The brains of all bilaterally symmetric animals on Earth are are divided into
left and right hemispheres. The anatomy and functionality of the hemispheres
have a large degree of overlap, but they specialize to possess different
attributes. The left hemisphere is believed to specialize in specificity and
routine, the right in generalities and novelty. In this study, we propose an
artificial neural network that imitates that bilateral architecture using two
convolutional neural networks with different training objectives and test it on
an image classification task. The bilateral architecture outperforms
architectures of similar representational capacity that don't exploit
differential specialization. It demonstrates the efficacy of bilateralism and
constitutes a new principle that could be incorporated into other computational
neuroscientific models and used as an inductive bias when designing new ML
systems. An analysis of the model can help us to understand the human brain.Comment: 11 pages, 10 figure