ArticleNature neuroscience2025
Latent circuit inference from heterogeneous neural responses during cognitive tasks.
Article in Nature neuroscience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Data-driven reduced modeling of neural dynamics.Nature communications · 2026Article
- Hippocampal engrams configure prefrontal context representations to guide flexible decisions.bioRxiv : the preprint server for biology · 2026Article
- Computational neural dynamics of goal-directed visual attention in macaques.Science advances · 2026Article
- The cost of efficiency in flexible neural representations.bioRxiv : the preprint server for biology · 2026Article
- Neural circuit models for evidence accumulation through choice-selective sequences.Nature communications · 2026Article
- A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks.bioRxiv : the preprint server for biology · 2026Article
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- Structured interconnectivity optimizes neural geometry for balancing specificity and generalization in object recognition.Communications biology · 2025Article
- Neural dynamics outside task-coding dimensions drive decision trajectories through transient amplification.bioRxiv : the preprint server for biology · 2025Article
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- Convergent neural dynamical systems for task control in artificial networks and human brains.bioRxiv : the preprint server for biology · 2025Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
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- Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.ArXiv · 2025Article
- Single-unit activations confer inductive biases for emergent circuit solutions to cognitive tasks.Nature machine intelligence · 2025Article
- Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.Science advances · 2024Article
- Interpretable deep learning for deconvolutional analysis of neural signals.bioRxiv : the preprint server for biology · 2024Article
- Dynamical mechanisms of how an RNN keeps a beat, uncovered with a low-dimensional reduced model.Scientific reports · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Higher cortical areas carry a wide range of sensory, cognitive and motor signals mixed in heterogeneous responses of single neurons tuned to multiple task variables. Dimensionality reduction methods that rely on correlations between neural activity and task variables leave unknown how heterogeneous responses arise from connectivity to drive behavior. We develop the latent circuit model, a dimensionality reduction approach in which task variables interact via low-dimensional recurrent connectivity to produce behavioral output. We apply the latent circuit inference to recurrent neural networks trained to perform a context-dependent decision-making task and find a suppression mechanism in which contextual representations inhibit irrelevant sensory responses. We validate this mechanism by confirming the behavioral effects of patterned connectivity perturbations predicted by the latent circuit model. We find similar suppression of irrelevant sensory responses in the prefrontal cortex of monkeys performing the same task. We show that incorporating causal interactions among task variables is critical for identifying behaviorally relevant computations from neural response data.
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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.