Abstract
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.
Highlights
Mathematical modeling and computer simulations are fundamental for understanding complex natural systems
Each brain region is represented by a neural mass model which is connected to other brain regions according to the underlying network structure of the brain, known as the connectome [23]
The structural connectivity of the brain is typically obtained by diffusion tensor imaging (DTI) which is used to infer the long-range axonal white matter tracts in the brain, a method known as fiber tractography
Summary
Mathematical modeling and computer simulations are fundamental for understanding complex natural systems. While microscopic simulations of neural systems often rely on large spiking neural network simulations where the membrane voltage of every neuron is simulated and kept track of, neural mass models typically consist of a system of differential equations that govern the macroscopic variables of a large system, such as the population firing rate. These models are considered useful for representing the average activity of a large neural population, e.g., a brain area. At the other end of the spectrum of neural mass models, simple phenomenological oscillator models [5,6,7,8] are used to
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