DeepAI AI Chat
Log In Sign Up

Computational tool to study high dimensional dynamic in NMM

by   A. González-Mitjans, et al.

Neuroscience has shown great progress in recent years. Several of the theoretical bases have arisen from the examination of dynamic systems, using Neural Mass Models (NMMs). Due to the largescale brain dynamics of NMMs and the difficulty of studying nonlinear systems, the local linearization approach to discretize the state equation was used via an algebraic formulation, as it intervenes favorably in the speed and efficiency of numerical integration. To study the spacetime organization of the brain and generate more complex dynamics, three structural levels (cortical unit, population and system) were defined and assumed, in which the new assumed representation for conduction delays and new ways of connecting were defined. This is a new time-delay NMM, which can simulate several types of EEG activities since kinetics information was considered at three levels of complexity. Results obtained in this analysis provide additional theoretical foundations and indicate specific characteristics for understanding neurodynamic.


page 9

page 10


Inferring untrained complex dynamics of delay systems using an adapted echo state network

Caused by finite signal propagation velocities, many complex systems fea...

Fractal and multifractal properties of electrographic recordings of human brain activity

The brain is a system operating on multiple time scales, and characteris...

An Error Analysis Framework for Neural Network Modeling of Dynamical Systems

We propose a theoretical framework for investigating a modeling error ca...

Exploring hyper-parameter spaces of neuroscience models on high performance computers with Learning to Learn

Neuroscience models commonly have a high number of degrees of freedom an...

Learning Robust State Observers using Neural ODEs (longer version)

Relying on recent research results on Neural ODEs, this paper presents a...

Region-Referenced Spectral Power Dynamics of EEG Signals: A Hierarchical Modeling Approach

Functional brain imaging through electroencephalography (EEG) relies upo...