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Advancing NeuroAI through developmental alignment.

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Τίτλος: Advancing NeuroAI through developmental alignment.
Συγγραφείς: Ayzenberg V; Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA. Electronic address: vayzenb@temple.edu., Bonner MF; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA., Bayet L; Department of Neuroscience, American University, Washington, DC, USA.
Πηγή: Neuron [Neuron] 2026 Oct 07; Vol. 114 (19), pp. 3550-3568. Date of Electronic Publication: 2026 Jul 21.
Τύπος έκδοσης: Journal Article; Review
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Cell Press Country of Publication: United States NLM ID: 8809320 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-4199 (Electronic) Linking ISSN: 08966273 NLM ISO Abbreviation: Neuron Subsets: MEDLINE
Imprint Name(s): Original Publication: [Cambridge, Mass. : Cell Press, c1988-
Ιατρικοί όροι (MeSH): Artificial Intelligence*/trends , Intelligence*/physiology , Neurosciences*/trends , Neurosciences*/methods , Child Development*/physiology , Brain*/physiology , Brain*/growth & development , Models, Neurological*, Learning/physiology ; Humans ; Animals
Περίληψη: The goal of artificial intelligence (AI) modeling in neuroscience, or NeuroAI, is to uncover the factors that give rise to human-level intelligence. However, current models overwhelmingly focus on simulating adulthood, the end state of intelligence, and often do not consider how this state was achieved in the first place. We argue that, to understand adult intelligence, it is important to model the developmental process by which intelligence arose. Here, we describe how developmental changes in children's neural architecture, experiences, and learning objectives are adaptively suited to support rapid learning and illustrate how these principles can be incorporated into the AI engineering process. Finally, we describe the early developing capacities of children and how these capacities provide an ideal set of benchmarks for evaluating AI models. Together, by modeling the developmental process by which humans achieve intelligence, we may build more mechanistically plausible models as well as improve the capabilities of AI.
(Copyright © 2026 Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of interests The authors declare no competing interests.
Contributed Indexing: Keywords: child development; computational cognitive science; computational modeling; computational neuroscience; developmental cognitive neuroscience
Entry Date(s): Date Created: 20260721 Date Completed: 20261007 Latest Revision: 20261007
Update Code: 20261008
DOI: 10.1016/j.neuron.2026.07.003
PMID: 42480537
Βάση Δεδομένων: MEDLINE
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  Data: Advancing NeuroAI through developmental alignment.
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  Data: <searchLink fieldCode="AU" term="%22Ayzenberg+V%22">Ayzenberg V</searchLink>; Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA. Electronic address: vayzenb@temple.edu.<br /><searchLink fieldCode="AU" term="%22Bonner+MF%22">Bonner MF</searchLink>; Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, USA.<br /><searchLink fieldCode="AU" term="%22Bayet+L%22">Bayet L</searchLink>; Department of Neuroscience, American University, Washington, DC, USA.
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  Data: <searchLink fieldCode="JN" term="%228809320%22">Neuron</searchLink> [Neuron] 2026 Oct 07; Vol. 114 (19), pp. 3550-3568. <i>Date of Electronic Publication: </i>2026 Jul 21.
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  Data: The goal of artificial intelligence (AI) modeling in neuroscience, or NeuroAI, is to uncover the factors that give rise to human-level intelligence. However, current models overwhelmingly focus on simulating adulthood, the end state of intelligence, and often do not consider how this state was achieved in the first place. We argue that, to understand adult intelligence, it is important to model the developmental process by which intelligence arose. Here, we describe how developmental changes in children's neural architecture, experiences, and learning objectives are adaptively suited to support rapid learning and illustrate how these principles can be incorporated into the AI engineering process. Finally, we describe the early developing capacities of children and how these capacities provide an ideal set of benchmarks for evaluating AI models. Together, by modeling the developmental process by which humans achieve intelligence, we may build more mechanistically plausible models as well as improve the capabilities of AI.<br /> (Copyright © 2026 Elsevier Inc. All rights reserved.)
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  Data: Declaration of interests The authors declare no competing interests.
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