ArticleScience advances2024
Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Reciprocal connections dynamically build consensus between neocortical areas.Nature neuroscience · 2026Article
- Linearizing and Forecasting: A Reservoir Computing Route to Digital Twins of the Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A neural mechanism for online discovery of latent contexts.bioRxiv : the preprint server for biology · 2026Article
- Stiefel Manifold Dynamical Systems for Tracking Representational Drift.bioRxiv : the preprint server for biology · 2026Article
- Article
- Convergent neural dynamical systems for task control in artificial networks and human brains.bioRxiv : the preprint server for biology · 2025Article
- Article
- Modeling Latent Neural Dynamics with Gaussian Process Switching Linear Dynamical Systems.ArXiv · 2025Article
- Limitation of switching sensory information flow in flexible perceptual decision making.Nature communications · 2025Article
- When and why does motor preparation arise in recurrent neural network models of motor control?eLife · 2024Article
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Authors and funding
3 authors.
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Abstract
The complex neural activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured PFC responses within contexts and revealed equally performing mechanisms. One model implemented context-dependent recurrent dynamics and relied on transient input amplification; the other relied on subtle contextual modulations of the inputs, providing constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both models revealed properties of inputs and recurrent dynamics that were not apparent from qualitative descriptions of PFC responses. By revealing mechanisms that are quantitatively consistent with complex cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation.
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