Isbister, James BrydenEcker, AndrásPokorny, ChristophBolaños Puchet, SirioEgas Santander, DanielaArnaudon, AlexisAwile, OmarBarros Zulaica, NataliBlanco Alonso, JorgeBoci, ElvisChindemi, GiuseppeCourcol, Jean-DenisDamart, Tanguy Pierre LouisDelemontex, ThomasDietz, AlexanderFicarelli, GianlucaGevaert, MichaelHerttuainen, JoniIvaska, GenrichJi, WeinaKeller, DanielKing, James GonzaloKumbhar, PramodLapere, SamuelLitvak, PolinaMandge, DarshanMuller, Eilif BenjaminPereira, FernandoPlanas Carbonell, JuditRanjan, RajnishReva, MariaRomani, ArmandoRössert, Christian AndreasSchürmann, FelixSood, VishalTeska, Aleksandra ZuzannaTuncel, AnilVan Geit, WernerWolf, MatthiasMarkram, HenryRamaswamy, SrikanthReimann, Michael2023-09-222023-09-222023-09-22202310.1101/2023.05.17.541168https://infoscience.epfl.ch/handle/20.500.14299/200924In recent years, large-scale computational models of the cortex have emerged as a powerful way to study the multi-scale mechanisms of neural processing. However, due to computational costs and difficulty of parameterization, detailed biophysical reconstructions have so far been restricted to small volumes of tissue, where the study of macro- and meso-scale interactions that are central to cortical function is not possible. We describe here, and in a companion paper, an approach to address the scaling challenges and provide a model of multiple interacting cortical regions at a subcellular level of detail. The model consists of 4.2 million morphologically detailed neurons in 8 sub-regions and connected with 13.2 billion synapses through local and long-range connectivity. Its anatomical aspects are described in the companion paper; here, we introduce physiological models of neuronal activity and synaptic transmission that integrate a large number of literature sources and were built using previously published algorithms. Biological neuronal diversity was captured in 208 morpho-electrical neuron types, five types of synaptic short-term dynamics, and pathway-specificity of synaptic parameters. A representation of synaptic input from cortical regions not present in the model was added and efficiently calibrated to reference firing rates. The model exhibits a spectrum of dynamical states differing in the degree to which they are internally versus externally driven. We characterized which parts of the spectrum are compatible with available experimental data on layer-specific delays and amplitudes of responses to simple stimuli, and found an in vivo-like regime at the edge of a transition from asynchronous to synchronous spontaneous activity. We developed a rich set of simulation tools to recreate a diverse set of laboratory experiments in silico, providing further validation and demonstrating the utility of the model in a variety of paradigms. Finally, we found that the large spatial scale of the model, that incorporates multiple cortical regions, led to the emergence of multiple independent computational units interacting through long-range synaptic pathways. The model provides a framework for the continued integration of experimental findings, for challenging hypotheses and making testable predictions, and provides a foundation for further simulation-based studies of cortical processing and learning.text::journal::journal article::research article