Bistable, irregular firing and population oscillations in a modular attractor memory network.
Bistable, irregular firing and population oscillations in a modular attractor memory network.
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DOI：
10.1371/journal.pcbi.1000803
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发表时间：
2010-06-03
影响因子：
4.3
通讯作者：
Lansner A
中科院分区：
文献类型：
--
作者：
Lundqvist M;Compte A;Lansner A
吸引子神经网络被认为是大脑皮层工作记忆功能的基础。已经提出了几个这样的模型，成功地再现了执行工作记忆任务的猴子记录的神经元的放电特性。然而，在这些模型中的穗列车的规则的时间结构往往是不符合实验数据。在这里，我们表明，在单细胞水平上不规则发射的生物活性的体内观察可以在一个大规模的网络模型中实现，该模型具有几个连接的超柱的模块化结构。尽管个别穗列车的高度不规则性，该模型显示人口振荡的β和γ带在基态和活性状态，分别。不规则放电通常出现在平衡的兴奋和抑制的高电导制度。人口振荡可以产生这样的制度，但在以前的模型中，只有一个非编码的基态振荡。由于我们的网络的模块化结构，振荡和不规则的放电也保持在活跃状态，无需微调。我们的模型提供了一个新的机制视图如何不规则的发射出现在皮层人群，因为他们从β到γ振荡在记忆检索。大脑的基本计算原理仍然是未知的，其中一个主要原因与同时测量足够多细胞的详细数据的困难有关。在监测细胞群的技术中，分辨率较低。计算模型没有这样的测量限制，并且可以在不同的粒度水平上受到几个实验的约束，从而能够测试不同计算理论的生物相容性。吸引子网络范式（attractor network paradigm）就是这样一种理论，它在过去的20年里获得了越来越多的支持，例如，将吸引子记忆模型的输出与新皮层神经元的群体数据和尖峰频率调制进行比较。我们进一步进行这种比较，也看看在一个网络模型中的活动的精细结构与一个新的模块化结构也在体内看到。这使得网络能够在一个新的动态机制中运行。特别是，我们重现了不规则的低速率尖峰的单细胞在体内，这是以前的吸引子网络模型的一个挑战。场电位在伽马和贝塔频率上的振荡，再次被认为与注意力和意识有关，甚至是必不可少的，成为模型潜在动力学的一个特征。
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. The basic computational principles of the brain are still unknown, and one major reason for this is related to the difficulties in simultaneously measuring detailed data from a sufficiently large number of cells. In techniques where populations of cells are monitored, resolution is low. Computational models have no such measurement limitations and can be constrained by several experiments at different levels of granularity, enabling testing of the biological plausibility of different computational theories. One such theory, the attractor network paradigm, has gained increasing support over the past twenty years by, for instance, comparing the output of attractor memory models to population data and spike frequency modulations of neocortical neurons. We take this comparison further by also looking at the fine-structure of activity in a network model with a novel modular structure also seen in vivo. This allows the network to operate in a new dynamic regime. In particular, we reproduce the irregular low-rate spiking of single cells in vivo, which has previously been a challenge for attractor network models. Oscillations in field potentials at gamma and beta frequencies, again believed to be connected to, or even essential for, attention and consciousness, emerge as a feature of the underlying dynamics of the model.
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影响因子：
1.3
作者：
Djurfeldt, M.;Lundqvist, M.;Lansner, A.
通讯作者：
Lansner, A.
影响因子：
2.5
作者：
Compte, A;Constantinidis, C;Wang, WJ
通讯作者：
Wang, WJ
影响因子：
56.9
作者：
FUSTER, JM;ALEXANDER, GE
通讯作者：
ALEXANDER, GE
影响因子：
1.2
作者：
Brunel, N;Wang, XJ
通讯作者：
Wang, XJ
影响因子：
64.8
作者：
Cossart, R;Aronov, D;Yuste, R
通讯作者：
Yuste, R