The relationship between synchronization among neuronal populations and their mean activity levels
The relationship between synchronization among neuronal populations and their mean activity levels
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DOI：
10.1162/089976699300016287
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发表时间：
1999-08-15
影响因子：
2.9
通讯作者：
Friston, KJ
中科院分区：
文献类型：
--
作者：
Chawla, D;Lumer, ED;Friston, KJ
在过去的十年里，大脑中同步动力学的重要性已经从经验和理论两个角度显现出来。振荡或非振荡性质的快速动态同步交互可以构成一种作为特征绑定和知觉合成基础的时间编码形式。神经元群体之间的同步与群体放电速率之间的关系解决了两个重要问题：神经元相互作用的速率编码和同步编码模型之间的区别，以及群体活动的经验测量(如神经成像所使用的测量)对同步变化的敏感程度。我们使用生物学上可信的模拟研究了平均种群活动和同步化之间的关系。在本文中，我们主要研究连续平稳动力学。(在即将发表的一篇名为Chawla的配套文章中，我们使用刺激诱发的瞬变来解决同样的问题。)通过操纵外部输入、内部噪声、突触效能、外部连接密度、突触后机制的电压敏感性、神经元数量和种群内的板层结构等参数，我们能够在各种模拟神经元结构下引入平均活动和同步性的变化。对模拟的棘波序列和局域场势的分析表明，在模型参数空间的几乎每一个区域，平均活动和同步都是紧密耦合的。当有效膜时间常数因活性增加而降低时，这种偶联似乎是通过增加同步增益来实现的。这些观察表明，在我们的模型中隐含的假设下，具有相互关联的神经系统中的速率编码和同步编码是关于同一潜在动态的两个视角。这表明，在缺乏将同步变化与放电水平分离的特定机制的情况下，纯粹基于突触活动的大脑活动指数(例如，功能磁共振成像)可能也对同步耦合的变化敏感。
In the past decade the importance of synchronized dynamics in the brain has emerged from both empirical and theoretical perspectives. Fast dynamic synchronous interactions of an oscillatory or nonoscillatory nature may constitute a form of temporal coding that underlies feature binding and perceptual synthesis. The relationship between synchronization among neuronal populations and the population firing rates addresses two important issues: the distinction between rate coding and synchronization coding models of neuronal interactions and the degree to which empirical measurements of population activity, such as those employed by neuroimaging, are sensitive to changes in synchronization. We examined the relationship between mean population activity and synchronization using biologically plausible simulations. In this article, we focus on continuous stationary dynamics. (In a companion article, Chawla (forthcoming), we address the same issue using stimulus-evoked transients.) By manipulating parameters such as extrinsic input, intrinsic noise, synaptic efficacy, density of extrinsic connections, the voltage-sensitive nature of postsynaptic mechanisms, the number of neurons, and the laminar structure within the populations, we were able to introduce variations in both mean activity and synchronization under a variety of simulated neuronal architectures. Analyses of the simulated spike trains and local field potentials showed that in nearly every domain of the model's parameter space, mean activity and synchronization were tightly coupled. This coupling appears to be mediated by an increase in synchronous gain when effective membrane time constants are lowered by increased activity. These observations show that under the assumptions implicit in our models, rate coding and synchrony coding in neural systems with reciprocal interconnections are two perspectives on the same underlying dynamic. This suggests that in the absence of specific mechanisms decoupling changes in synchronization from firing levels, indexes of brain activity that are based purely on synaptic activity (e.g., functional magnetic resonance imaging) may also be sensitive to changes in synchronous coupling.