ArticleeLife2017
Reward-based training of recurrent neural networks for cognitive and value-based tasks.
Article in eLife, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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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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Who cites it
78 citing papers in PubMed.
- Laminar architecture of a decision circuit in the mouse orbitofrontal cortex.Nature communications · 2026Article
- Orbitofrontal-sensory cortical interactions in learning and adaptive decision-making.Trends in cognitive sciences · 2026Review
- Recurrent Neural Network Exploration Strategies During Reinforcement Learning Depend on Network Capacity.Computational brain & behavior · 2026Article
- Distributed neural codes of the 3D position in the marmoset frontal cortex and hippocampus.Nature communications · 2026Article
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- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
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- Primate-informed neural network for visual decision-making.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Neural basis of concurrent deliberation toward a choice and confidence judgment.Nature neuroscience · 2026Article
- On the need for abstract, deep reinforcement learning models in neuroscience.Frontiers in neuroinformatics · 2026Article
- A neural circuit framework for economic choice: from building blocks of valuation to compositionality in multitasking.bioRxiv : the preprint server for biology · 2025Article
- Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks.eLife · 2025Article
- The effects of the post-delay epochs on working memory error reduction.PLoS computational biology · 2025Article
- Neural basis of concurrent deliberation toward a choice and confidence judgment.bioRxiv : the preprint server for biology · 2025Article
- Striatal arbitration between choice strategies guides few-shot adaptation.Nature communications · 2025Article
- A neural implementation model of feedback-based motor learning.Nature communications · 2025Article
- Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation.Nature communications · 2024Article
- A working memory model based on recurrent neural networks using reinforcement learning.Cognitive neurodynamics · 2024Article
- Exploration-Exploitation Mechanisms in Recurrent Neural Networks and Human Learners in Restless Bandit Problems.Computational brain & behavior · 2024Article
- Neural representational geometries reflect behavioral differences in monkeys and recurrent neural networks.Nature communications · 2024Article
18 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Trained neural network models, which exhibit features of neural activity recorded from behaving animals, may provide insights into the circuit mechanisms of cognitive functions through systematic analysis of network activity and connectivity. However, in contrast to the graded error signals commonly used to train networks through supervised learning, animals learn from reward feedback on definite actions through reinforcement learning. Reward maximization is particularly relevant when optimal behavior depends on an animal's internal judgment of confidence or subjective preferences. Here, we implement reward-based training of recurrent neural networks in which a value network guides learning by using the activity of the decision network to predict future reward. We show that such models capture behavioral and electrophysiological findings from well-known experimental paradigms. Our work provides a unified framework for investigating diverse cognitive and value-based computations, and predicts a role for value representation that is essential for learning, but not executing, a task.
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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.