ArticleJournal of vision2020
Contrast response in a comprehensive network model of macaque V1.
Article in Journal of vision, 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.
- A comprehensive data-driven model of cat primary visual cortex.PLoS computational biology · 2024Article
- Efficient models of cortical activity via local dynamic equilibria and coarse-grained interactions.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Varying test-pattern duration to explore the dynamics of contrast-comparison and contrast-normalization processes.Journal of vision · 2023Article
- A Computational Model of Direction Selectivity in Macaque V1 Cortex Based on Dynamic Differences between On and Off Pathways.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2022Article
- A data-informed mean-field approach to mapping of cortical parameter landscapes.PLoS computational biology · 2021Article
- Malleability of gamma rhythms enhances population-level correlations.Journal of computational neuroscience · 2021Article
- Unraveling the mechanisms of surround suppression in early visual processing.PLoS computational biology · 2021Article
- DNN-assisted statistical analysis of a model of local cortical circuits.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The response to contrast is one of the most important functions of the macaque primary visual cortex, V1, but up to now there has not been an adequate theory for it. To fill this gap in our understanding of cortical function, we built and analyzed a new large-scale, biologically constrained model of the input layer, 4Cα, of macaque V1. We called the new model CSY2. We challenged CSY2 with a three-parameter family of visual stimuli that varied in contrast, orientation, and spatial frequency. CSY2 accurately simulated experimental data and made many new predictions. It accounted for 1) the shapes of firing-rate-versus-contrast functions, 2) orientation and spatial frequency tuning versus contrast, and 3) the approximate contrast-invariance of cortical activity maps. Post-analysis revealed that the mechanisms that were needed to produce the successful simulations of contrast response included strong recurrent excitation and inhibition that find dynamic equilibria across the cortical surface, dynamic feedback between L6 and L4, and synaptic dynamics like inhibitory synaptic depression.
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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.