macaque inferior temporal cortex following object training.Nature communications · 2026Article
- Spike-based alignment learning solves the weight transport problem.Nature communications · 2026Article
- The primate claustrum as a hub for precision weighting of prediction errors across the cortical hierarchy.PLoS biology · 2026Article
- Neural timescales from a computational perspective.Nature neuroscience · 2026Review
- Critical period plasticity enables credit assignment.bioRxiv : the preprint server for biology · 2026Article
353 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
32 authors.
Funding
Abstract
Systems neuroscience seeks explanations for how the brain implements a wide variety of perceptual, cognitive and motor tasks. Conversely, artificial intelligence attempts to design computational systems based on the tasks they will have to solve. In artificial neural networks, the three components specified by design are the objective functions, the learning rules and the architectures. With the growing success of deep learning, which utilizes brain-inspired architectures, these three designed components have increasingly become central to how we model, engineer and optimize complex artificial learning systems. Here we argue that a greater focus on these components would also benefit systems neuroscience. We give examples of how this optimization-based framework can drive theoretical and experimental progress in neuroscience. We contend that this principled perspective on systems neuroscience will help to generate more rapid progress.
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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.