ArticlePLoS computational biology2020
Recurrent interactions can explain the variance in single trial responses.
Article in PLoS computational biology, 2020. 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.
- Nonuniform spike count shared variability in the mouse early visual system.Journal of neurophysiology · 2026Article
- A dynamic subset of network interactions underlies tuning to natural movements in marmoset sensorimotor cortex.Nature communications · 2024Article
- Trial-by-trial variability in cortical responses exhibits scaling of spatial correlations predicted from critical dynamics.Cell reports · 2024Article
- A dynamic subset of network interactions underlies tuning to natural movements in marmoset sensorimotor cortex.Research square · 2023Article
- Propagation of activity through the cortical hierarchy and perception are determined by neural variability.Nature neuroscience · 2023Article
- Coupled Dynamics of Stimulus-Evoked Gustatory Cortical and Basolateral Amygdalar Activity.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Dynamic structure of motor cortical neuron coactivity carries behaviorally relevant information.Network neuroscience (Cambridge, Mass.) · 2023Article
- Network Analysis of Murine Cortical Dynamics Implicates Untuned Neurons in Visual Stimulus Coding.Cell reports · 2020Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
To develop a complete description of sensory encoding, it is necessary to account for trial-to-trial variability in cortical neurons. Using a linear model with terms corresponding to the visual stimulus, mouse running speed, and experimentally measured neuronal correlations, we modeled short term dynamics of L2/3 murine visual cortical neurons to evaluate the relative importance of each factor to neuronal variability within single trials. We find single trial predictions improve most when conditioning on the experimentally measured local correlations in comparison to predictions based on the stimulus or running speed. Specifically, accurate predictions are driven by positively co-varying and synchronously active functional groups of neurons. Including functional groups in the model enhances decoding accuracy of sensory information compared to a model that assumes neuronal independence. Functional groups, in encoding and decoding frameworks, provide an operational definition of Hebbian assemblies in which local correlations largely explain neuronal responses on individual trials.
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Registered trials
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