School of Computer Science and Communication, Department of Computational Biology, Royal Institute of Technology (KTH), Stockholm, Sweden.
PLoS Comput Biol. 2010 Jun 3;6(6):e1000803. doi: 10.1371/journal.pcbi.1000803.
Attractor neural networks are thought to underlie working memory functions in the cerebral cortex. Several such models have been proposed that successfully reproduce firing properties of neurons recorded from monkeys performing working memory tasks. However, the regular temporal structure of spike trains in these models is often incompatible with experimental data. Here, we show that the in vivo observations of bistable activity with irregular firing at the single cell level can be achieved in a large-scale network model with a modular structure in terms of several connected hypercolumns. Despite high irregularity of individual spike trains, the model shows population oscillations in the beta and gamma band in ground and active states, respectively. Irregular firing typically emerges in a high-conductance regime of balanced excitation and inhibition. Population oscillations can produce such a regime, but in previous models only a non-coding ground state was oscillatory. Due to the modular structure of our network, the oscillatory and irregular firing was maintained also in the active state without fine-tuning. Our model provides a novel mechanistic view of how irregular firing emerges in cortical populations as they go from beta to gamma oscillations during memory retrieval.
吸引子神经网络被认为是大脑皮层工作记忆功能的基础。已经提出了几种这样的模型，这些模型成功地再现了在执行工作记忆任务的猴子身上记录的神经元的发射特性。然而，这些模型中尖峰序列的规则时间结构通常与实验数据不兼容。在这里，我们表明，在具有几个连接的超柱的模块化结构的大规模网络模型中，可以实现单细胞水平上具有不规则发射的双稳态活动的体内观察。尽管单个尖峰序列的不规则性很高，但模型在基础状态和活动状态下分别显示出β和γ波段的群体振荡。不规则发射通常出现在平衡兴奋和抑制的高电导状态下。群体振荡可以产生这样的状态，但在以前的模型中，只有非编码的基础状态是振荡的。由于我们的网络具有模块化结构，因此在不需要微调的情况下，即使在活动状态下，振荡和不规则发射也能保持。我们的模型提供了一种新的机制观点，即当皮质群体在记忆检索过程中从β波转变为γ波时，不规则发射是如何出现的。
