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具有阈值疲劳的漏电积分发放模型中的峰间间隔相关性、记忆、适应性和不应期

Interspike interval correlations, memory, adaptation, and refractoriness in a leaky integrate-and-fire model with threshold fatigue.

作者信息

Chacron Maurice J, Pakdaman Khashayar, Longtin André

机构信息

Department of Physics, University of Ottawa, Ottawa, Canada K1N 6N5.

出版信息

Neural Comput. 2003 Feb;15(2):253-78. doi: 10.1162/089976603762552915.

DOI:10.1162/089976603762552915
PMID:12590807
Abstract

Neuronal adaptation as well as interdischarge interval correlations have been shown to be functionally important properties of physiological neurons. We explore the dynamics of a modified leaky integrate-and-fire (LIF) neuron, referred to as the LIF with threshold fatigue, and show that it reproduces these properties. In this model, the postdischarge threshold reset depends on the preceding sequence of discharge times. We show that in response to various classes of stimuli, namely, constant currents, step currents, white gaussian noise, and sinusoidal currents, the model exhibits new behavior compared with the standard LIF neuron. More precisely, (1) step currents lead to adaptation, that is, a progressive decrease of the discharge rate following the stimulus onset, while in the standard LIF, no such patterns are possible; (2) a saturation in the firing rate occurs in certain regimes, a behavior not seen in the LIF neuron; (3) interspike intervals of the noise-driven modified LIF under constant current are correlated in a way reminiscent of experimental observations, while those of the standard LIF are independent of one another; (4) the magnitude of the correlation coefficients decreases as a function of noise intensity; and (5) the dynamics of the sinusoidally forced modified LIF are described by iterates of an annulus map, an extension to the circle map dynamics displayed by the LIF model. Under certain conditions, this map can give rise to sensitivity to initial conditions and thus chaotic behavior.

摘要

神经元适应性以及放电间隔相关性已被证明是生理神经元的重要功能特性。我们研究了一种改进的漏电整合发放(LIF)神经元的动力学,即具有阈值疲劳的LIF神经元,并表明它能再现这些特性。在这个模型中,放电后阈值重置取决于先前的放电时间序列。我们表明,与标准LIF神经元相比,该模型在响应各种类型的刺激时,即恒定电流、阶跃电流、白高斯噪声和正弦电流时,表现出了新的行为。更确切地说,(1)阶跃电流会导致适应性,即刺激开始后放电率逐渐降低,而在标准LIF中,不可能出现这种模式;(2)在某些情况下会出现放电率饱和,这是LIF神经元中未观察到的行为;(3)在恒定电流下,噪声驱动的改进LIF的峰峰间隔以一种让人联想到实验观察结果的方式相关,而标准LIF的峰峰间隔则相互独立;(4)相关系数的大小随噪声强度的增加而减小;(5)正弦驱动的改进LIF的动力学由环形映射的迭代描述,这是对LIF模型所展示的圆映射动力学的扩展。在某些条件下,这种映射会导致对初始条件的敏感性,从而产生混沌行为。

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