Center for Neural Science, New York University, New York, United States; Bristol Computational Neuroscience Unit, School of Engineering Mathematics and Technology, University of Bristol, Bristol, United Kingdom
Center for Neural Science, New York University, New York, United States; Campus Institute for Dynamics of Biological Networks, University of Göttingen, Göttingen, Germany
Center for Neural Science, New York University, New York, United States; The Key Laboratory of Biomedical Information Engineering of Ministry of Education,Institute of Health and Rehabilitation Science,School of Life Science and Technology, Research Center for Brain-inspired Intelligence, Xi’an Jiaotong University, Xi'an, China
Recent advances in connectomics and neurophysiology make it possible to probe whole-brain mechanisms of cognition and behavior. We developed a large-scale model of the multiregional mouse 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 interareal 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 attractor states, which are 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.
