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.
Competing Interest Statement
The authors have declared no competing interest.
Copyright
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