ArticleNeuron2026
Inferring brain-wide interactions using data-constrained recurrent neural network models.
Article in Neuron, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
49 citing papers in PubMed.
- Article
- Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses.bioRxiv : the preprint server for biology · 2026Article
- Data-derived agents reveal dynamical reservoirs in mouse cortex for adaptive behavior.bioRxiv : the preprint server for biology · 2026Article
- Article
- InputDSA : Demixing then comparing recurrent and externally driven dynamics.... International Conference on Learning Representations · 2026Article
- Constructing biologically constrained RNNs via Dale's backpropagation and topologically informed pruning.Science advances · 2025Article
- Constraining inference of across-region interactions using neural activity perturbations.bioRxiv : the preprint server for biology · 2025Article
- Recurrent neural network models reveal unified mechanisms generating event-related potentials from MMN to P300.bioRxiv : the preprint server for biology · 2025Article
- Article
- Inpainting the Neural Picture: Inferring Unrecorded Brain Area Dynamics from Multi-Animal Datasets.ArXiv · 2025Article
- Linear and categorical coding units in the mouse gustatory cortex drive population dynamics and behavior in taste decision-making.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.ArXiv · 2025Article
- Bridging the gap between the connectome and whole-brain activity inbioRxiv : the preprint server for biology · 2025Article
- Context rescales a social action code in a hormone-sensitive network.bioRxiv : the preprint server for biology · 2025Article
- Measuring Stimulus Information Transfer Between Neural Populations Through the Communication Subspace.Neural computation · 2025Article
- Exploring the Architectural Biases of the Cortical Microcircuit.Neural computation · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.Proceedings of machine learning research · 2025Article
- Unraveling the neural code: analysis of large-scale two-photon microscopy data.Microscopy (Oxford, England) · 2025Review
- Article
- A Multi-Region Brain Model to Elucidate the Role of Hippocampus in Spatially Embedded Decision-Making.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors.
Funding
Abstract
Behavior arises from the coordinated activity across anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates robust, scalable computational models to extract meaningful features of inter-region communication and principled theories to interpret those features. Here, we introduce current-based decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that autonomously produce dynamics consistent with experimentally obtained neural data. CURBD leverages the functional interactions inferred from such models to reveal directional currents between multiple brain regions simultaneously. We first show that CURBD accurately isolates inter-region currents in simulated, ground-truth networks with known connectivity and dynamics. We then apply CURBD to multi-region neural recordings obtained from many species-larval zebrafish, mice, macaques, and humans-to demonstrate the widespread applicability of CURBD in untangling brain-wide interactions and inter-area communication principles underlying behavior.
Indexed as
Identifiers
What OpenQuestion holds
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.