Hutt Axel, Sutherland Connie, Longtin André
INRIA CR Nancy-Grand Est, CS20101, 54603 Villers-ls-Nancy Cedex, France.
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Aug;78(2 Pt 1):021911. doi: 10.1103/PhysRevE.78.021911. Epub 2008 Aug 26.
We consider how oscillatory activity in networks of excitable systems depends on spatial correlations of random inputs and the spatial range of feedback coupling. Analysis of a neural field model with topographic delayed recurrent feedback reveals how oscillations in certain frequency bands, including the gamma band, are enhanced by increases in the input correlation length. Further, the enhancement is maximal when this length exceeds the feedback coupling range. Suppression of oscillatory power occurs concomitantly in other bands. These effects depend solely on the ratio of input and feedback length scales. The precise positions of these bands are determined by the synaptic constants and the delays. The results agree with numerical simulations of the model and of a network of stochastic spiking neurons, and are expected for any noise-driven excitable element networks.
我们研究了可兴奋系统网络中的振荡活动如何依赖于随机输入的空间相关性以及反馈耦合的空间范围。对具有地形延迟递归反馈的神经场模型的分析揭示了某些频段（包括伽马频段）的振荡如何通过输入相关长度的增加而增强。此外，当该长度超过反馈耦合范围时，增强效果最大。在其他频段会同时出现振荡功率的抑制。这些效应仅取决于输入和反馈长度尺度的比率。这些频段的精确位置由突触常数和延迟决定。结果与该模型以及随机发放脉冲神经元网络的数值模拟结果一致，并且预计适用于任何噪声驱动的可兴奋元素网络。
