The brains of all bilaterally symmetric animals on Earth are divided into
left and right hemispheres. The anatomy and functionality of the hemispheres
have a large degree of overlap, but there are asymmetries and they specialize
to possess different attributes. Several studies have used computational models
to mimic hemispheric asymmetries with a focus on reproducing human data on
semantic and visual processing tasks. In this study, we aimed to understand how
dual hemispheres could interact in a given task. We propose a bilateral
artificial neural network that imitates a lateralization observed in nature:
that the left hemisphere specializes in specificity and the right in
generalities. We used two ResNet-9 convolutional neural networks with different
training objectives and tested it on an image classification task. Our analysis
found that the hemispheres represent complementary features that are exploited
by a network head which implements a type of weighted attention. The bilateral
architecture outperformed a range of baselines of similar representational
capacity that don't exploit differential specialization. The results
demonstrate the efficacy of bilateralism, contribute to an understanding of
bilateralism in biological brains and the architecture serves as an inductive
bias when designing new AI systems.

该研究提出了一种双侧神经网络结构，模仿大脑左右脑半球的功能区别，并通过对图像分类任务的测试表明，这种结构比其他容量类似的基线模型更具有效性，可以实现对图像特征的加权注意力。