ArticleeLife2021
Gated recurrence enables simple and accurate sequence prediction in stochastic, changing, and structured environments.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Understanding learning through uncertainty and bias.Communications psychology · 2025Review
- Computation noise promotes zero-shot adaptation to uncertainty during decision-making in artificial neural networks.Science advances · 2024Article
- Thalamocortical architectures for flexible cognition and efficient learning.Trends in cognitive sciences · 2024Review
- Two Determinants of Dynamic Adaptive Learning for Magnitudes and Probabilities.Open mind : discoveries in cognitive science · 2024Article
- Development and validation of predictive model based on deep learning method for classification of dyslipidemia in Chinese medicine.Health information science and systems · 2023Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
From decision making to perception to language, predicting what is coming next is crucial. It is also challenging in stochastic, changing, and structured environments; yet the brain makes accurate predictions in many situations. What computational architecture could enable this feat? Bayesian inference makes optimal predictions but is prohibitively difficult to compute. Here, we show that a specific recurrent neural network architecture enables simple and accurate solutions in several environments. This architecture relies on three mechanisms: gating, lateral connections, and recurrent weight training. Like the optimal solution and the human brain, such networks develop internal representations of their changing environment (including estimates of the environment's latent variables and the precision of these estimates), leverage multiple levels of latent structure, and adapt their effective learning rate to changes without changing their connection weights. Being ubiquitous in the brain, gated recurrence could therefore serve as a generic building block to predict in real-life environments.
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Registered trials
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