Topic: asynchronous state

Mean-field models of neuronal populations in the brain have proven extremely useful to understand network dynamics and response to stimuli, but these models generally lack a faithful description of the fluctuations in the biologically relevant case of finite network size and connection probabilities $p<1$ (non-full connectivity). To gain insight into the mechanisms underlying the response and variability of the dynamics of populations of spiking neurons, we derive here a nonlinear stochastic mean-field model for a network of spiking Poisson neurons with quenched random connectivity. We treat

Understanding the intricate functionality of the cerebral cortex requires integrating local circuit properties with global brain dynamics. Our study draws insights from biophysically detailed models of spiking neurons containing specific neural mechanisms, such as M-current and adaptation currents (Dalla Porta et al. Plos Comp. Bio., 2023). We incorporated these features in a mean-field model and, using human tractography data, we constructed a virtual whole-brain model consisting of 68 cortical nodes (Goldman et al. Front. Comput. Neurosci., 2023). To enhance realism, we introduced heterogene