Topic: reduced Wong-Wang model

Background: Biophysical network modeling (BNM) of the brain is a promising technique for bridging macro- and microscale levels of investigation, enabling inferences about latent features of brain activity such as excitation-inhibition balance. This approach allows personalized models of the brain to be fitted to individual subjects’ imaging data through parameter optimization [1, 2]. However, the process typically requires several thousand simulations per subject, making it computationally expensive and limiting its scalability to larger subject pools and more complex models. To address this,

Neuronal morphology plays a fundamental role in determining the neuronal network's function. However, understanding the effect of single neuron morphology on whole-brain function is still an open challenge. Limited by resolution and time cost, current studies on the functional properties of whole-brain connectivity are mainly described with simulations from the mesoscale perspective, like those generated in The Virtual Mouse Brain (TVMB) platform. Whole mouse brain activity is simulated using connectivity weights between brain regions obtained from tracer injections in populations of neurons a