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.
翻译:横向连接对视觉皮层的感官处理具有重要作用,它支持即使是对高度相似的特征也可以进行不相容的神经反应。 在目前的工作中,我们表明,沿过滤器域沿过滤器域建立具有生物灵感的墨西哥帽子横向连接剖面可大大提高各种轻量神经神经神经网络的分类准确性,而无需增加可训练的网络参数。此外,我们证明,可以分析确定调制过滤器激活的固定分布,从而避免在模拟时间动态时再次使用。我们进一步显示,墨西哥帽子连接剖面具有在将早期视觉皮层的特征选择性地形组织组合在一起的顺序中订购过滤器的效果。在有顺序的过滤器序列中,这个剖面可以使过滤器的调整曲线更精细。