ArticleeLife2017
Biologically plausible learning in recurrent neural networks reproduces neural dynamics observed during cognitive 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 41 papers.
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Who cites it
41 citing papers in PubMed.
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- Predictive coding of reward in the hippocampus.Nature · 2026Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
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- Random noise promotes slow heterogeneous synaptic dynamics important for robust working memory computation.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- CONSTRUCTING BIOLOGICALLY CONSTRAINED RNNS VIA DALE'S BACKPROP AND TOPOLOGICALLY-INFORMED PRUNING.bioRxiv : the preprint server for biology · 2025Article
- Ornstein-Uhlenbeck Adaptation as a Mechanism for Learning in Brains and Machines.Entropy (Basel, Switzerland) · 2024Article
- Chaotic neural dynamics facilitate probabilistic computations through sampling.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Specific connectivity optimizes learning in thalamocortical loops.Cell reports · 2024Article
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- Neural spiking for causal inference and learning.PLoS computational biology · 2023Article
- The contribution of the basal ganglia and cerebellum to motor learning: A neuro-computational approach.PLoS computational biology · 2023Article
- Distinguishing Learning Rules with Brain Machine Interfaces.Advances in neural information processing systems · 2022Article
- Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.Nature communications · 2022Article
- Thalamic regulation of frontal interactions in human cognitive flexibility.PLoS computational biology · 2022Article
- Consciousness is supported by near-critical slow cortical electrodynamics.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Thunderstruck: The ACDC model of flexible sequences and rhythms in recurrent neural circuits.PLoS computational biology · 2022Article
- Learning the Synaptic and Intrinsic Membrane Dynamics Underlying Working Memory in Spiking Neural Network Models.Neural computation · 2021Article
- From synapse to network: models of information storage and retrieval in neural circuits.Current opinion in neurobiology · 2021Review
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No grant is acknowledged in the PubMed record.
Abstract
Neural activity during cognitive tasks exhibits complex dynamics that flexibly encode task-relevant variables. Chaotic recurrent networks, which spontaneously generate rich dynamics, have been proposed as a model of cortical computation during cognitive tasks. However, existing methods for training these networks are either biologically implausible, and/or require a continuous, real-time error signal to guide learning. Here we show that a biologically plausible learning rule can train such recurrent networks, guided solely by delayed, phasic rewards at the end of each trial. Networks endowed with this learning rule can successfully learn nontrivial tasks requiring flexible (context-dependent) associations, memory maintenance, nonlinear mixed selectivities, and coordination among multiple outputs. The resulting networks replicate complex dynamics previously observed in animal cortex, such as dynamic encoding of task features and selective integration of sensory inputs. We conclude that recurrent neural networks offer a plausible model of cortical dynamics during both learning and performance of flexible behavior.
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