Retina gap junctions support the robust perception by warping neural representational geometries along the visual hierarchy
Deep Neural Networks (DNNs) are vulnerable to elaborately designed adversarial noise, although they have achieved extraordinary success in many tasks. Compared with DNNs, the human visual system is highly robust. However, it is unclear how the human visual system defends against adversarial attacks, especially the role of the early visual system and its influence on the brain manifold. Due to retina gap junctions being crucial for the denoising function in the early visual system, we combine a retina gap junction-based filter, G-filter, with DNN as an abstract human visual system model called the biological hybrid model. We adopt this model to study the defense performance of retina gap j...
| Authors | Yang Yue, Shenjian Zhang, Yonghong Tian et al. |
|---|---|
| Primary category | q-bio.NC |
| Published | 3 April 2026 |
| Last updated at source | 3 April 2026 |
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