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 computation models 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 in a bilateral architecture interact to perform well in a given task. We propose a bilateral artificial neural network that imitates lateralization observed in nature: that the left hemisphere specializes in specificity and the right in generalities. We used different training objectives to achieve the desired specialization and tested it on an image classification task with two different CNN backbones -- ResNet and VGG. 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, with the exception of a conventional ensemble of unilateral networks trained on a dual training objective for specifics and generalities. The results demonstrate the efficacy of bilateralism, contribute to the discussion of bilateralism in biological brains and the principle may serves as an inductive bias for new AI systems.

翻译：地球上所有双侧对称动物的脑分为左右半球。两半球的解剖结构和功能具有高度重叠性，但存在不对称性，并各自特化出不同属性。已有若干计算模型模仿半球不对称性，重点关注人类语义和视觉处理任务的数据复现。本研究旨在探索双侧架构中双半球如何相互作用以在既定任务中表现优异。我们提出一种模仿自然界侧化现象的双侧人工神经网络：左半球专攻特殊性，右半球专攻一般性。通过设定不同训练目标实现期望的特化，并在采用两种不同CNN主干（ResNet和VGG）的图像分类任务上进行测试。分析发现，两半球表征具有互补特征，这些特征被实现加权注意力机制的网络头部所利用。除使用双重训练目标（特殊性与一般性）的传统单侧网络集成模型外，该双侧架构在相似表征能力范围内优于未利用差异性特化的各类基线模型。研究结果证明了双侧化的有效性，为生物脑双侧化理论提供了新论据，该原理亦可作为新型人工智能系统的归纳偏置。