Dynamics of EEGs as Signals of Neuronal Populations

2017 ◽  
Author(s):  
Fabrice Wendling ◽  
Fernando H. Lopes da Silva
Keyword(s):  
Dynamic Systems ◽  
Neuronal Networks ◽  
Computational Models ◽  
Physiological Data ◽  
Biophysical Properties ◽  
Eeg Signals ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Topological Models ◽  
Different Levels

This chapter gives an overview of approaches used to understand the generation of electroencephalographic (EEG) signals using computational models. The basic concept is that appropriate modeling of neuronal networks, based on relevant anatomical and physiological data, allows researchers to test hypotheses about the nature of EEG signals. Here these models are considered at different levels of complexity. The first level is based on single cell biophysical properties anchored in classic Hodgkin-Huxley theory. The second level emphasizes on detailed neuronal networks and their role in generating different kinds of EEG oscillations. At the third level are models derived from the Wilson-Cowan approach, which constitutes the backbone of neural mass models. Another part of the chapter is dedicated to models of epileptiform activities. Finally, the themes of nonlinear dynamic systems and topological models in EEG generation are discussed.

scholarly journals Modeling the Generation of Phase-Amplitude Coupling in Cortical Circuits: From Detailed Networks to Neural Mass Models

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Roberto C. Sotero
Keyword(s):  
Computational Models ◽  
High Frequency Oscillation ◽  
Mathematical Framework ◽  
Cortical Circuits ◽  
Frequency Oscillation ◽  
Neuronal Populations ◽  
Neural Mass ◽  
Phase Amplitude ◽  
The Brain ◽  
Neuroscience Community

Phase-amplitude coupling (PAC), the phenomenon where the amplitude of a high frequency oscillation is modulated by the phase of a lower frequency oscillation, is attracting an increasing interest in the neuroscience community due to its potential relevance for understanding healthy and pathological information processing in the brain. PAC is a diverse phenomenon, having been experimentally detected in at least ten combinations of rhythms: delta-theta, delta-alpha, delta-beta, delta-gamma, theta-alpha, theta-beta, theta-gamma, alpha-beta, alpha-gamma, and beta-gamma. However, a complete understanding of the biophysical mechanisms generating this diversity is lacking. Here we review computational models of PAC generation that range from detailed models of neuronal networks, where each cell is described by Hodgkin-Huxley-type equations, to neural mass models (NMMs) where only the average activities of neuronal populations are considered. We argue that NMMs are an appropriate mathematical framework (due to the small number of parameters and variables involved and the richness of the dynamics they can generate) to study the PAC phenomenon.

scholarly journals Neural Mass Model-Based Different EEG Signal Generation and Analysis in Simulink Sheikh Md. Rabiul Islam1, Md. Shakibul Islam2

2021 ◽  
pp. 1-7
Author(s):  
Sheikh Md. Rabiul Islam ◽  
◽  
Md. Shakibul Islam ◽  
Keyword(s):  
Signal Generation ◽  
Neural Mass Model ◽  
Pathophysiological Mechanism ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Mass Model ◽  
Detailed Model ◽  
Simulation Based ◽  
Neural Mass ◽  
The Brain

The electroencephalogram (EEG) is an electrophysiological monitoring strategy that records the spontaneous electrical movement of the brain coming about from ionic current inside the neurons of the brain. The importance of the EEG signal is mainly the diagnosis of different mental and brain neurodegenerative diseases and different abnormalities like seizure disorder, encephalopathy, dementia, memory problem, sleep disorder, stroke, etc. The EEG signal is very useful for someone in case of a coma to determine the level of brain activity. So, it is very important to study EEG generation and analysis. To reduce the complexity of understanding the pathophysiological mechanism of EEG signal generation and their changes, different simulation-based EEG modeling has been developed which are based on anatomical equivalent data. In this paper, Instead of a detailed model a neural mass model has been used to implement different simulation-based EEG models for EEG signal generation which refers to the simplified and straightforward method. This paper aims to introduce obtained EEG signals of own implementation of the Lopes da Silva model, Jansen-Rit model, and Wendling model in Simulink and to compare characteristic features with real EEG signals and better understanding the EEG abnormalities especially the seizure-like signal pattern.

scholarly journals A Fuzzy Control Heuristic Applied to Non-linear Dynamic System Using a Fuzzy Knowledge Representation

2010 ◽  
Vol 5 (5) ◽  
pp. 664 ◽  
Author(s):  
Felisa M. Cordova ◽  
Guillermo Leyton
Keyword(s):  
Knowledge Representation ◽  
Fuzzy Control ◽  
Dynamic Systems ◽  
Nonlinear Dynamic ◽  
Nonlinear Dynamic Systems ◽  
System State ◽  
Specific Level ◽  
Base Matrix ◽  
Different Levels ◽  
Fuzzy Knowledge

This paper presents the design of a fuzzy control heuristic that can be applied for modeling nonlinear dynamic systems using a fuzzy knowledge representation. Nonlinear dynamic systems have been modeled traditionally on the basis of connections between the subsystems that compose it. Nevertheless, this model design does not consider some of the following problems: existing dynamics between the subsystems; order and priority of the connection between subsystems; degrees of influence or causality between subsystems; particular state of each subsystem and state of the system on the basis of the combination of the diverse states of the subsystems; positive or negative influences between subsystems. In this context, the main objective of this proposal is to manage the whole system state by managing the state combination of the subsystems involved. In the proposed design the diverse states of subsystems at different levels are represented by a knowledge base matrix of fuzzy intervals (KBMFI). This type of structure is a fuzzy hypercube that provides facilities operations like: insert, delete, and switching. It also allows Boolean operations between different KBMFI and inferences. Each subsystem in a specific level and its connectors are characterized by factors with fuzzy attributes represented by membership functions. Existing measures the degree of influence among the different levels are obtained (negatives, positives). In addition, the system state is determined based on the combination of the statements of the subsystems (stable, oscillatory, attractor, chaos). It allows introducing the dynamic effects in the calculation of each output level. The control and search of knowledge patterns are made by means of a fuzzy control heuristic. Finally, an application to the co-ordination of the activities among different levels of the operation of an underground mine is developed and discussed.

Language Structures in Cellular Automata

2010 ◽  
pp. 248-281
Author(s):  
Eleonora Bilotta ◽  
Pietro Pantano
Keyword(s):  
Cellular Automata ◽  
Computational Models ◽  
Dynamic Interaction ◽  
Cognitive Systems ◽  
Human Language ◽  
Natural Languages ◽  
Modern Species ◽  
Evolutionary Success ◽  
Different Levels ◽  
Infinite Range

The ingenuity of nature and the power of DNA have generated an infinite range of languages - including human language. The existence of these languages inspires us to design artificial cognitive systems whose dynamic interaction with the environment is grounded, at least to some extent, on the same basic laws. Modern scientific knowledge provides us with new opportunities to investigate and understand the logic underlying biological life. We can then use this logic to derive design principles and computational models for artificial systems. The technologies we apply in these studies provide us with new insights into the complexity of the processes underlying the evolutionary success of modern species. We have yet to fully penetrate the mysteries of these natural languages. Nonetheless, the literature suggests (Chomsky, 1957; Aronof & Rees-Miller, 2003; Bilotta & Pantano, 2006) that while the superficial features of different languages depend on different physical supports and different mechanisms, their deep structures share common rules. These constitute linguistic universals, organized at different levels of complexity, where each level has its own rules of composition. At all levels, we can consider these rules as “production rules” or even as rules of reproduction.

scholarly journals Strain rate–dependent mechanical metamaterials

2020 ◽  
Vol 6 (25) ◽  
pp. eaba0616 ◽  
Author(s):  
S. Janbaz ◽  
K. Narooei ◽  
T. van Manen ◽  
A. A. Zadpoor
Keyword(s):  
Strain Rate ◽  
Viscoelastic Properties ◽  
Computational Models ◽  
Geometric Imperfections ◽  
Rate Dependent ◽  
Instability Modes ◽  
Mechanical Metamaterials ◽  
Unique Strain ◽  
Different Levels ◽  
Static Nature

Mechanical metamaterials are usually designed to exhibit novel properties and functionalities that are rare or even unprecedented. What is common among most previous designs is the quasi-static nature of their mechanical behavior. Here, we introduce a previously unidentified class of strain rate-dependent mechanical metamaterials. The principal idea is to laterally attach two beams with very different levels of strain rate-dependencies to make them act as a single bi-beam. We use an analytical model and multiple computational models to explore the instability modes of such a bi-beam construct, demonstrating how different combinations of hyperelastic and viscoelastic properties of both beams, as well as purposefully introduced geometric imperfections, could be used to create robust and highly predictable strain rate-dependent behaviors of bi-beams. We then use the bi-beams to design and experimentally realize lattice structures with unique strain rate-dependent properties including switching between auxetic and conventional behaviors and negative viscoelasticity.

Neurophysiological Analysis of the Genesis Mechanism of EEG During the Interictal and Ictal Periods Using a Multiple Neural Masses Model

2017 ◽  
Vol 28 (01) ◽  
pp. 1750027 ◽  
Author(s):  
Zhen Ma
Keyword(s):  
Matching Pursuit ◽  
Neural Mass Model ◽  
Model Parameters ◽  
Eeg Signals ◽  
Neural Signals ◽  
Mass Model ◽  
Neurophysiological Mechanism ◽  
Norm Minimization ◽  
Neural Mass ◽  
Inhibitory Strength

Electroencephalography (EEG) is an important method to investigate the neurophysiological mechanism underlying epileptogenesis to identify new therapies for the treatment of epilepsy. The neurophysiologically based neural mass model (NMM) can build a bridge between signal processing and neurophysiology, which can be used as a platform to explore the neurophysiological mechanism of epileptogenesis. Most EEG signals cannot be regarded as the outputs of a single NMM with identical model parameters. The outputs of NMM are simple because the diversity of neural signals in the same NMM is ignored. To improve the simulation of EEG signals, a multiple NMM is proposed, the output of which is the linear combination of the outputs of all NMMs. The NMM number is not fixed and is minimized under the premise of guaranteeing the fitting effect. Orthogonal matching pursuit is used to solve a constrained [Formula: see text] norm minimization problem for NMM number and the strength of every NMM. The results showed that the NMM number was significantly lower during the ictal period than during the interictal period, and the strength of major NMMs increased. This indicates that neural masses fuse into fewer larger neural masses with greater strength. The distribution of excitatory and inhibitory strength during the ictal and interictal periods was similar, whereas the excitation/inhibition ratio was higher during the ictal period than during the interictal period.

scholarly journals A Novel Recognition Strategy for Epilepsy EEG Signals Based on Conditional Entropy of Ordinal Patterns

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1092
Author(s):  
Xian Liu ◽  
Zhuang Fu
Keyword(s):  
Conditional Entropy ◽  
Neural Mass Model ◽  
High Temporal Resolution ◽  
Eeg Signals ◽  
Mass Model ◽  
Neural Mass ◽  
Brain Rhythm ◽  
Brain States ◽  
Ordinal Patterns ◽  
Electroencephalogram Eeg

Epilepsy is one of the most ordinary neuropathic illnesses, and electroencephalogram (EEG) is the essential method for recording various brain rhythm activities due to its high temporal resolution. The conditional entropy of ordinal patterns (CEOP) is known to be fast and easy to implement, which can effectively measure the irregularity of the physiological signals. The present work aims to apply the CEOP to analyze the complexity characteristics of the EEG signals and recognize the epilepsy EEG signals. We discuss the parameter selection and the performance analysis of the CEOP based on the neural mass model. The CEOP is applied to the real EEG database of Bonn epilepsy for identification. The results show that the CEOP is an excellent metrics for the analysis and recognition of epileptic EEG signals. The differences of the CEOP in normal and epileptic brain states suggest that the CEOP could be a judgment tool for the diagnosis of the epileptic seizure.

scholarly journals A framework to estimate cognitive load using physiological data

2020 ◽  
Author(s):  
Muneeb Imtiaz Ahmad ◽  
Ingo Keller ◽  
David A. Robb ◽  
Katrin S. Lohan
Keyword(s):  
Cognitive Load ◽  
Human Error ◽  
Human Performance ◽  
Predictive Accuracy ◽  
Pupil Diameter ◽  
High Accuracy ◽  
Physiological Data ◽  
Workload Management ◽  
Different Levels ◽  
Diameter Change

Abstract Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load.

scholarly journals Functional CB1 Receptors Are Broadly Expressed in Neocortical GABAergic and Glutamatergic Neurons

2007 ◽  
Vol 97 (4) ◽  
pp. 2580-2589 ◽  
Author(s):  
Elisa L. Hill ◽  
Thierry Gallopin ◽  
Isabelle Férézou ◽  
Bruno Cauli ◽  
Jean Rossier ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Cannabinoid Receptor ◽  
Physiological Data ◽  
Glutamatergic Neurons ◽  
Neuronal Populations ◽  
Postsynaptic Currents ◽  
Neocortical Neurons ◽  
Neuronal Types ◽  
Cb1 Antagonist ◽  
Whole Cell Recordings

The cannabinoid receptor CB1 is found in abundance in brain neurons, whereas CB2 is essentially expressed outside the brain. In the neocortex, CB1 is observed predominantly on large cholecystokinin (CCK)-expressing interneurons. However, physiological evidence suggests that functional CB1 are present on other neocortical neuronal types. We investigated the expression of CB1 and CB2 in identified neurons of rat neocortical slices using single-cell RT-PCR. We found that 63% of somatostatin (SST)-expressing and 69% of vasoactive intestinal polypeptide (VIP)-expressing interneurons co-expressed CB1. As much as 49% of pyramidal neurons expressed CB1. In contrast, CB2 was observed in a small proportion of neocortical neurons. We performed whole cell recordings of pyramidal neurons to corroborate our molecular findings. Inhibitory postsynaptic currents (IPSCs) induced by a mixed muscarinic/nicotinic cholinergic agonist showed depolarization-induced suppression of inhibition and were decreased by the CB1 agonist WIN-55212-2 (WIN-2), suggesting that interneurons excited by cholinergic agonists (mainly SST and VIP neurons) possess CB1. IPSCs elicited by a nicotinic receptor agonist were also reduced in the presence of WIN-2, suggesting that neurons excited by nicotinic agonists (mainly VIP neurons) indeed possess CB1. WIN-2 largely decreased excitatory postsynaptic currents evoked by intracortical electrical stimulation, pointing at the presence of CB1 on glutamatergic pyramidal neurons. All WIN-2 effects were strongly reduced by the CB1 antagonist AM 251. We conclude that CB1 is expressed in various neocortical neuronal populations, including glutamatergic neurons. Our combined molecular and physiological data suggest that CB1 widely mediates endocannabinoid effects on glutamatergic and GABAergic transmission to modulate cortical networks.

scholarly journals Modeling fMRI signals can provide insights into neural processing in the cerebral cortex

2015 ◽  
Vol 114 (2) ◽  
pp. 768-780 ◽  
Author(s):  
Simo Vanni ◽  
Fariba Sharifian ◽  
Hanna Heikkinen ◽  
Ricardo Vigário
Keyword(s):  
Computational Models ◽  
Real Life ◽  
Mass Action ◽  
System Level ◽  
Specific System ◽  
Physiological Mechanisms ◽  
Mathematical Functions ◽  
Neural Mass ◽  
Recent Developments ◽  
The Relationship

Every stimulus or task activates multiple areas in the mammalian cortex. These distributed activations can be measured with functional magnetic resonance imaging (fMRI), which has the best spatial resolution among the noninvasive brain imaging methods. Unfortunately, the relationship between the fMRI activations and distributed cortical processing has remained unclear, both because the coupling between neural and fMRI activations has remained poorly understood and because fMRI voxels are too large to directly sense the local neural events. To get an idea of the local processing given the macroscopic data, we need models to simulate the neural activity and to provide output that can be compared with fMRI data. Such models can describe neural mechanisms as mathematical functions between input and output in a specific system, with little correspondence to physiological mechanisms. Alternatively, models can be biomimetic, including biological details with straightforward correspondence to experimental data. After careful balancing between complexity, computational efficiency, and realism, a biomimetic simulation should be able to provide insight into how biological structures or functions contribute to actual data processing as well as to promote theory-driven neuroscience experiments. This review analyzes the requirements for validating system-level computational models with fMRI. In particular, we study mesoscopic biomimetic models, which include a limited set of details from real-life networks and enable system-level simulations of neural mass action. In addition, we discuss how recent developments in neurophysiology and biophysics may significantly advance the modelling of fMRI signals.

Sign in / Sign up

Export Citation Format

Share Document