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皮质γ振荡的输入依赖频率调制塑造空间同步并实现相位编码。

Input-dependent frequency modulation of cortical gamma oscillations shapes spatial synchronization and enables phase coding.

作者信息

Lowet Eric, Roberts Mark, Hadjipapas Avgis, Peter Alina, van der Eerden Jan, De Weerd Peter

机构信息

Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.

University of Nicosia Medical School, University of Nicosia, Cyprus; St George's University of London, London, United Kingdom.

出版信息

PLoS Comput Biol. 2015 Feb 13;11(2):e1004072. doi: 10.1371/journal.pcbi.1004072. eCollection 2015 Feb.

DOI:10.1371/journal.pcbi.1004072
PMID:25679780
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4334551/
Abstract

Fine-scale temporal organization of cortical activity in the gamma range (∼25-80Hz) may play a significant role in information processing, for example by neural grouping ('binding') and phase coding. Recent experimental studies have shown that the precise frequency of gamma oscillations varies with input drive (e.g. visual contrast) and that it can differ among nearby cortical locations. This has challenged theories assuming widespread gamma synchronization at a fixed common frequency. In the present study, we investigated which principles govern gamma synchronization in the presence of input-dependent frequency modulations and whether they are detrimental for meaningful input-dependent gamma-mediated temporal organization. To this aim, we constructed a biophysically realistic excitatory-inhibitory network able to express different oscillation frequencies at nearby spatial locations. Similarly to cortical networks, the model was topographically organized with spatially local connectivity and spatially-varying input drive. We analyzed gamma synchronization with respect to phase-locking, phase-relations and frequency differences, and quantified the stimulus-related information represented by gamma phase and frequency. By stepwise simplification of our models, we found that the gamma-mediated temporal organization could be reduced to basic synchronization principles of weakly coupled oscillators, where input drive determines the intrinsic (natural) frequency of oscillators. The gamma phase-locking, the precise phase relation and the emergent (measurable) frequencies were determined by two principal factors: the detuning (intrinsic frequency difference, i.e. local input difference) and the coupling strength. In addition to frequency coding, gamma phase contained complementary stimulus information. Crucially, the phase code reflected input differences, but not the absolute input level. This property of relative input-to-phase conversion, contrasting with latency codes or slower oscillation phase codes, may resolve conflicting experimental observations on gamma phase coding. Our modeling results offer clear testable experimental predictions. We conclude that input-dependency of gamma frequencies could be essential rather than detrimental for meaningful gamma-mediated temporal organization of cortical activity.

摘要

伽马波段(约25 - 80赫兹)的精细时间组织可能在信息处理中发挥重要作用,例如通过神经分组(“绑定”)和相位编码。最近的实验研究表明,伽马振荡的精确频率会随输入驱动(如视觉对比度)而变化,并且在附近的皮质位置可能有所不同。这对假设在固定共同频率下广泛存在伽马同步的理论提出了挑战。在本研究中,我们调查了在存在输入依赖频率调制的情况下,哪些原则支配伽马同步,以及它们是否对有意义的输入依赖伽马介导的时间组织有害。为此,我们构建了一个具有生物物理真实性的兴奋性 - 抑制性网络,该网络能够在附近空间位置表达不同的振荡频率。与皮质网络类似,该模型在地形上进行了组织,具有空间局部连接性和空间变化的输入驱动。我们分析了关于锁相、相位关系和频率差异的伽马同步,并量化了由伽马相位和频率表示的与刺激相关的信息。通过逐步简化我们的模型,我们发现伽马介导的时间组织可以简化为弱耦合振荡器的基本同步原则,其中输入驱动决定振荡器的固有(自然)频率。伽马锁相、精确的相位关系和出现的(可测量的)频率由两个主要因素决定:失谐(固有频率差异,即局部输入差异)和耦合强度。除了频率编码外,伽马相位还包含互补的刺激信息。至关重要的是,相位编码反映了输入差异,而不是绝对输入水平。这种相对输入到相位转换的特性,与潜伏期编码或较慢振荡相位编码形成对比,可能解决关于伽马相位编码的相互矛盾的实验观察结果。我们的建模结果提供了明确的可测试实验预测。我们得出结论,伽马频率的输入依赖性对于有意义的伽马介导的皮质活动时间组织可能是至关重要的,而不是有害的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/3a60cae798aa/pcbi.1004072.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/2d8d0f8ab860/pcbi.1004072.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/98833489a5b5/pcbi.1004072.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/54d660ca350e/pcbi.1004072.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/432ffaaecc4b/pcbi.1004072.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/286f4efbdf08/pcbi.1004072.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/8d49453a0ad4/pcbi.1004072.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/df14c00a64bc/pcbi.1004072.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/3a60cae798aa/pcbi.1004072.g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/2d8d0f8ab860/pcbi.1004072.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/98833489a5b5/pcbi.1004072.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/54d660ca350e/pcbi.1004072.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/432ffaaecc4b/pcbi.1004072.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/286f4efbdf08/pcbi.1004072.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/8d49453a0ad4/pcbi.1004072.g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/df14c00a64bc/pcbi.1004072.g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/aa72/4334551/3a60cae798aa/pcbi.1004072.g008.jpg

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