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A new regime for highly robust gamma oscillation with co-exist of accurate and weak synchronization in excitatory–inhibitory networks

机译:在兴奋性抑制网络中高鲁棒性伽马振荡与精确同步和弱同步并存的新机制

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摘要

A great number of biological experiments show that gamma oscillation occurs in many brain areas after the presentation of stimulus. The neural systems in these brain areas are highly heterogeneous. Specifically, the neurons and synapses in these neural systems are diversified; the external inputs and parameters of these neurons and synapses are heterogeneous. How the gamma oscillation generated in such highly heterogeneous networks remains a challenging problem. Aiming at this problem, a highly heterogeneous complex network model that takes account of many aspects of real neural circuits was constructed. The network model consists of excitatory neurons and fast spiking interneurons, has three types of synapses (GABAA, AMPA, and NMDA), and has highly heterogeneous external drive currents. We found a new regime for robust gamma oscillation, i.e. the oscillation in inhibitory neurons is rather accurate but the oscillation in excitatory neurons is weak, in such highly heterogeneous neural networks. We also found that the mechanism of the oscillation is a mixture of interneuron gamma (ING) and pyramidal-interneuron gamma (PING). We explained the mixture ING and PING mechanism in a consistent-way by a compound post-synaptic current, which has a slowly rising-excitatory stage and a sharp decreasing-inhibitory stage.
机译:大量的生物学实验表明,出现刺激后,许多大脑区域都会发生伽马振荡。这些大脑区域的神经系统高度异质。具体来说,这些神经系统中的神经元和突触是多样化的。这些神经元和突触的外部输入和参数是异质的。在如此高度异构的网络中如何产生伽马振荡仍然是一个具有挑战性的问题。针对这个问题,构建了一个高度异构的复杂网络模型,该模型考虑了真实神经回路的许多方面。该网络模型由兴奋性神经元和快速突触的中间神经元组成,具有三种类型的突触(GABAA,AMPA和NMDA),并且具有高度异构的外部驱动电流。我们发现了一种强大的伽马振荡的新机制,即在这种高度异构的神经网络中,抑制性神经元的振荡相当准确,但兴奋性神经元的振荡却微弱。我们还发现,振荡的机制是中间神经元γ(ING)和金字塔形中间神经元γ(PING)的混合。我们通过复合的突触后电流以一致的方式解释了ING和PING的混合机制,该过程具有缓慢上升的兴奋阶段和急剧下降的抑制阶段。

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