Caglar Cakan, Nikola Jajcay, Klaus Obermayer

neurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e. the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows one to characterize the model’s behavior given a set of changing parameters. An optimization module can fit a model to multimodal empirical data using an evolutionary algorithm. neurolib is designed to be extendable such that custom neural mass models can be implemented easily, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

Cogn Comput. 1158(31) (2021)
Cogn Comput. 1132(15) (2023)