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

Cakan, Caglar; Jajcay, Nikola; Obermayer, Klaus

doi:10.1007/s12559-021-09931-9

Cited by 41 publications

(34 citation statements)

References 81 publications

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“…Finally, these offspring are mutated, added to the total population, and the procedure is repeated until a stopping condition is reached, such as reaching a maximum number of generations. The multi-objective optimization is based on non-dominated sorting and several other evolutionary operators as introduced in Deb et al ( 2002 ) which are implemented in our software package neurolib (Cakan et al, 2021 ) using the evolutionary algorithm framework DEAP (Fortin et al, 2012 ).…”

Section: Methodsmentioning

confidence: 99%

“…All simulations, the parameter explorations, and the optimization framework including the evolutionary algorithm are implemented as a Python package in our whole-brain neural mass modeling framework neurolib (Cakan et al, 2021 ) which can be found at https://github.com/neurolib-dev/neurolib . The forward Euler method was used for the numerical integration with an integration time step of dt = 0.1 ms.…”

Section: Methodsmentioning

confidence: 99%

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Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

Cakan

Dimulescu

Khakimova

et al. 2022

Front. Comput. Neurosci.

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During slow-wave sleep, the brain is in a self-organized regime in which slow oscillations (SOs) between up- and down-states travel across the cortex. While an isolated piece of cortex can produce SOs, the brain-wide propagation of these oscillations are thought to be mediated by the long-range axonal connections. We address the mechanism of how SOs emerge and recruit large parts of the brain using a whole-brain model constructed from empirical connectivity data in which SOs are induced independently in each brain area by a local adaptation mechanism. Using an evolutionary optimization approach, good fits to human resting-state fMRI data and sleep EEG data are found at values of the adaptation strength close to a bifurcation where the model produces a balance between local and global SOs with realistic spatiotemporal statistics. Local oscillations are more frequent, last shorter, and have a lower amplitude. Global oscillations spread as waves of silence across the undirected brain graph, traveling from anterior to posterior regions. These traveling waves are caused by heterogeneities in the brain network in which the connection strengths between brain areas determine which areas transition to a down-state first, and thus initiate traveling waves across the cortex. Our results demonstrate the utility of whole-brain models for explaining the origin of large-scale cortical oscillations and how they are shaped by the connectome.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

Cakan

Dimulescu

Khakimova

et al. 2022

Front. Comput. Neurosci.

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“…Another emerging simulator at the whole brain scale level is neurolib (Cakan et al, 2021). Similar to TVB, neurolib provides the end user with a variety of neural mass models, the ability to create networks based on empirical connectivity data and generate simulated signals which can be optimized using parameter fitting methods against empirical data.…”

Section: Simulation Enginesmentioning

confidence: 99%

ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

Herbers

Calvo

Diaz-Pier

et al. 2022

Front. Neuroinform.

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An open challenge on the road to unraveling the brain's multilevel organization is establishing techniques to research connectivity and dynamics at different scales in time and space, as well as the links between them. This work focuses on the design of a framework that facilitates the generation of multiscale connectivity in large neural networks using a symbolic visual language capable of representing the model at different structural levels—ConGen. This symbolic language allows researchers to create and visually analyze the generated networks independently of the simulator to be used, since the visual model is translated into a simulator-independent language. The simplicity of the front end visual representation, together with the simulator independence provided by the back end translation, combine into a framework to enhance collaboration among scientists with expertise at different scales of abstraction and from different fields. On the basis of two use cases, we introduce the features and possibilities of our proposed visual language and associated workflow. We demonstrate that ConGen enables the creation, editing, and visualization of multiscale biological neural networks and provides a whole workflow to produce simulation scripts from the visual representation of the model.

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“…If not mentioned otherwise, simulated time was t = 30 seconds with an integration timestep of dt = 0.01 ms. After integration, time series were subsampled at dt samp = 10 ms. The thalamocortical model was simulated using the neurolib library [56]. neurolib is a computational framework for wholebrain modeling written in Python.…”

Section: Numerical Simulationsmentioning

confidence: 99%

Cross-frequency slow oscillation–spindle coupling in a biophysically realistic thalamocortical neural mass model

Jajcay

Cakan

Obermayer

2021

Preprint

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Sleep manifests itself by the spontaneous emergence of characteristic oscillatory rhythms, which often time-lock and are implicated in the memory formation. Here, we analyze a neural mass model of the thalamo-cortical loop of which the cortical node can generate slow oscillations (approx. 1 Hz) while its thalamic component can generate sleep spindles of σ-band activity (12-15 Hz). We study the dynamics for different coupling strengths between the thalamic and cortical nodes, for different conductance values of the thalamic node's potassium leak and anomalous rectifying currents, and for different parameter regimes of the cortical node. The latter are: (1) a low activity (DOWN) state with noise-induced, transient excursions into a high activity (UP) state, (2) an adaptation induced slow oscillation limit cycle with alternating UP and DOWN states, and (3) a high activity (UP) state with noise-induced, transient excursions into the low activity (DOWN) state. During UP states, thalamic spindling is abolished or reduced. During DOWN states, the thalamic node generates sleep spindles, which in turn can cause DOWN to UP transitions in the cortical node. Consequently, this leads to spindle-induced UP state transitions in parameter regime (1), thalamic spindles induced in some but not all DOWN states in regime (2), and thalamic spindles following UP to DOWN transitions in regime (3). The spindle-induced σ-band activity in the cortical node, however, is typically strongest during the UP state, which follows a DOWN state "window of opportunity" for spindling. When the cortical node is parametrized in regime (3), the model well explains the interactions between slow oscillations and sleep spindles observed experimentally during Non-Rapid Eye Movement sleep. The model is computationally efficient and can be integrated into large-scale modeling frameworks to study spatial aspects like sleep wave propagation.

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

Cited by 41 publications

References 81 publications

Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

Spatiotemporal Patterns of Adaptation-Induced Slow Oscillations in a Whole-Brain Model of Slow-Wave Sleep

ConGen—A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks

Cross-frequency slow oscillation–spindle coupling in a biophysically realistic thalamocortical neural mass model

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