VCNet: Recreating High-Level Visual Cortex Principles for Robust Artificial Vision

• URL: http://arxiv.org/abs/2508.02995v1
• Date: Tue, 05 Aug 2025 01:52:42 GMT
• Title: VCNet: Recreating High-Level Visual Cortex Principles for Robust Artificial Vision
• Authors: Brennen A. Hill, Zhang Xinyu, Timothy Putra Prasetio,
• Abstract summary: We introduce Visual Cortex Network (VCNet), a novel neural network architecture based on the macro-scale organization of the primate visual cortex.<n> VCNet emulates key biological mechanisms, including hierarchical processing across distinct cortical areas, dual-stream information segregation, and top-down predictive feedback.<n>Our results show that VCNet achieves a classification accuracy of 92.1% on Spots-10 and 74.4% on the light field dataset, surpassing contemporary models of comparable size.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their success in image classification, modern convolutional neural networks (CNNs) exhibit fundamental limitations, including data inefficiency, poor out-of-distribution generalization, and vulnerability to adversarial perturbations. The primate visual system, in contrast, demonstrates superior efficiency and robustness, suggesting that its architectural principles may offer a blueprint for more capable artificial vision systems. This paper introduces Visual Cortex Network (VCNet), a novel neural network architecture whose design is informed by the macro-scale organization of the primate visual cortex. VCNet emulates key biological mechanisms, including hierarchical processing across distinct cortical areas, dual-stream information segregation, and top-down predictive feedback. We evaluate VCNet on two specialized benchmarks: the Spots-10 animal pattern dataset and a light field image classification task. Our results show that VCNet achieves a classification accuracy of 92.1\% on Spots-10 and 74.4\% on the light field dataset, surpassing contemporary models of comparable size. This work demonstrates that integrating neuroscientific principles into network design can lead to more efficient and robust models, providing a promising direction for addressing long-standing challenges in machine learning.

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