pAE：一种通过整合人类视觉系统中的前馈和反馈流来建模外侧膝状核的高效自编码器架构
pAE: An Efficient Autoencoder Architecture for Modeling the Lateral Geniculate Nucleus by Integrating Feedforward and Feedback Streams in Human Visual System
AI总结：
提出一种基于剪枝自编码器（pAE）的深度卷积模型，整合前馈与反馈流来模拟外侧膝状核（LGN）功能，在自然图像数据集上达到99.26%预测性能，比人类结果提升约28%。
摘要：
视觉皮层是大脑的重要组成部分，负责对物体进行层级识别。理解外侧膝状核（LGN）作为视觉皮层的前置区域，在自下而上和自上而下两条通路中处理视觉信息时的作用至关重要。当视觉刺激到达视网膜时，它们会被传递到LGN区域进行初步处理，然后再被发送到视觉皮层进行进一步处理。在本研究中，我们引入了一个深度卷积模型，该模型紧密模拟人类视觉信息处理过程。我们旨在使用一个经过训练的浅层卷积模型来近似LGN区域的功能，该模型基于一种剪枝自编码器（pAE）架构设计。pAE模型尝试将来自/通往V1区域的前馈和反馈流整合到该问题中。该建模框架涵盖了视觉刺激数据集的时序和非时序数据输入模式，该数据集包含由固定相机在连续帧中拍摄的自然图像，具有两个类别：包含动物（在运动）的图像和不包含动物的图像。随后，我们将所提出的深度调优模型的结果与采用Gabor和双正交小波函数的小波滤波器组方法进行了比较。我们的实验表明，基于深度调优模型的所提方法不仅获得了与人类基准高度相似的结果，而且其性能显著优于其他模型。pAE模型最终达到了99.26%的预测性能，并且在时序模式下相较于人类结果实现了约28%的显著提升。
英文摘要：
The visual cortex is a vital part of the brain, responsible for hierarchically identifying objects. Understanding the role of the lateral geniculate nucleus (LGN) as a prior region of the visual cortex is crucial when processing visual information in both bottom-up and top-down pathways. When visual stimuli reach the retina, they are transmitted to the LGN area for initial processing before being sent to the visual cortex for further processing. In this study, we introduce a deep convolutional model that closely approximates human visual information processing. We aim to approximate the function for the LGN area using a trained shallow convolutional model which is designed based on a pruned autoencoder (pAE) architecture. The pAE model attempts to integrate feed forward and feedback streams from/to the V1 area into the problem. This modeling framework encompasses both temporal and non-temporal data feeding modes of the visual stimuli dataset containing natural images captured by a fixed camera in consecutive frames, featuring two categories: images with animals (in motion), and images without animals. Subsequently, we compare the results of our proposed deep-tuned model with wavelet filter bank methods employing Gabor and biorthogonal wavelet functions. Our experiments reveal that the proposed method based on the deep-tuned model not only achieves results with high similarity in comparison with human benchmarks but also performs significantly better than other models. The pAE model achieves the final 99.26% prediction performance and demonstrates a notable improvement of around 28% over human results in the temporal mode.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
