Cellularly-driven differences in network synchronization propensity are differentially modulated by firing frequency.
Cellularly-driven differences in network synchronization propensity are differentially modulated by firing frequency.
复制标题
DOI：
10.1371/journal.pcbi.1002062
复制
发表时间：
2011-05
影响因子：
4.3
通讯作者：
Zochowski M
中科院分区：
文献类型：
--
作者：
Fink CG;Booth V;Zochowski M
神经元网络时空模式的形成取决于细胞和网络同步特性之间的相互作用。神经元相位响应曲线（PRC）是一种实验可获得的测量方法，表征细胞对小扰动的反应，可以作为细胞同步倾向的指标。已经确定了神经元的两大类PRCs: I型，其中小的兴奋性扰动只诱导放电提前，II型，其中小的兴奋性扰动可以诱导放电提前和延迟。有趣的是，神经元PRCs通常随着峰值频率的增加而衰减，II型PRCs通常表现出相延迟区比相提前区更大的衰减。我们发现这种现象是由活跃离子电流的时间常数和峰间间隔的相互作用引起的。因此，与具有II型PRCs的神经元组成的兴奋性网络相比，具有I型PRCs的神经元组成的兴奋性网络对频率调制的反应非常不同。具体而言，频率增加导致II型神经元网络的同步性急剧下降，而频率增加仅对I型神经元网络的同步性产生最小影响。这些结果在用Morris-Lecar模型对两种类型的神经元进行一般建模的网络中得到了证明，也在由基于hodgkin - huxley模型的皮质锥体细胞组成的网络中得到了证明，其中乙酰胆碱的模拟效应改变了PRC类型。这些结果适用于不同的网络结构、突触强度和驱动神经元活动的模式，表明I型和II型兴奋网络可能表现出两种不同的信息处理模式。大脑中神经元的同步放电与许多认知功能有关，如识别面孔、辨别气味和协调运动。因此，了解神经元网络的哪些特性促进神经放电的同步性是很重要的。通常用于确定单个神经元对网络同步的贡献的一种测量称为相位响应曲线（PRC）。prc描述了神经元放电的时间如何根据输入（如突触信号）何时被神经元接收而变化。PRCs的一个特征以前没有被很好地理解，即它们随着神经元的放电频率被调制而发生显著变化。这种效应具有潜在的意义，因为认知功能通常与大脑中网络活动的特定频率有关。我们通过计算表明，prc的频率依赖性可以通过离子膜电流相对于脉冲发射之间的时间来解释。我们的模拟还表明，神经元prc的频率依赖性导致网络同步的频率依赖性变化，这种变化对于不同的神经元类型可能是不同的。这些结果进一步加深了我们对同步如何在大脑中产生以支持各种认知功能的理解。
Spatiotemporal pattern formation in neuronal networks depends on the interplay between cellular and network synchronization properties. The neuronal phase response curve (PRC) is an experimentally obtainable measure that characterizes the cellular response to small perturbations, and can serve as an indicator of cellular propensity for synchronization. Two broad classes of PRCs have been identified for neurons: Type I, in which small excitatory perturbations induce only advances in firing, and Type II, in which small excitatory perturbations can induce both advances and delays in firing. Interestingly, neuronal PRCs are usually attenuated with increased spiking frequency, and Type II PRCs typically exhibit a greater attenuation of the phase delay region than of the phase advance region. We found that this phenomenon arises from an interplay between the time constants of active ionic currents and the interspike interval. As a result, excitatory networks consisting of neurons with Type I PRCs responded very differently to frequency modulation compared to excitatory networks composed of neurons with Type II PRCs. Specifically, increased frequency induced a sharp decrease in synchrony of networks of Type II neurons, while frequency increases only minimally affected synchrony in networks of Type I neurons. These results are demonstrated in networks in which both types of neurons were modeled generically with the Morris-Lecar model, as well as in networks consisting of Hodgkin-Huxley-based model cortical pyramidal cells in which simulated effects of acetylcholine changed PRC type. These results are robust to different network structures, synaptic strengths and modes of driving neuronal activity, and they indicate that Type I and Type II excitatory networks may display two distinct modes of processing information. Synchronization of the firing of neurons in the brain is related to many cognitive functions, such as recognizing faces, discriminating odors, and coordinating movement. It is therefore important to understand what properties of neuronal networks promote synchrony of neural firing. One measure that is often used to determine the contribution of individual neurons to network synchrony is called the phase response curve (PRC). PRCs describe how the timing of neuronal firing changes depending on when input, such as a synaptic signal, is received by the neuron. A characteristic of PRCs that has previously not been well understood is that they change dramatically as the neuron's firing frequency is modulated. This effect carries potential significance, since cognitive functions are often associated with specific frequencies of network activity in the brain. We showed computationally that the frequency dependence of PRCs can be explained by the relative timing of ionic membrane currents with respect to the time between spike firings. Our simulations also showed that the frequency dependence of neuronal PRCs leads to frequency-dependent changes in network synchronization that can be different for different neuron types. These results further our understanding of how synchronization is generated in the brain to support various cognitive functions.
登录
查看更多内容
影响因子：
3.7
作者：
Stiefel KM;Gutkin BS;Sejnowski TJ
通讯作者：
Sejnowski TJ
影响因子：
3.4
作者：
Tateno, T.;Robinson, H. P. C.
通讯作者：
Robinson, H. P. C.
影响因子：
2.9
作者：
Ermentrout, B
通讯作者：
Ermentrout, B
影响因子：
4
作者：
Mormann, F;Lehnertz, K;Elger, CE
通讯作者：
Elger, CE
DOI：
10.1523/jneurosci.5218-08.2009
发表时间：
2009-02-11
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Bogaard A;Parent J;Zochowski M;Booth V
通讯作者：
Booth V