Deep learning in a bilateral brain with hemispheric specialization

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.