Control properties of multiagent dynamical systems modelling brain neural networks

García-Planas, M. Isabel

doi:10.1109/macise49704.2020.00025

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…Controllability is one of the most important properties of dynamical systems, and that is why a great portion of the literature refers to this concept ( [6,8,9], among others).…”

Section: Controllability and Exact Controllabilitymentioning

confidence: 99%

“…García-Planas in [6] showed that a noise-free multisystem of linear discrete-time and time-invariant model…”

Section: Introductionmentioning

confidence: 99%

“…Structural controllability theory could be a good tool to control structured linear systems, in this way Garcia-Planas in ( [6], showed that structural controllability in a mathematical instrument which could be inferred to multi-agent systems in which each of the agents has a previously determined structure.…”

Section: Introductionmentioning

confidence: 99%

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Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach

García-Planas

García-Camba

2022

Mathematics

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The human brain can be interpreted mathematically as a linear dynamical system that shifts through various cognitive regions promoting more or less complicated behaviors. The dynamics of brain neural network play a considerable role in cognitive function and therefore of interest in the bid to understand the learning processes and the evolution of possible disorders. The mathematical theory of systems and control makes available procedures, concepts, and criteria that can be applied to ease the perception of the dynamic processes that administer the evolution of the brain with learning and its control with treatment in case of disorder. In this work, a geometric study through the conception of exact controllability is comprehended to detect the minimum set and the location of the driving nodes of learning. We will describe the different roles of the nodes in the control of the paths of brain networks and show the transition of some driving nodes and the preservation of the rest in the course of learning in patients with some learning disability.

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“…Controllability is one of the most important properties of dynamical systems, and that is why a great portion of the literature refers to this concept ( [6,8,9], among others).…”

Section: Controllability and Exact Controllabilitymentioning

confidence: 99%

“…García-Planas in [6] showed that a noise-free multisystem of linear discrete-time and time-invariant model…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach

García-Planas

García-Camba

2022

Mathematics

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“…García-Planas in [5] showed that a noise-free multisystem of linear discrete-time and time-invariant model…”

Section: Networkmentioning

confidence: 99%

Control theory tools for best understanding brain Learning Disorders

García-Planas

García-Camba²

2022

2022 7th International Conference on Mathematics and Computers in Sciences and Industry (MCSI)

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Recent developments in cognitive neuroscience have made it possible to take a step forward in understanding the processes involved in the development of learning. Sometimes there are alterations in learning processes that cause structural and functional dysfunctions at the brain level called learning disorders. These disorders are brain-specific disorders that for detection, are needed specific screeners are needed, rather than more general tests of learning abilities.One of the brain disorders that is little studied and that causes great damage to those who suffer from it is dyscalculia. The lack of study on this alteration means that many cases remain undiagnosed or misdiagnosed, and therefore remain untreated. In order to make a correct diagnosis for the correct treatment we have been able to prove that by performing a BAEP and analyzing wave VI, the presence or absence of a learning disorder can be objectified. Another problem that arises is that of the evolution of the disorder, for which a good tool could be the modeling of brain activity through dynamic systems. In this work, in addition to delving into how to make a correct diagnosis, the evolution and possible control of linear dynamic systems that model the process in order to infer it are analyzed, taking advantage of brain plasticity that can facilitate the control that allows improvement of the dysfunction

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State Feedback Control of Multiagent Singular Linear Systems Representing Brain Neural Networks

Garcia-planas

2024

Universal Journal of Mathematics and Applications

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A multi-agent singular system is an extension of a traditional multi-agent system. The behavior of neural networks within the brain is crucial for cognitive functions, making it essential to understand the learning processes and the development of potential disorders. This study utilizes the analysis of singular linear systems representing brain neural networks to delve into the complexities of the human brain. In this context, the digraph approach is a powerful method for unraveling the intricate neural interconnections. Directed graphs, or digraphs, provide an intuitive visual representation of the causal and influential relationships among different neural units, facilitating a detailed analysis of network dynamics. This work explores the use of digraphs in analyzing singular linear multi-agent systems that model brain neural networks, emphasizing their significance and potential in enhancing our understanding of cognition and brain function.

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Control properties of multiagent dynamical systems modelling brain neural networks

Cited by 5 publications

References 18 publications

Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach

Controllability of Brain Neural Networks in Learning Disorders—A Geometric Approach

Control theory tools for best understanding brain Learning Disorders

State Feedback Control of Multiagent Singular Linear Systems Representing Brain Neural Networks

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