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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F23%3A00547193" target="_blank" >RIV/67985807:_____/23:00547193 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/s12559-021-09931-9" target="_blank" >http://dx.doi.org/10.1007/s12559-021-09931-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s12559-021-09931-9" target="_blank" >10.1007/s12559-021-09931-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30103 - Neurosciences (including psychophysiology)

Projekt

<a href="/cs/project/EF19_074%2F0016209" target="_blank" >EF19_074/0016209: Modelování spícího mozku: směrem k neurálnímu masovému modelu spánkových rytmů a jejich interakcí</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Cognitive Computation

ISSN

1866-9956

e-ISSN

1866-9964

Svazek periodika

15

Číslo periodika v rámci svazku

July 2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

21

Strana od-do

1132-1152

Kód UT WoS článku

000706010400001

EID výsledku v databázi Scopus

2-s2.0-85116942431