ArticlePLoS computational biology2021
Dynamic Electrode-to-Image (DETI) mapping reveals the human brain's spatiotemporal code of visual information.
Article in PLoS computational biology, 2021. 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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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.
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4 citing papers in PubMed.
- Linking retinal sampling in neural encoding models to temporal profiles of visual processing in humans.PLoS computational biology · 2026Article
- Brain-guided convolutional neural networks reveal task-specific representations in scene processing.Scientific reports · 2025Article
- Fixation-related potentials during a virtual navigation task: The influence of image statistics on early cortical processing.Attention, perception & psychophysics · 2025Article
- Expectation Modifies the Representational Fidelity of Complex Visual Objects.Imaging neuroscience (Cambridge, Mass.) · 2024Article
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Authors and funding
3 authors.
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Abstract
A number of neuroimaging techniques have been employed to understand how visual information is transformed along the visual pathway. Although each technique has spatial and temporal limitations, they can each provide important insights into the visual code. While the BOLD signal of fMRI can be quite informative, the visual code is not static and this can be obscured by fMRI's poor temporal resolution. In this study, we leveraged the high temporal resolution of EEG to develop an encoding technique based on the distribution of responses generated by a population of real-world scenes. This approach maps neural signals to each pixel within a given image and reveals location-specific transformations of the visual code, providing a spatiotemporal signature for the image at each electrode. Our analyses of the mapping results revealed that scenes undergo a series of nonuniform transformations that prioritize different spatial frequencies at different regions of scenes over time. This mapping technique offers a potential avenue for future studies to explore how dynamic feedforward and recurrent processes inform and refine high-level representations of our visual world.
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