生物启发的卷积神经网络语义横向连通性
Biologically Inspired Semantic Lateral Connectivity for Convolutional Neural Networks
摘要：
横向连接在视觉皮层的感觉处理中起着重要作用，即使是对高度相似的特征也能支持可辨别的神经元反应。在目前的工作中，我们表明，建立一个生物启发墨西哥帽横向连接配置文件沿过滤器域可以显着提高分类精度的各种轻量级卷积神经网络，而无需添加可训练的网络参数。此外，我们证明，它是可能的分析确定调制滤波器激活的平稳分布，从而避免使用递归建模时间动态。我们还发现，墨西哥帽连接配置文件的效果排序过滤器在一个序列类似的地形组织功能选择性在早期视觉皮层。在有序滤波器序列中，该配置文件然后锐化滤波器的调谐曲线。
英文摘要：
Lateral connections play an important role for sensory processing in visual cortex by supporting discriminable neuronal responses even to highly similar features. In the present work, we show that establishing a biologically inspired Mexican hat lateral connectivity profile along the filter domain can significantly improve the classification accuracy of a variety of lightweight convolutional neural networks without the addition of trainable network parameters. Moreover, we demonstrate that it is possible to analytically determine the stationary distribution of modulated filter activations and thereby avoid using recurrence for modeling temporal dynamics. We furthermore reveal that the Mexican hat connectivity profile has the effect of ordering filters in a sequence resembling the topographic organization of feature selectivity in early visual cortex. In an ordered filter sequence, this profile then sharpens the filters' tuning curves.
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