neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

0547193 - ÚI 2024 RIV US eng J - Článek v odborném periodiku
Cakan, C. - Jajcay, Nikola - Obermayer, K.
neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling.
Cognitive Computation. Roč. 15, July 2023 (2023), s. 1132-1152. ISSN 1866-9956. E-ISSN 1866-9964
Grant CEP: GA MŠMT(CZ) EF19_074/0016209
Institucionální podpora: RVO:67985807
Klíčová slova: Whole-brain model * Neural mass model * Brain networks * Neuroinformatics
Obor OECD: Neurosciences (including psychophysiology
Impakt faktor: 5.4, rok: 2022
Způsob publikování: Open access
http://dx.doi.org/10.1007/s12559-021-09931-9

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 made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, 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.
Trvalý link: http://hdl.handle.net/11104/0323502