Laminar RNNs: using biologically-inspired network topology on the cortical laminar level in memory tasks

Abstract

Advancements in neuroscience and artificial intelligence have been fueling one another for decades. In this study, we integrate a neuroimaging model of laminar-level connectomics into a biologically-inspired deep learning model of recurrent neural networks (RNNs) for working memory tasks. The resulting model offers a way to incorporate a more comprehensive representation of brain topology into artificial intelligence without diminishing the performance of the network compared to previous models.