ReviewCurrent opinion in neurobiology2020
Modeling statistical dependencies in multi-region spike train data.
Review in Current opinion in neurobiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.Nature communications · 2026Article
- Sensory and palatability coding of taste stimuli in cortex involves dynamic and asymmetric cortico-amygdalar interactions.Journal of neurophysiology · 2026Article
- A Population Coupling Model Identifies Reduced Propagation from V1 to Higher Visual Areas During Locomotion.bioRxiv : the preprint server for biology · 2026Article
- Constraining inference of across-region interactions using neural activity perturbations.bioRxiv : the preprint server for biology · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.ArXiv · 2025Article
- Optimal Transport based Cross-Domain Integration for Heterogeneous Data.Journal of the American Statistical Association · 2025Article
- Global and local origins of trial-to-trial spike count variability in visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Fast Multigroup Gaussian Process Factor Models.Neural computation · 2025Article
- Accurate Identification of Communication Between Multiple Interacting Neural Populations.Proceedings of machine learning research · 2025Article
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.bioRxiv : the preprint server for biology · 2025Article
- A Bayesian mixture model for Poisson network autoregression.Social network analysis and mining · 2025Article
- Identifying interactions across brain areas while accounting for individual-neuron dynamics with a Transformer-based variational autoencoder.Advances in neural information processing systems · 2025Article
- Stimulus-invariant aspects of the retinal code drive discriminability of natural scenes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Article
- Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.ArXiv · 2024Article
- Thalamocortical interactions shape hierarchical neural variability during stimulus perception.iScience · 2024Article
- A multi-demand operating system underlying diverse cognitive tasks.Nature communications · 2024Article
- Time-varying generalized linear models: characterizing and decoding neuronal dynamics in higher visual areas.Frontiers in computational neuroscience · 2024Review
- Towards a "universal translator" for neural dynamics at single-cell, single-spike resolution.Advances in neural information processing systems · 2024Article
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Neural computations underlying cognition and behavior rely on the coordination of neural activity across multiple brain areas. Understanding how brain areas interact to process information or generate behavior is thus a central question in neuroscience. Here we provide an overview of statistical approaches for characterizing statistical dependencies in multi-region spike train recordings. We focus on two classes of models in particular: regression-based models and shared latent variable models. Regression-based models describe interactions in terms of a directed transformation of information from one region to another. Shared latent variable models, on the other hand, seek to describe interactions in terms of sources that capture common fluctuations in spiking activity across regions. We discuss the advantages and limitations of each of these approaches and future directions for the field. We intend this review to be an introduction to the statistical methods in multi-region models for computational neuroscientists and experimentalists alike.
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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.