ReviewNature reviews. Neuroscience2023
The neuroconnectionist research programme.
Review in Nature reviews. Neuroscience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 102 papers.
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Who cites it
102 citing papers in PubMed.
- Review
- Making models disagree to learn how brains compute.Nature reviews. Neuroscience · 2026Review
- Top-down feedback in deep neural networks leads to functional differences during audiovisual integration.eLife · 2026Article
- A single computational objective can produce specialization of streams in visual cortex.Nature communications · 2026Article
- Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.PLoS computational biology · 2026Article
- Article
- Shared representations in brains and models reveal a two-route cortical organization during scene perception.Communications biology · 2026Article
- Modeling attention and binding in the brain through bidirectional recurrent gating.Nature communications · 2026Article
- Article
- Perceptual and conceptual influences on memory judgments.Communications psychology · 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
- Infants have rich visual categories in ventrotemporal cortex at 2 months of age.Nature neuroscience · 2026Article
- Studying infant vision in the scanner and in silico reveals the richness of early brain function.Nature neuroscience · 2026Article
- Achieving more human brain-like vision via human EEG representational alignment.Communications biology · 2026Article
- A 7T fMRI dataset of synthetic images for out-of-distribution modeling of vision.Nature communications · 2026Article
- Behavioral differences between humans and machines arise early in visual processing.Journal of vision · 2026Article
- Joint modelling of brain and behaviour dynamics with artificial intelligence.Nature reviews. Neuroscience · 2026Review
- What makes memories vivid?Journal of experimental psychology. General · 2026Article
- Addressing low statistical power in computational modelling studies in psychology and neuroscience.Nature human behaviour · 2026Article
42 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial neural networks (ANNs) inspired by biology are beginning to be widely used to model behavioural and neural data, an approach we call 'neuroconnectionism'. ANNs have been not only lauded as the current best models of information processing in the brain but also criticized for failing to account for basic cognitive functions. In this Perspective article, we propose that arguing about the successes and failures of a restricted set of current ANNs is the wrong approach to assess the promise of neuroconnectionism for brain science. Instead, we take inspiration from the philosophy of science, and in particular from Lakatos, who showed that the core of a scientific research programme is often not directly falsifiable but should be assessed by its capacity to generate novel insights. Following this view, we present neuroconnectionism as a general research programme centred around ANNs as a computational language for expressing falsifiable theories about brain computation. We describe the core of the programme, the underlying computational framework and its tools for testing specific neuroscientific hypotheses and deriving novel understanding. Taking a longitudinal view, we review past and present neuroconnectionist projects and their responses to challenges and argue that the research programme is highly progressive, generating new and otherwise unreachable insights into the workings of the brain.
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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.