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Neuronal excitability is the phenomena that describes action potential generation due to a stimulus input. Commonly, neuronal excitability is divided into two classes: Type I and Type II, both having different properties that affect…

神经元与认知 · 定量生物学 2020-11-03 Jantine A. C. Broek , Guillaume Drion

Neuronal avalanches, measured in vitro and in vivo, exhibit a robust critical behaviour. Their temporal organization hides the presence of correlations. Here we present experimental measurements of the waiting time distribution between…

神经元与认知 · 定量生物学 2012-04-30 F. Lombardi , H. J. Herrmann , C. Perrone-Capano , D. Plenz , L. de Arcangelis

The relation between spontaneous and stimulated brain activity is a fundamental question in neuroscience, which has received wide attention in experimental studies. Recently, it has been suggested that the evoked response to external…

无序系统与神经网络 · 物理学 2023-06-16 M. K. Nandi , A. De Candia , A. Sarracino , H. J. Herrmann , L. de Arcangelis

Excitability is an attribute of life, and is a driving force in the descent of complexity. Cellular electrical activity as realized by membrane proteins that act as either channels or transporters is the basis of excitability. Electrical…

生物大分子 · 定量生物学 2009-11-05 Ashok Palaniappan , Eric Jakobsson

Neurons and networks in the cerebral cortex must operate reliably despite multiple sources of noise. To evaluate the impact of both input and output noise, we determine the robustness of single-neuron stimulus selective responses, as well…

神经元与认知 · 定量生物学 2018-01-24 Ran Rubin , L. F. Abbott , Haim Sompolinsky

Cortical neurons include many sub-cellular processes, operating at multiple timescales, which may affect their response to stimulation through non-linear and stochastic interaction with ion channels and ionic concentrations. Since new…

神经元与认知 · 定量生物学 2014-05-01 Daniel Soudry , Ron Meir

The "edge of chaos" phase transition in artificial neural networks is of renewed interest in light of recent evidence for criticality in brain dynamics. Statistical mechanics traditionally studied this transition with connectivity $k$ as…

无序系统与神经网络 · 物理学 2019-07-17 Lorenz Baumgarten , Stefan Bornholdt

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

We examine whether a single biophysical cortical circuit model can explain both spiking and perceptual variability. We consider perceptual rivalry, which provides a window into intrinsic neural processing since neural activity in some brain…

神经元与认知 · 定量生物学 2018-11-20 Benjamin P Cohen , Carson C Chow , Shashaank Vattikuti

This chapter revisits the concept of excitability, a basic system property of neurons. The focus is on excitable systems regarded as behaviors rather than dynamical systems. By this we mean open systems modulated by specific interconnection…

神经元与认知 · 定量生物学 2017-04-18 Rodolphe Sepulchre , Guillaume Drion , Alessio Franci

Here we numerically study a model of excitable media, namely, a network with occasionally quiet nodes and connection weights that vary with activity on a short-time scale. Even in the absence of stimuli, this exhibits unstable dynamics,…

无序系统与神经网络 · 物理学 2015-05-19 S. de Franciscis , J. J. Torres , J. Marro

We present experimental and theoretical arguments, at the single neuron level, suggesting that neuronal response fluctuations reflect a process that positions the neuron near a transition point that separates excitable and unexcitable…

神经元与认知 · 定量生物学 2013-12-25 Asaf Gal , Shimon Marom

The paper studies the excitability properties of a generalized FitzHugh-Nagumo model. The model differs from the purely competitive FitzHugh-Nagumo model in that it accounts for the effect of cooperative gating variables such as activation…

动力系统 · 数学 2012-04-26 Alessio Franci , Guillaume Drion , Rodolphe Sepulchre

This work proposes a two-dimensional electrophysiological model for describing neuronal responses to external electric stimuli under patch-clamped conditions. Our proposed model successfully captures the key features of the Hodgkin-Huxley…

神经元与认知 · 定量生物学 2023-12-13 Lízia Maria Gonçalves Branco , Rui Manuel Agostinho Dilão

Consistency and predictability of brain functionalities depend on reproducible activity of a single neuron. We identify a reproducible non-chaotic neuronal phase where deviations between concave response latency profiles of a single neuron…

神经元与认知 · 定量生物学 2014-05-27 Hagar Marmari , Roni Vardi , Ido Kanter

Cortical neural circuits display highly irregular spiking in individual neurons but variably sized collective firing, oscillations and critical avalanches at the population level, all of which have functional importance for information…

无序系统与神经网络 · 物理学 2021-01-08 Junhao Liang , Tianshou Zhou , Changsong Zhou

Balance of excitation and inhibition is a fundamental feature of in vivo network activity and is important for its computations. However, its presence in the neocortex of higher mammals is not well established. We investigated the dynamics…

We consider a stochastic version of an excitable system based on the Morris-Lecar model of a neuron, in which the noise originates from stochastic Sodium and Potassium ion channels opening and closing. One can analyze neural excitability in…

神经元与认知 · 定量生物学 2015-06-15 Jay M. Newby , Paul C. Bressloff , James P. Keener

We study a simple map as a minimal model of excitable cells. The map has two fast variables which mimic the behavior of class I neurons, undergoing a sub-critical Hopf bifurcation. Adding a third slow variable allows the system to present…

神经元与认知 · 定量生物学 2007-05-23 M. Copelli , M. H. R. Tragtenberg , O. Kinouchi

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
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