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The human brain is autonomously active, being characterized by a self-sustained neural activity which would be present even in the absence of external sensory stimuli. Here we study the interrelation between the self-sustained activity in…

Neurons and Cognition · Quantitative Biology 2017-11-27 C. Gros , G. Kaczor

We consider an abstract nonlinear second order evolution equation, inspired by some models for damped oscillations of a beam subject to external loads or magnetic fields, and shaken by a transversal force. When there is no external force,…

Analysis of PDEs · Mathematics 2017-10-24 Marina Ghisi , Massimo Gobbino , Alain Haraux

The activity patterns of highly developed cognitive systems like the human brain are dominated by autonomous dynamical processes, that is by a self-sustained activity which would be present even in the absence of external sensory stimuli.…

Neurons and Cognition · Quantitative Biology 2008-04-14 Claudius Gros , Gregor Kaczor

In this work we consider the non local evolution equation with time-dependent terms which arises in models of phase separation in $\mathbb{R}^N$ \[ \partial_t u=- u + g \left(\beta(J*u) +\beta h(t,u)\right) \] under some restrictions on…

Dynamical Systems · Mathematics 2014-01-06 Flank D. M. Bezerra , Miriam da S. Pereira , Severino H. da Silva

The authors consider non-autonomous dynamical behavior of wave-type evolutionary equations with nonlinear damping and critical nonlinearity. These type of waves equations are formulated as non-autonomous dynamical systems (namely,…

Dynamical Systems · Mathematics 2009-11-11 Chunyou Sun , Daomin Cao , Jinqiao Duan

Wilson-Cowan and Amari-type models capture nonlinear neural population dynamics, providing a fundamental framework for modeling how sensory and other exogenous inputs shape activity in neural tissue. We study the controllability properties…

Optimization and Control · Mathematics 2025-10-28 Cyprien Tamekue , ShiNung Ching

In this paper we consider a nonlocal evolution problem and obtain by a scaling method the first term in the asymptotic behavior of the solutions. The method employed treats in different way the smooth and the rough part of the solution.

Analysis of PDEs · Mathematics 2012-10-30 Tatiana I. Ignat

In this paper, we obtain some stability results of (abstract) dissipative evolution equations with a nonautonomous and nonlinear damping using the exponential stability of the retrograde problem with a linear and autonomous feedback and a…

Analysis of PDEs · Mathematics 2021-10-22 Serge Nicaise

Global dynamics of nonautonomous diffusive Hindmarsh-Rose equations on a three-dimensional bounded domain in neurodynamics is investigated. The existence of a pullback attractor is proved through uniform estimates showing the pullback…

Analysis of PDEs · Mathematics 2019-09-10 Chi Phan , Yuncheng You

In this paper, we study the asymptotic behavior of solution to a non-autonomous diffusion equations with delay containing some hereditary characteristics and nonlocal diffusion in time-dependent space $C_{\mathcal{H}_{t}(\Omega)}$. When the…

Analysis of PDEs · Mathematics 2022-10-20 Yuming Qin , Bin Yang

In this article we study the asymptotic behavior of solutions, in sense of global pullback attractors, of the evolution system $$ \begin{cases} u_{tt} +\eta\Delta^2 u+a(t)\Delta\theta=f(t,u), & t>\tau,\ x\in\Omega,\\ \theta_t-\kappa\Delta…

Analysis of PDEs · Mathematics 2016-09-05 Flank D. M. Bezerra , Vera L. Carbone , Marcelo J. D. Nascimento , Karina Schiabel

We study the asymptotic behavior of the trajectory of a nonautonomous evolution equation governed by a quasi-nonexpansive operator in Hilbert spaces. We prove the weak convergence of the trajectory to a fixed point of the operator by…

Optimization and Control · Mathematics 2020-09-08 Ming Zhu , Rong Hu , Ya-Ping Fang

In a Hilbert setting $H$, we study the asymptotic behavior of the trajectories of nonautonomous evolution equations $\dot x(t)+A_t(x(t))\ni 0$, where for each $t\geq 0$, $A_t:H\tto H$ denotes a maximal monotone operator. We provide general…

Optimization and Control · Mathematics 2016-01-06 Hedy Attouch , Alexandre Cabot , Marc-Olivier Czarnecki

We study a stochastic process describing the continuous time evolution of the membrane potentials of finite system of neurons in the absence of external stimuli. The values of the membrane potentials evolve under the effect of {\it chemical…

Probability · Mathematics 2017-07-14 Aline Duarte , Guilherme Ost

In this paper we consider a $2m$-th order non autonomous quasilinear parabolic equation. Under suitable conditions of growth and regularity for the nonlinear functions present in the model, we prove a result of existence and…

Analysis of PDEs · Mathematics 2025-07-29 Flank D. M. Bezerra , Silvia Sastre-Gómez

We show how the approach of Yosida approximation of the derivative serves to obtain new results for evolution systems. Using this method we obtain multivalued time dependent perturbation results. Additionally, translation invariant…

Dynamical Systems · Mathematics 2013-12-11 Josef Kreulich

We study the power spectrum of a space-time dependent neural field which describes the average membrane potential of neurons in a single layer. This neural field is modelled by a dissipative integro-differential equation, the so-called…

Neurons and Cognition · Quantitative Biology 2019-01-29 Luca Salasnich

In this paper we develop the notion of ergodicity to include functions dominated by a weight $w$. Such functions have polynomial means and include, amongst many others, the $w$-almost periodic functions. This enables us to describe the…

Functional Analysis · Mathematics 2012-06-22 Bolis Basit , A. J. Pryde

Habituation - a phenomenon in which a dynamical system exhibits a diminishing response to repeated stimulations that eventually recovers when the stimulus is withheld - is universally observed in living systems from animals to unicellular…

Adaptation and Self-Organizing Systems · Physics 2024-07-26 Matthew Smart , Stanislav Y. Shvartsman , Martin Mönnigmann

We address the problem of reinforcement learning in which observations may exhibit an arbitrary form of stochastic dependence on past observations and actions. The task for an agent is to attain the best possible asymptotic reward where the…

Machine Learning · Computer Science 2007-05-23 Daniil Ryabko , Marcus Hutter
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