ArticleNeuroinformatics2018
Neuronal Activities in the Mouse Visual Cortex Predict Patterns of Sensory Stimuli.
Article in Neuroinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- Higher order visual areas enhance stimulus responsiveness in mouse primary visual cortex.Cerebral cortex (New York, N.Y. : 1991) · 2022Article
- Mouse Higher Visual Areas Provide Both Distributed and Specialized Contributions to Visually Guided Behaviors.Current biology : CB · 2020Article
- A large-scale standardized physiological survey reveals functional organization of the mouse visual cortex.Nature neuroscience · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Visual cortex forms the basis of visual processing and plays important roles in visual encoding. By using the recently published Allen Brain Observatory dataset consisting of large-scale calcium imaging of mouse V1 activities under visual stimuli, we were able to obtain high-quality data capturing simultaneous neuronal activities at multiple sub-areas and cortical depths of V1. Using prediction models, we analyzed the activity profiles related to static and drifting grating stimuli. We conducted a comprehensive survey of the coding ability of multiple cortical locations toward different stimulus attributes. Specifically, we focused on orientations and spatial frequencies (for static stimuli), as well as moving directions and speed (for drifting stimuli). By using results produced from a prediction model, we quantified the decoding performance profile at different sub-areas and layers of V1. In addition, we analyzed the interactions and interference between different stimulus attributes. The insights obtained from these discoveries would contribute to more precise and quantitative understanding of V1 coding mechanisms.
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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.