Caglar Cakan will introduce neurolib and discuss its development in this developer session.

• Date: February 23, 2021. 1700 UTC/ 1800 Berlin time (Click here to see your local time).
• Location (Zoom): (link no longer valid)

The abstract for the talk is below:

neurolib is a computational framework for whole-brain modelling 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 structural connectivity data, i.e. the connectome of the brain. neurolib can load structural and functional data sets, 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 to empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is possible using a parameter exploration module, which allows to characterize the model’s behaviour given a set of changing parameters. An optimization module allows for fitting a model to multimodal empirical data using an evolutionary algorithm. Besides its included functionality, neurolib is designed to be extendable such that custom neural mass models can be implemented easily. neurolib offers a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure-function relationship of brain networks, and for in-silico optimization of whole-brain models.