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The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic…

神经元与认知 · 定量生物学 2017-07-20 Moritz Augustin , Josef Ladenbauer , Fabian Baumann , Klaus Obermayer

In this article we present the modeling of bi-stability view problems described by the activity or firing rates of two interacting population of neurons. Starting from the study of a complex system, the sys-tem of stochastic differential…

偏微分方程分析 · 数学 2014-11-27 S Mancini

Starting from a spectral expansion of the Fokker-Plank equation for the membrane potential density in a network of spiking neurons, a low-dimensional dynamics of the collective firing rate is derived. As a result a $n$-order ordinary…

神经元与认知 · 定量生物学 2016-09-29 Maurizio Mattia

The issue of the relaxation to equilibrium has been at the core of the kinetic theory of rarefied gas dynamics. In the paper, we introduce the Deep Neural Network (DNN) approximated solutions to the kinetic Fokker-Planck equation in a…

数值分析 · 数学 2020-07-15 Hyung Ju Hwang , Jin Woo Jang , Hyeontae Jo , Jae Yong Lee

In neuroscience, the distribution of a decision time is modelled by means of a one-dimensional Fokker--Planck equation with time-dependent boundaries and space-time-dependent drift. Efficient approximation of the solution to this equation…

数值分析 · 数学 2023-02-08 Udo Boehm , Sonja Cox , Gregor Gantner , Rob Stevenson

Populations of spiking neuron models have densities of their microscopic variables (e.g., single-cell membrane potentials) whose evolution fully capture the collective dynamics of biological networks, even outside equilibrium. Despite its…

神经元与认知 · 定量生物学 2021-11-08 Gianni V. Vinci , Maurizio Mattia

Stochastic differential equations play an important role in various applications when modeling systems that have either random perturbations or chaotic dynamics at faster time scales. The time evolution of the probability distribution of a…

数值分析 · 数学 2022-11-11 Yao Li , Caleb Meredith

Firing rate fluctuations in neural populations are observed experimentally over multiple time scales, in single neurons, across trials when elicited by stimuli, and across populations. In this work, we examine how firing rate fluctuations…

神经元与认知 · 定量生物学 2026-05-15 Wilten Nicola , Sue Ann Campbell

Many systems of partial differential equations have been proposed as simplified representations of complex collective behaviours in large networks of neurons. In this survey, we briefly discuss their derivations and then review the…

偏微分方程分析 · 数学 2025-01-13 José A Carrillo , Pierre Roux

Providing an analytical treatment to the stochastic feature of neurons' dynamics is one of the current biggest challenges in mathematical biology. The noisy leaky integrate-and-fire model and its associated Fokker-Planck equation are…

神经元与认知 · 定量生物学 2015-12-14 Grégory Dumont , Jacques Henry , Carmen Oana Tarniceriu

In this paper we study the dynamics of a fast-slow Fokker-Planck partial differential equation (PDE) viewed as the evolution equation for the density of a multiscale planar stochastic differential equation (SDE). Our key focus is on the…

偏微分方程分析 · 数学 2025-02-03 Christian Kuehn , Jan-Eric Sulzbach

The human brain is a complex dynamical system which displays a wide range of macroscopic and mesoscopic patterns of neural activity, whose mechanistic origin remains poorly understood. Whole-brain modelling allows us to explore candidate…

神经元与认知 · 定量生物学 2025-04-25 Cristiana Dimulescu , Ronja Strömsdörfer , Agnes Flöel , Klaus Obermayer

In this work, the primary goal is to establish rigorous connection between the Fokker-Planck equation of neural networks with its microscopic model: the diffusion-jump stochastic process that captures the mean field behavior of collections…

偏微分方程分析 · 数学 2021-11-01 Jian-guo Liu , Ziheng Wang , Yuan Zhang , Zhennan Zhou

The dynamical evolution of a neural network during training has been an incredibly fascinating subject of study. First principal derivation of generic evolution of variables in statistical physics systems has proved useful when used to…

机器学习 · 计算机科学 2025-06-06 Wei Bu , Uri Kol , Ziming Liu

The Nonlinear Noisy Leaky Integrate and Fire neuronal models are mathematical models that describe the activity of neural networks. These models have been studied at a microscopic level, using Stochastic Differential Equations, and at a…

神经元与认知 · 定量生物学 2020-11-12 María J. Cáceres , Alejandro Ramos-Lora

We begin by demonstrating that the neuronal state equation from Dynamic Causal Modelling takes on the form of the discretized Fokker-Planck equation upon the inclusion of local activity gradients within a network. Using the Jacobian of this…

定量方法 · 定量生物学 2019-11-27 Erik D. Fagerholm , Rosalyn J. Moran , Robert Leech

We obtain equilibration rates for a one-dimensional nonlocal Fokker-Planck equation with time-dependent diffusion coefficient and drift, modeling the relaxation of a large swarm of robots, feeling each other in terms of their distance,…

偏微分方程分析 · 数学 2023-06-06 Ferdinando Auricchio , Giuseppe Toscani , Mattia Zanella

Solving the Fokker-Planck equation for high-dimensional complex dynamical systems remains a pivotal yet challenging task due to the intractability of analytical solutions and the limitations of traditional numerical methods. In this work,…

机器学习 · 计算机科学 2025-09-04 Naoufal El Bekri , Lucas Drumetz , Franck Vermet

The Fokker-Planck (FP) equation is a linear partial differential equation which governs the temporal and spatial evolution of the probability density function (PDF) associated with the response of stochastic dynamical systems. An exact…

计算物理 · 物理学 2023-10-02 Hussam Alhussein , Mohammed Khasawneh , Mohammed F. Daqaq

We consider a population dynamics model coupling cell growth to a diffusion in the space of metabolic phenotypes as it can be obtained from realistic constraints-based modelling. In the asymptotic regime of slow diffusion, that coincides…

种群与进化 · 定量生物学 2017-02-17 Daniele De Martino , Davide Masoero
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