Voges Nicole, Perrinet Laurent
Institut de Neurosciences Cognitives de la Méditerranée, UMR CNRS - Aix-Marseille Université, France.
J Physiol Paris. 2010 Jan-Mar;104(1-2):51-60. doi: 10.1016/j.jphysparis.2009.11.004. Epub 2009 Nov 10.
We study cortical network dynamics for a spatially embedded network model. It represents, in terms of spatial scale, a large piece of cortex allowing for long-range connections, resulting in a rather sparse connectivity. The spatial embedding also permits us to include distance-dependent conduction delays. We use two different types of conductance-based I&F neurons as excitatory and inhibitory units, as well as specific connection probabilities. In order to remain computationally tractable, we reduce neuron density, modelling part of the missing internal input via external poissonian spike trains. Compared to previous studies, we observe significant changes in the dynamical phase space: Altered activity patterns require another regularity measures than the coefficient of variation. Hence, we compare three different regularity measure on the basis of artificial inter-spike-interval distributions. We identify two types of mixed states, where different phases coexist in certain regions of the phase space. More notably, our boundary between high and low activity states depends predominantly on the relation between excitatory and inhibitory synaptic strength instead of the input rate.
我们研究了空间嵌入网络模型的皮层网络动力学。就空间尺度而言，它代表了一大块允许长程连接的皮层，从而导致相当稀疏的连接性。空间嵌入还使我们能够纳入距离依赖性传导延迟。我们使用两种不同类型的基于电导的积分发放（I&F）神经元作为兴奋性和抑制性单元，以及特定的连接概率。为了保持计算上的可处理性，我们降低了神经元密度，通过外部泊松尖峰序列对部分缺失的内部输入进行建模。与之前的研究相比，我们观察到动力学相空间有显著变化：改变的活动模式需要不同于变异系数的其他规律性度量。因此，我们基于人工峰峰间期分布比较了三种不同的规律性度量。我们识别出两种混合状态，其中不同相在相空间的某些区域共存。更值得注意的是，我们的高活动状态和低活动状态之间的边界主要取决于兴奋性和抑制性突触强度之间的关系，而不是输入速率。
