ArticleeLife2021
Modelling the neural code in large populations of correlated neurons.
Article in eLife, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Neural Response Reliability as a Marker of the Transition of Neural Codes along Auditory Pathways.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Continuous partitioning of neuronal variability.bioRxiv : the preprint server for biology · 2025Article
- The geometry of correlated variability leads to highly suboptimal discriminative sensory coding.Journal of neurophysiology · 2025Article
- Signal switching may enhance processing power of the brain.Trends in cognitive sciences · 2024Review
- Calibrating Bayesian Decoders of Neural Spiking Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Efficient Decoding of Large-Scale Neural Population Responses With Gaussian-Process Multiclass Regression.Neural computation · 2024Article
- Modeling the diverse effects of divisive normalization on noise correlations.PLoS computational biology · 2023Article
- A large-scale fMRI dataset for the visual processing of naturalistic scenes.Scientific data · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations. In this paper, we propose a class of response model based on mixture models and exponential families. We show how to fit our models with expectation-maximization, and that they capture diverse variability and covariability in recordings of macaque primary visual cortex. We also show how they facilitate accurate Bayesian decoding, provide a closed-form expression for the Fisher information, and are compatible with theories of probabilistic population coding. Our framework could allow researchers to quantitatively validate the predictions of neural coding theories against both large-scale neural recordings and cognitive performance.
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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.