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Information transmission is a key element for information processing in the brain. A number of mechanisms have been proposed for transferring volleys of spikes between layers of a feedforward neural circuit. Many of these mechanisms use…

神经元与认知 · 定量生物学 2015-12-03 Cong Wang , Zhuocheng Xiao , Zhou Wang , Andrew T. Sornborger , Louis Tao

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

We present a complete mean field theory for a balanced state of a simple model of an orientation hypercolumn. The theory is complemented by a description of a numerical procedure for solving the mean-field equations quantitatively. With our…

神经元与认知 · 定量生物学 2007-05-23 A. Lerchner , G. Sterner , J. Hertz , M. Ahmadi

This work reports a transfer function-based approach to characterizing the operation of single neuronal cells in terms of the instantaneous frequency of the input and output signals. We adopt the leaky integrate-and-fire model. The transfer…

神经元与认知 · 定量生物学 2019-10-23 Felipe L. Gewers , Luciano da F. Costa

We consider a noise driven network of integrate-and-fire neurons. The network evolves as result of the activities of the neurons following spike-timing-dependent plasticity rules. We apply a self-consistent mean-field theory to the system…

神经元与认知 · 定量生物学 2010-02-05 Chun-Chung Chen , David Jasnow

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 neural dynamics generating sensory, motor, and cognitive functions are commonly understood through field theories for neural population activity. Classic neural field theories are derived from highly simplified models of individual…

神经元与认知 · 定量生物学 2023-11-21 Gabriel Koch Ocker

We review the use of mean field theory for describing the dynamics of dense, randomly connected cortical circuits. For a simple network of excitatory and inhibitory leaky integrate-and-fire neurons, we can show how the firing irregularity,…

神经元与认知 · 定量生物学 2007-05-23 John Hertz , Alexander Lerchner , Mandana Ahmadi

Mean field theory is a device to analyze the collective behavior of a dynamical system comprising many interacting particles. The theory allows to reduce the behavior of the system to the properties of a handful of parameters. In neural…

神经元与认知 · 定量生物学 2022-06-10 Giancarlo La Camera

In this manuscript we analyze the collective behavior of mean-field limits of large-scale, spatially extended stochastic neuronal networks with delays. Rigorously, the asymptotic regime of such systems is characterized by a very intricate…

动力系统 · 数学 2017-02-21 Jonathan Touboul

The Integrate-and-Fire model is a Fokker-Planck equation arising in neuroscience. It describes the evolution of the probability density of the neuronal membrane potential and fitting has shown that the inclusion of a em superlinear drift…

偏微分方程分析 · 数学 2026-01-28 Benoît Perthame , Clément Rieutord , Delphine Salort

A recent study on the effect of colored driving noise on the escape from a metastable state derives an analytic expression of the transfer function of the leaky integrate-and-fire neuron model subject to colored noise. Here we present an…

神经元与认知 · 定量生物学 2015-10-14 Jannis Schuecker , Markus Diesmann , Moritz Helias

In recurrent networks of leaky integrate-and-fire (LIF) neurons, mean-field theory has proven successful in describing various statistical properties of neuronal activity at equilibrium, such as firing rate distributions. Mean-field theory…

神经元与认知 · 定量生物学 2023-11-10 Marina Vegué , Antoine Allard , Patrick Desrosiers

A general approach to consider spatially extended stochastic systems with correlations between additive and multiplicative noises subject to nonlinear damping is developed. Within modified cumulant expansion method, we derive an effective…

统计力学 · 物理学 2009-11-10 A. I. Olemskoi , D. O. Kharchenko , I. A. Knyaz'

Strong, correlated noise in recurrent neural circuits often passes through nonlinear transfer functions, complicating dynamical mean-field analyses of complex phenomena such as transients and bifurcations. We introduce a method that…

神经元与认知 · 定量生物学 2026-01-23 Shoshana Chipman , Brent Doiron

We consider a pair of stochastic integrate and fire neurons receiving correlated stochastic inputs. The evolution of this system can be described by the corresponding Fokker-Planck equation with non-trivial boundary conditions resulting…

神经元与认知 · 定量生物学 2010-12-14 Robert Rosenbaum , Jianfu Ma , Fabien Marpeau , Aditya Barua , Kresimir Josic

Neural network dynamics emerge from the interaction of spiking cells. One way to formulate the problem is through a theoretical framework inspired by ideas coming from statistical physics, the so-called mean-field theory. In this document,…

偏微分方程分析 · 数学 2020-11-11 Grégory Dumont , Pierre Gabriel

We derive the mean-field equations arising as the limit of a network of interacting spiking neurons, as the number of neurons goes to infinity. The neurons belong to a fixed number of populations and are represented either by the…

神经元与认知 · 定量生物学 2016-11-25 Javier Baladron , Diego Fasoli , Olivier Faugeras , Jonathan Touboul

In a generic neuron model, we present the linear response theory for the firing rate in response to both time dependent input currents and noise amplitudes. In both cases the signal transmission is strongly attenuated for frequencies above…

生物物理 · 物理学 2007-05-23 Bjoern Naundorf , Theo Geisel , Fred Wolf

We study non-locally coupled noisy integrate-and-fire neurons with the Fokker-Planck equation. A propagating pulse state and a wavy state appear as a phase transition from an asynchronous state. We also find a solution in which traveling…

适应与自组织系统 · 物理学 2009-11-10 H. Sakaguchi
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