19 research outputs found
Exponential multistability of memristive Cohen-Grossberg neural networks with stochastic parameter perturbations
© 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.Due to instability being induced easily by parameter disturbances of network systems, this paper investigates the multistability of memristive Cohen-Grossberg neural networks (MCGNNs) under stochastic parameter perturbations. It is demonstrated that stable equilibrium points of MCGNNs can be flexibly located in the odd-sequence or even-sequence regions. Some sufficient conditions are derived to ensure the exponential multistability of MCGNNs under parameter perturbations. It is found that there exist at least (w+2) l (or (w+1) l) exponentially stable equilibrium points in the odd-sequence (or the even-sequence) regions. In the paper, two numerical examples are given to verify the correctness and effectiveness of the obtained results.Peer reviewe
On fixed points, their geometry and application to satellite web coupling problem in Sâmetric spaces
We introduce an Mâclass function in an Sâmetric space which is a viable, productive, and powerful technique for finding the existence of a fixed point and fixed circle. Our conclusions unify, improve, extend, and generalize numerous results to a widespread class of discontinuous maps. Next, we introduce notions of a fixed ellipse (elliptic disc) in an Sâmetric space to investigate the geometry of the collection of fixed points and prove fixed ellipse (elliptic disc) theorems. In the sequel, we validate these conclusions with illustrative examples. We explore some conditions which eliminate the possibility of the identity map in the existence of an ellipse (elliptic disc). Some remarks, propositions, and examples to exhibit the feasibility of the results are presented. The paper is concluded with a discussion of activation functions that are discontinuous in nature and, consequently, utilized in a neural network for increasing the storage capacity. Towards the end, we solve the satellite web coupling problem and propose two open problems
The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields
The cortex is a complex system, characterized by its dynamics and architecture,
which underlie many functions such as action, perception, learning, language,
and cognition. Its structural architecture has been studied for more than a
hundred years; however, its dynamics have been addressed much less thoroughly.
In this paper, we review and integrate, in a unifying framework, a variety of
computational approaches that have been used to characterize the dynamics of the
cortex, as evidenced at different levels of measurement. Computational models at
different spaceâtime scales help us understand the fundamental
mechanisms that underpin neural processes and relate these processes to
neuroscience data. Modeling at the single neuron level is necessary because this
is the level at which information is exchanged between the computing elements of
the brain; the neurons. Mesoscopic models tell us how neural elements interact
to yield emergent behavior at the level of microcolumns and cortical columns.
Macroscopic models can inform us about whole brain dynamics and interactions
between large-scale neural systems such as cortical regions, the thalamus, and
brain stem. Each level of description relates uniquely to neuroscience data,
from single-unit recordings, through local field potentials to functional
magnetic resonance imaging (fMRI), electroencephalogram (EEG), and
magnetoencephalogram (MEG). Models of the cortex can establish which types of
large-scale neuronal networks can perform computations and characterize their
emergent properties. Mean-field and related formulations of dynamics also play
an essential and complementary role as forward models that can be inverted given
empirical data. This makes dynamic models critical in integrating theory and
experiments. We argue that elaborating principled and informed models is a
prerequisite for grounding empirical neuroscience in a cogent theoretical
framework, commensurate with the achievements in the physical sciences
Classes de dynamiques neuronales et correlations structurées par l'experience dans le cortex visuel.
Neuronal activity is often considered in cognitive neuroscience by the evoked response but most the energy used by the brain is devoted to the sustaining of ongoing dynamics in cortical networks. A combination of classification algorithms (K means, Hierarchical tree, SOM) is used on intracellular recordings of the primary visual cortex of the cat to define classes of neuronal dynamics and to compare it with the activity evoked by a visual stimulus. Those dynamics can be studied with simplified models (FitzHugh Nagumo, hybrid dynamical systems, Wilson Cowan) for which an analysis is presented. Finally, with simulations of networks composed of columns of spiking neurons, we study the ongoing dynamics in a model of the primary visual cortex and their effect on the response evoked by a stimulus. After a learning period during which visual stimuli are presented, waves of depolarization propagate through the network. The study of correlations in this network shows that the ongoing dynamics reflect the functional properties acquired during the learning period.L'activitĂ© neuronale est souvent considĂ©rĂ©e en neuroscience cognitive par la rĂ©ponse Ă©voquĂ©e mais l'essentiel de l'Ă©nergie consommĂ©e par le cerveau permet d'entretenir les dynamiques spontanĂ©es des rĂ©seaux corticaux. L'utilisation combinĂ©e d'algorithmes de classification (K means, arbre hirarchique, SOM) sur des enregistrements intracellulaires du cortex visuel primaire du chat nous permet de dĂ©finir des classes de dynamiques neuronales et de les comparer l'activitĂ© Ă©voquĂ©e par un stimulus visuel. Ces dynamiques peuvent ĂȘtre Ă©tudiĂ©es sur des systĂšmes simplifiĂ©s (FitzHugh-Nagumo, systĂšmes dynamiques hybrides, Wilson-Cowan) dont nous prĂ©sentons l'analyse. Enfin, par des simulations de rĂ©seaux composĂ©s de colonnes de neurones, un modĂšle du cortex visuel primaire nous permet d'Ă©tudier les dynamiques spontanĂ©es et leur effet sur la rĂ©ponse Ă un stimulus. AprĂšs une pĂ©riode d'apprentissage pendant laquelle des stimuli visuels sont presentĂ©s, des vagues de dĂ©polarisation se propagent dans le rĂ©seau. L'Ă©tude des correlations dans ce rĂ©seau montre que les dynamiques spontanĂ©es reflĂštent les propriĂ©tĂ©s fonctionnelles acquises au cours de l'apprentissage
26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15â20 July 2017
This work was produced as part of the activities of FAPESP Research,\ud
Disseminations and Innovation Center for Neuromathematics (grant\ud
2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud
FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud
supported by a CNPq fellowship (grant 306251/2014-0)
Generalized averaged Gaussian quadrature and applications
A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal
MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications
Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described
International Conference on Mathematical Analysis and Applications in Science and Engineering â Book of Extended Abstracts
The present volume on Mathematical Analysis and Applications in Science and Engineering - Book of
Extended Abstracts of the ICMASCâ2022 collects the extended abstracts of the talks presented at the
International Conference on Mathematical Analysis and Applications in Science and Engineering â
ICMA2SC'22 that took place at the beautiful city of Porto, Portugal, in June 27th-June 29th 2022 (3 days).
Its aim was to bring together researchers in every discipline of applied mathematics, science, engineering,
industry, and technology, to discuss the development of new mathematical models, theories, and
applications that contribute to the advancement of scientific knowledge and practice. Authors proposed
research in topics including partial and ordinary differential equations, integer and fractional order
equations, linear algebra, numerical analysis, operations research, discrete mathematics, optimization,
control, probability, computational mathematics, amongst others.
The conference was designed to maximize the involvement of all participants and will present the state-of-
the-art research and the latest achievements.info:eu-repo/semantics/publishedVersio