ArticleProceedings of the National Academy of Sciences of the United States of America2024
Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Addressing degeneracy in rodent studies of cognition.Current opinion in neurobiology · 2026Review
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- A neural circuit framework for economic choice: from building blocks of valuation to compositionality in multitasking.bioRxiv : the preprint server for biology · 2025Article
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- Brain mechanism of foraging: Reward-dependent synaptic plasticity versus neural integration of values.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Reward expectations direct learning and drive operant matching inProceedings of the National Academy of Sciences of the United States of America · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
During foraging behavior, action values are persistently encoded in neural activity and updated depending on the history of choice outcomes. What is the neural mechanism for action value maintenance and updating? Here, we explore two contrasting network models: synaptic learning of action value versus neural integration. We show that both models can reproduce extant experimental data, but they yield distinct predictions about the underlying biological neural circuits. In particular, the neural integrator model but not the synaptic model requires that reward signals are mediated by neural pools selective for action alternatives and their projections are aligned with linear attractor axes in the valuation system. We demonstrate experimentally observable neural dynamical signatures and feasible perturbations to differentiate the two contrasting scenarios, suggesting that the synaptic model is a more robust candidate mechanism. Overall, this work provides a modeling framework to guide future experimental research on probabilistic foraging.
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