Toward the neural implementation of structure learning
Abstract
Despite significant advances in neuroscience, the neural bases of intelligence remain poorly understood. Arguably the most elusive aspect of intelligence is the ability to make robust inferences that go far beyond one's experience. Animals categorize objects, learn to vocalize and may even estimate causal relationships - all in the face of data that is often ambiguous and sparse. Such inductive leaps are thought to result from the brain's ability to infer latent structure that governs the environment. However, we know little about the neural computations that underlie this ability. Recent advances in developing computational frameworks that can support efficient structure learning and inductive inference may provide insight into the underlying component processes and help pave the path for uncovering their neural implementation.
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Relating Bayesian inference and learning, especially structure learning in nonparametric models, to its implementation in the brain remains challenging.
“However, relating Bayesian inference and learning, especially structure learning in nonparametric models, to its implementation in the brain remains challenging107.”
From Cognitive computational neuroscience (2018) · cites this paper as [107]
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- Authors
- Tervo, D. G. R., Tenenbaum, J. B., & Gershman, S. J.
- Affiliations
- Howard Hughes Medical Institute (US)
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- Harvard University (US)
- Corresponding author
- Tervo, D Gowanlock R
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2016-04
- Volume
- 37
- Pages
- 99-105
- ISSN
- 0959-4388 (ISSN-L)
- OpenAlex
- W2286353276
- Cited by
- 106
- References
- 68
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 5 · 2023: 14 · 2022: 10 · 2021: 11 · 2020: 18 · 2019: 13 · 2018: 8 · 2017: 7 · 2016: 5
- FWCI
- 1.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Current Opinion in Neurobiology (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Howard Hughes Medical Institute
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Bayesian Modeling and Causal Inference
- Life Sciences › Neuroscience › Cognitive Neuroscience › Memory and Neural Mechanisms
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- structure learning, inductive reasoning, computational models of learning, data sparsity, causal inference, object classification, vocal learning, neural computation
- MeSH terms
- Animals, Brain, Learning, Models, Neurological
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