Biologically plausible neural models can account for binocular disparity
Multiple computational and neuroscience studies demonstrate that biologically plausible neural network models can effectively account for binocular disparity and depth perception.
The retrieved papers provide robust theoretical and empirical support indicating that biologically inspired neural models, predictive coding frameworks, and cortical simulations can successfully account for binocular disparity and 3D depth estimation. There are no papers refuting this claim.
Priorelli M, Pezzulo G, Stoianov IP. Active Vision in Binocular Depth Estimation: A Top-Down Perspective.. 2023. https://doi.org/10.3390/biomimetics8050445
The paper demonstrates that depth estimation from binocular disparity can be implemented via biologically plausible hierarchical generative models and predictive coding.
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Chen T, Cheng X, Tsao T. Understanding and simulating border ownership centered segmentation.. 2025. https://doi.org/10.1038/s41598-025-18843-9
The study integrates binocular disparity representation within a comprehensive neural model of border ownership and figure-ground organization.
Wundari BG, Fujita I, Ban H. Human and artificial visual systems share a computational principle for transforming binocular disparity into depth representation.. 2025. https://doi.org/10.1038/s42003-025-08474-1
Research shows that both human visual cortices and artificial deep neural networks share computational principles for transforming binocular disparity into depth representations.
Kalou K, Sedda G, Gibaldi A, Sabatini SP. Learning bio-inspired head-centric representations of 3D shapes in an active fixation setting.. 2022. https://doi.org/10.3389/frobt.2022.994284
A recurrent neural network modeling population responses of cortical V1 cells successfully interprets 3D scene depth from binocular disparity.
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