Predicting distributed working memory activity in a large-scale mouse brain: the importance of the cell type-specific connectome

Abstract Recent advances in connectome and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the mouse multiregional brain for a cardinal cognitive function called working memory, the brain’s ability to internally hold and process information without sensory input. The model is built on mesoscopic connectome data for inter-areal cortical connections and endowed with a macroscopic gradient of measured parvalbumin-expressing interneuron density. We found that working memory coding is distributed yet exhibits modularity; the spatial pattern of mnemonic representation is determined by long-range cell type-specific targeting and density of cell classes. Cell type-specific graph measures predict the activity patterns and a core subnetwork for memory maintenance. The model shows numerous self-sustained internal states (each engaging a distinct subset of areas). This work provides a framework to interpret large-scale recordings of brain activity during cognition, while highlighting the need for cell type-specific connectomics.