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相关论文: Bifurcations of Emergent Bursting in a Neuronal Ne…

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Bursting neurons are considered to be a potential cause of over-excitability and seizure susceptibility. The functional influence of these neurons in extended epileptic networks is still poorly understood. There is mounting evidence that…

神经元与认知 · 定量生物学 2016-10-07 Christian Geier , Alexander Rothkegel , Christian E. Elger , Klaus Lehnertz

`Bursting', defined as periods of high frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both \textit{in vitro} and \textit{in vivo}. It has been associated with a range of…

神经元与认知 · 定量生物学 2018-06-20 E. Cotterill , S. J. Eglen

We report on the origin of synchronized bursting dynamics in various networks of neural spiking oscillators, when a certain threshold in coupling strength is exceeded. These ensembles synchronize at relatively low coupling strength and lose…

混沌动力学 · 物理学 2007-05-23 Mikhail V. Ivanchenko , Grigory V. Osipov , Vladimir D. Shalfeev , Jurgen Kurths

Despite the fact that the phenomenon of bursting activity is important for functioning of living neural networks, the mechanisms of its origin are still not clear. In this paper, we propose a new phenomenological model that can explain the…

神经元与认知 · 定量生物学 2023-03-01 Nikita Barabash , Tatiana Levanova , Sergey Stasenko

When we look at the world around us, we see complex physical systems and emergent phenomena. Emergence occurs when a system is observed to have properties that its parts do not have on their own. These properties or behaviors emerge only…

神经元与认知 · 定量生物学 2021-05-26 Nir Lahav

Many networks of physical and biological interest are characterized by a long-range coupling mediated by a chemical which diffuses through a medium in which oscillators are embedded. We considered a one-dimensional model for this effect for…

Modeling and parameter estimation for neuronal dynamics are often challenging because many parameters can range over orders of magnitude and are difficult to measure experimentally. Moreover, selecting a suitable model complexity requires a…

动力系统 · 数学 2018-01-31 J. E. Rubin , B. Krauskopf , H. M. Osinga

A simple model that replicates the dynamics of spiking and spiking-bursting activity of real biological neurons is proposed. The model is a two-dimensional map which contains one fast and one slow variable. The mechanisms behind generation…

混沌动力学 · 物理学 2009-11-07 Nikolai F. Rulkov

Bursting is a phenomenon found in a variety of physical and biological systems. For example, in neuroscience, bursting is believed to play a key role in the way information is transferred in the nervous system. In this work, we propose a…

神经元与认知 · 定量生物学 2016-05-31 Maria Luisa Saggio , Andreas Spiegler , Christophe Bernard , Viktor K. Jirsa

The brain is a highly complex system. Most of such complexity stems from the intermingled connections between its parts, which give rise to rich dynamics and to the emergence of high-level cognitive functions. Disentangling the underlying…

神经元与认知 · 定量生物学 2023-08-14 Vito Dichio , Fabrizio De Vico Fallani

We describe a simple conductance-based model neuron that includes intra- and extra-cellular ion concentration dynamics and show that this model exhibits periodic bursting. The bursting arises as the fast spiking behavior of the neuron is…

细胞行为 · 定量生物学 2011-09-22 Ernest Barreto , John R. Cressman

Elucidating the neurophysiological mechanisms underlying neural pattern formation remains an outstanding challenge in Computational Neuroscience. In this paper, we address the issue of understanding the emergence of neural patterns by…

神经元与认知 · 定量生物学 2024-06-04 Gregory Dumont , Carmen Oana Tarniceriu

Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of…

神经元与认知 · 定量生物学 2014-08-26 Klaus Lehnertz , Gerrit Ansmann , Stephan Bialonski , Henning Dickten , Christian Geier , Stephan Porz

Living neuronal networks in dissociated neuronal cultures are widely known for their ability to generate highly robust spatiotemporal activity patterns in various experimental conditions. These include neuronal avalanches satisfying the…

适应与自组织系统 · 物理学 2022-05-17 I. Y. Tyukin , D. Iudin , F. Iudin , T. Tyukina , V. Kazantsev , I. Mukhina , A. N. Gorban

A large repertoire of spatiotemporal activity patterns in the brain is the basis for adaptive behaviour. Understanding the mechanism by which the brain's hundred billion neurons and hundred trillion synapses manage to produce such a range…

神经元与认知 · 定量生物学 2010-10-14 Dante R. Chialvo

Networks of model neurons with balanced recurrent excitation and inhibition produce irregular and asynchronous spiking activity. We extend the analysis of balanced networks to include the known dependence of connection probability on the…

神经元与认知 · 定量生物学 2014-06-02 Robert Rosenbaum , Brent Doiron

Understanding the relation between cortical neuronal network structure and neuronal activity is a fundamental unresolved question in neuroscience, with implications to our understanding of the mechanism by which neuronal networks evolve…

This paper proposes a framework for the biological learning mechanism as a general learning system. The proposal is as follows. The bursting and tonic modes of firing patterns found in many neuron types in the brain correspond to two…

神经与进化计算 · 计算机科学 2018-12-27 Hin Wai Lui

Cultures of neurons grown on multi-electrode arrays have become a common experimental preparation for investigating developing neural networks. Experiment and simulation have shown that these developing networks eventually exhibit bursting…

神经元与认知 · 定量生物学 2026-02-27 Michael Stiber , Natalie Gonzales , Jewel YunHsuan Lee

Recurrent neural networks are powerful tools for understanding and modeling computation and representation by populations of neurons. Continuous-variable or "rate" model networks have been analyzed and applied extensively for these…

神经元与认知 · 定量生物学 2016-01-29 Brian DePasquale , Mark M. Churchland , L. F. Abbott
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