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 specialise in possesses different attributes. Other authors have used computational models to mimic hemispheric asymmetries with a focus on reproducing human data on semantic and visual processing tasks. We took a different approach and 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 lateralisation observed in nature: that the left hemisphere specialises in local features and the right in global features. We used different training objectives to achieve the desired specialisation 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 that implements a type of weighted attention. The bilateral architecture outperformed a range of baselines of similar representational capacity that do not exploit differential specialisation, with the exception of a conventional ensemble of unilateral networks trained on dual training objectives for local and global features. The results demonstrate the efficacy of bilateralism, contribute to the discussion of bilateralism in biological brains, and the principle may serve as an inductive bias for new AI systems.

翻译：地球上所有两侧对称动物的大脑都分为左右两个半球。这些半球在解剖结构和功能上存在很大程度的重叠，但也存在不对称性，它们各自特化并拥有不同的属性。其他研究者曾使用计算模型来模拟半球不对称性，重点在于复现人类在语义和视觉处理任务上的数据。我们采取了不同的方法，旨在理解双侧架构中的两个半球如何通过交互在给定任务中表现出色。我们提出了一种模仿自然界中观察到的侧化现象的双侧人工神经网络：左半球特化于局部特征，右半球特化于全局特征。我们使用不同的训练目标来实现所需的特化，并在图像分类任务上使用两种不同的CNN骨干网络（ResNet和VGG）进行了测试。我们的分析发现，两个半球表征了互补的特征，这些特征被一个实现加权注意力机制的网络头部所利用。除了在局部和全局特征上使用双重训练目标训练的传统单侧网络集成模型外，这种双侧架构在表现上优于一系列具有相似表征能力但未利用差异化特化的基线模型。这些结果证明了双侧架构的有效性，为生物大脑中双侧性的讨论提供了参考，并且该原理可能作为新人工智能系统的归纳偏置。