ArticlePLoS computational biology2015
How attention can create synaptic tags for the learning of working memories in sequential tasks.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Fine-Pruning: A biologically inspired algorithm for personalization of machine learning models.Patterns (New York, N.Y.) · 2025Article
- Biologically plausible gated recurrent neural networks for working memory and learning-to-learn.PloS one · 2024Article
- A Circuit Model for Working Memory Based on Hybrid Positive and Negative-Derivative Feedback Mechanism.Brain sciences · 2022Article
- Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Learning at Variable Attentional Load Requires Cooperation of Working Memory, Meta-learning, and Attention-augmented Reinforcement Learning.Journal of cognitive neuroscience · 2021Article
- Modifying the mMolecular psychiatry · 2021Article
- Efficient Spike-Driven Learning With Dendritic Event-Based Processing.Frontiers in neuroscience · 2021Article
- A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity.PLoS computational biology · 2020Article
- Review
- Feature-specific prediction errors and surprise across macaque fronto-striatal circuits.Nature communications · 2019Article
- A Closed-Loop Toolchain for Neural Network Simulations of Learning Autonomous Agents.Frontiers in computational neuroscience · 2019Article
- Reply to 'Can neocortical feedback alter the sign of plasticity?'Nature reviews. Neuroscience · 2018Article
- An Integrative Framework for Sensory, Motor, and Cognitive Functions of the Posterior Parietal Cortex.Neuron · 2018Review
- Control of synaptic plasticity in deep cortical networks.Nature reviews. Neuroscience · 2018Review
- Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules.Frontiers in neural circuits · 2018Review
- Multi-Timescale Memory Dynamics Extend Task Repertoire in a Reinforcement Learning Network With Attention-Gated Memory.Frontiers in computational neuroscience · 2018Article
- The influence of attention and reward on the learning of stimulus-response associations.Scientific reports · 2017Article
- The laminar integration of sensory inputs with feedback signals in human cortex.Brain and cognition · 2017Article
- Learning a New Selection Rule in Visual and Frontal Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2016Article
- Toward an Integration of Deep Learning and Neuroscience.Frontiers in computational neuroscience · 2016Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Intelligence is our ability to learn appropriate responses to new stimuli and situations. Neurons in association cortex are thought to be essential for this ability. During learning these neurons become tuned to relevant features and start to represent them with persistent activity during memory delays. This learning process is not well understood. Here we develop a biologically plausible learning scheme that explains how trial-and-error learning induces neuronal selectivity and working memory representations for task-relevant information. We propose that the response selection stage sends attentional feedback signals to earlier processing levels, forming synaptic tags at those connections responsible for the stimulus-response mapping. Globally released neuromodulators then interact with tagged synapses to determine their plasticity. The resulting learning rule endows neural networks with the capacity to create new working memory representations of task relevant information as persistent activity. It is remarkably generic: it explains how association neurons learn to store task-relevant information for linear as well as non-linear stimulus-response mappings, how they become tuned to category boundaries or analog variables, depending on the task demands, and how they learn to integrate probabilistic evidence for perceptual decisions.
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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.