ArticleScience advances2024
Between-area communication through the lens of within-area neuronal 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.
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Controlling Spatio-Temporal Sequences of Neural Activity by Local Synaptic Changes.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Input-dependent directionality of interactions between cortical areas.bioRxiv : the preprint server for biology · 2026Article
- Decomposing the modulation of interactions between neuronal populations.bioRxiv : the preprint server for biology · 2026Article
- Prioritized learning of cross-population neural dynamics.Journal of neural engineering · 2025Article
- Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace.Neural computation · 2025Article
- Article
- Circuit-Based Understanding of Fine Spatial Scale Clustering of Orientation Tuning in Mouse Visual Cortex.bioRxiv : the preprint server for biology · 2025Article
- Measuring Stimulus Information Transfer Between Neural Populations through the Communication Subspace.bioRxiv : the preprint server for biology · 2024Article
- Early selection of task-relevant features through population gating.Nature communications · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
A core problem in systems and circuits neuroscience is deciphering the origin of shared dynamics in neuronal activity: Do they emerge through local network interactions, or are they inherited from external sources? We explore this question with large-scale networks of spatially ordered spiking neuron models where a downstream network receives input from an upstream sender network. We show that linear measures of the communication between the sender and receiver networks can discriminate between emergent or inherited population dynamics. A match in the dimensionality of the sender and receiver population activities promotes faithful communication. In contrast, a nonlinear mapping between the sender to receiver activity, for example, through downstream emergent population-wide fluctuations, can impair linear communication. Our work exposes the benefits and limitations of linear measures when analyzing between-area communication in circuits with rich population-wide neuronal dynamics.
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