ReviewNature reviews. Neuroscience2021
Biological constraints on neural network models of cognitive function.
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 72 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.
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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
72 citing papers in PubMed.
- A brain-constrained neural model of cognition and language with NEST: transitioning from the Felix framework.Cognitive neurodynamics · 2026Article
- What is so hard about abstract words, anyways? A neuromechanistic explanation using brain-constrained neural network models.Cognitive neurodynamics · 2026Article
- Hierarchical learning creates invariant schema within plastic neural networks.Journal of computational neuroscience · 2026Article
- Developmental deviations of association-network structural connectivity in youths with ADHD predict symptom and treatment outcomes.Nature biomedical engineering · 2026Article
- Molecular and cellular correlates of human cortical lateralization.Communications biology · 2026Article
- Shared genetic architecture between ADHD and intelligence varies across ADHD subtypes.BMC medicine · 2026Article
- A Deep Learning Framework for Spatiotemporal Modeling of Visual Task fMRI.bioRxiv : the preprint server for biology · 2026Article
- E/I imbalance and internal noise cause weak neural representations and face recognition challenges in ASD.Communications biology · 2026Article
- Competitive interactions shape mammalian brain network dynamics and computation.Nature neuroscience · 2026Article
- Article
- Interrogating functional connectivity of in vitro neural glia tissue model modulated through integrative control of matrix stiffness and a neurotrophic factor.Biomaterials · 2026Article
- Advancing neuroengineering with Neuromorphic Twins.Nature communications · 2026Review
- From Spontaneous Ignitions to Sensorimotor Cell Assemblies via Dopamine: A Spiking Neurocomputational Model of Infants' Hand Action Acquisition.Brain sciences · 2026Article
- From cortical and white matter structure to meaning: a brain-constrained neural network of semantic grounding in action and perception.Frontiers in human neuroscience · 2026Article
- The potential role of aberrant microglial synaptic pruning in the neurodevelopmental pathogenesis of tourette syndrome.Frontiers in neuroscience · 2026Review
- Brain-AI Alignment in Naturalistic Movies.bioRxiv : the preprint server for biology · 2025Article
- The molecular and cellular underpinnings of human brain lateralization.bioRxiv : the preprint server for biology · 2025Article
- Complex functions of human lateral frontopolar cortex.Brain : a journal of neurology · 2025Review
- Mapping EEG Metrics to Human Affective and Cognitive Models: An Interdisciplinary Scoping Review from a Cognitive Neuroscience Perspective.Biomimetics (Basel, Switzerland) · 2025Review
- Transcranial Magnetic Stimulation Alleviates Spatial Learning and Memory Impairment by Inhibiting the Expression of SARM1 in Rats with Cerebral Ischemia-Reperfusion Injury.Neuromolecular medicine · 2025Article
12 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
4 authors.
Funding
Abstract
Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent years and even achieve human-like performance on complex perceptual and cognitive tasks, their similarity to aspects of brain anatomy and physiology is imperfect. Here, we discuss different types of neural models, including localist, auto-associative, hetero-associative, deep and whole-brain networks, and identify aspects under which their biological plausibility can be improved. These aspects range from the choice of model neurons and of mechanisms of synaptic plasticity and learning to implementation of inhibition and control, along with neuroanatomical properties including areal structure and local and long-range connectivity. We highlight recent advances in developing biologically grounded cognitive theories and in mechanistically explaining, on the basis of these brain-constrained neural models, hitherto unaddressed issues regarding the nature, localization and ontogenetic and phylogenetic development of higher brain functions. In closing, we point to possible future clinical applications of brain-constrained modelling.
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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.