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相关论文: Modeling rhythmic patterns in the hippocampus

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This article investigates the emergence of phase synchronization in a network of randomly connected neurons by chemical synapses. The study uses the classic Hodgkin-Huxley model to simulate the neuronal dynamics under the action of a train…

We consider a heterogeneous, globally coupled population of excitatory quadratic integrate-and-fire neurons with excitability adaptation due to a metabolic feedback associated with ketogenic diet, a form of therapy for epilepsy. Bifurcation…

适应与自组织系统 · 物理学 2024-05-29 Sebastian Eydam , Igor Franović , Louis Kang

In neural circuits, synaptic strengths influence neuronal activity by shaping network dynamics, and neuronal activity influences synaptic strengths through activity-dependent plasticity. Motivated by this fact, we study a recurrent-network…

神经元与认知 · 定量生物学 2024-01-12 David G. Clark , L. F. Abbott

The dynamics of three mutually coupled cortical neurons with time delays in the coupling are explored numerically and analytically. The neurons are coupled in a line, with the middle neuron sending a somewhat stronger projection to the…

混沌动力学 · 物理学 2011-01-25 Alexandra S. Landsman , Ira B. Schwartz

Serotonergic, noradrenergic and dopaminergic brainstem (including midbrain) neurons, often exhibit spontaneous and fairly regular spiking with frequencies of order a few Hz, though dopaminergic and noradrenergic neurons only exhibit such…

神经元与认知 · 定量生物学 2017-04-18 Henry C. Tuckwell , Ying Zhou , Nicholas J. Penington

In the brain, coherent neuronal activities often appear simultaneously in multiple frequency bands, e.g., as combinations of alpha (8-12 Hz), beta (12.5-30 Hz), gamma (30-120 Hz) oscillations, among others. These rhythms are believed to…

神经元与认知 · 定量生物学 2023-04-26 Tianyi Wu , Yuhang Cai , Ruilin Zhang , Zhongyi Wang , Louis Tao , Zhuo-Cheng Xiao

In computer simulations of spiking neural networks, often it is assumed that every two neurons of the network are connected by a probability of 2\%, 20\% of neurons are inhibitory and 80\% are excitatory. These common values are based on…

神经元与认知 · 定量生物学 2015-03-06 Hamed Seyed-allaei

Recordings from area V4 of monkeys have revealed that when the focus of attention is on a visual stimulus within the receptive field of a cortical neuron, two distinct changes can occur: The firing rate of the neuron can change and there…

神经元与认知 · 定量生物学 2007-05-23 Paul H. E. Tiesinga , Jean-Marc Fellous , Emilio Salinas , Jorge V. Jose , Terrence J. Sejnowski

The brain is composed of complex networks of interacting neurons that express considerable heterogeneity in their physiology and spiking characteristics. How does neural heterogeneity affect macroscopic neural dynamics and how does it…

神经元与认知 · 定量生物学 2022-06-20 Richard Gast , Sara A. Solla , Ann Kennedy

We present a mathematical analysis of a networks with Integrate-and-Fire neurons and adaptive conductances. Taking into account the realistic fact that the spike time is only known within some \textit{finite} precision, we propose a model…

生物物理 · 物理学 2010-11-09 B. Cessac , T. Vieville

Low-dimensional yet rich dynamics often emerge in the brain. Examples include oscillations and chaotic dynamics during sleep, epilepsy, and voluntary movement. However, a general mechanism for the emergence of low dimensional dynamics…

神经元与认知 · 定量生物学 2018-08-29 Wilten Nicola , Peter Hellyer , Sue Ann Campbell , Claudia Clopath

We study the dynamics of a low-dimensional system of coupled model neurons as a step towards understanding the vastly complex network of neurons in the brain. We analyze the bifurcation structure of a system of two model neurons with…

动力系统 · 数学 2019-03-27 Elizabeth N. Davison , Zahra Aminzare , Biswadip Dey , Naomi Ehrich Leonard

We derive rigorous results describing the asymptotic dynamics of a discrete time model of spiking neurons introduced in \cite{BMS}. Using symbolic dynamic techniques we show how the dynamics of membrane potential has a one to one…

动力系统 · 数学 2008-02-12 B. Cessac

Inhibitory neurons play a crucial role in maintaining persistent neuronal activity. Although connected extensively through electrical synapses (gap-junctions), these neurons also exhibit interactions through chemical synapses in certain…

神经元与认知 · 定量生物学 2021-04-08 R. Janaki , A. S. Vytheeswaran

We propose a hierarchically modular, dynamical neural network model whose architecture minimizes a specifically designed energy function and defines its temporal characteristics. The model has an internal and an external space that are…

神经元与认知 · 定量生物学 2026-04-16 Kazuyoshi Tsutsumi , Ernst Niebur

We study the stochastic FitzHugh-Nagumo equations, modelling the dynamics of neuronal action potentials, in parameter regimes characterised by mixed-mode oscillations. The interspike time interval is related to the random number of…

概率论 · 数学 2012-07-10 Nils Berglund , Damien Landon

We discuss the synchronization of coupled neurons which are modelled as FitzHugh-Nagumo systems. As smallest entity in a larger network, we focus on two diffusively coupled subsystems, which can be interpreted as two mutually interacting…

混沌动力学 · 物理学 2008-09-05 Philipp Hoevel , Markus A. Dahlem , Eckehard Schoell

Neuronal heterogeneity, characterized by the presence of a multitude of spiking neuronal patterns, is a widespread phenomenon throughout the nervous system. In particular, the brain exhibits strong variability among inhibitory neurons.…

神经元与认知 · 定量生物学 2024-12-02 Katiele V. P. Brito , Joana M. G. L. Silva , Claudio R. Mirasso , Fernanda S. Matias

We study the dynamics of two neuronal populations weakly and mutually coupled in a multiplexed ring configuration. We simulate the neuronal activity with the stochastic FitzHugh-Nagumo (FHN) model. The two neuronal populations perceive…

适应与自组织系统 · 物理学 2021-03-17 M. Masoliver , C. Masoller , A. Zakharova

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