ArticleFrontiers in neuroscience2020
Contrast-Dependence of Temporal Frequency Tuning in Mouse V1.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Functionally distinct GABAergic amacrine cell types regulate spatiotemporal encoding in the mouse retina.Nature neuroscience · 2025Article
- Chilean brush-tailed mouse (Journal of neurophysiology · 2025Article
- Activation and depression of neural and hemodynamic responses induced by the intracortical microstimulation and visual stimulation in the mouse visual cortex.Journal of neural engineering · 2024Article
- Mammals Achieve Common Neural Coverage of Visual Scenes Using Distinct Sampling Behaviors.eNeuro · 2024Article
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Authors and funding
3 authors.
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Abstract
The perception of speed is influenced by visual contrast. In primary visual cortex (V1), an early stage in the visual perception pathway, the neural tuning to speed is directly related to the neural tuning to temporal frequency of stimulus changes. The influence of contrast on speed perception can be caused by the joint dependency of neural responses in V1 on temporal frequency and contrast. Here, we investigated how tuning to contrast and temporal frequency in V1 of anesthetized mice are related. We found that temporal frequency tuning is contrast-dependent. V1 was more responsive at lower temporal frequencies than the dLGN, consistent with previous work at high contrast. The temporal frequency tuning moves toward higher temporal frequencies with increasing contrast. The low half-maximum temporal frequency does not change with contrast. The Heeger divisive normalization equation provides a good fit to many response characteristics in V1, but does not fit the dependency of temporal frequency and contrast with set of parameters for all temporal frequencies. Different mechanisms for normalization in the visual cortex may predict different relationships between temporal frequency and contrast non-linearity. Our data could help to make a model selection.
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