Inhibitory Networks of Fast-Spiking Interneurons Generate Slow Population Activities due to Excitatory Fluctuations and Network Multistability
Inhibitory Networks of Fast-Spiking Interneurons Generate Slow Population Activities due to Excitatory Fluctuations and Network Multistability
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DOI：
10.1523/jneurosci.5446-11.2012
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发表时间：
2012-07-18
影响因子：
5.3
通讯作者：
Skinner, Frances K.
中科院分区：
文献类型：
--
作者：
Ho, Ernest C. Y.;Ber, Michael Stru;Skinner, Frances K.
缓慢群体活动 (SPA) 存在于大脑中，频率低于 5 Hz。尽管 SPA 在多个皮质区域中很突出并且具有许多假定的功能，但它们的机制尚不清楚。我们研究了 C57BL/6 小鼠海马体表现出的一种特定类型的体外 GABA 能、基于抑制的 SPA。我们采用了包括实验、模拟和数学分析在内的多管齐下的方法来揭示海马 SPA 的机制。我们的结果表明，海马 SPA 是一种新兴现象，其中网络的“缓慢”是由于个体快速放电、抑制性中间神经元的突触和细胞特征之间的相互作用造成的。我们的模拟量化了海马 SPA 的特征。特别是，对于海马 SPA 的发生，我们预测单个快速尖峰中间神经元应该具有频率-电流 (f-I) 曲线，该曲线表现出适当大小的扭结，其中曲线的斜率在伽马频率范围内随着电流的增加而更突然地减小。我们还预测这些中间神经元应该彼此良好连接。我们的数学分析表明，正如我们的模拟所预测的那样，突触和内在条件的结合可以促进网络的多稳定性。当兴奋性波动驱动网络在不同的稳定网络放电状态之间时，群体慢时间尺度就会发生。由于我们使用的许多参数都是从实验中提取的，并且随后对快速尖峰中间神经元的实验 f-I 曲线的测量显示出预测的特征，因此我们建议我们的网络模型捕获生物海马网络中的基本操作机制。
Slow population activities (SPAs) exist in the brain and have frequencies below similar to 5 Hz. Despite SPAs being prominent in several cortical areas and serving many putative functions, their mechanisms are not well understood. We studied a specific type of in vitro GABAergic, inhibition-based SPA exhibited by C57BL/6 murine hippocampus. We used a multipronged approach consisting of experiment, simulation, and mathematical analyses to uncover mechanisms responsible for hippocampal SPAs. Our results show that hippocampal SPAs are an emergent phenomenon in which the "slowness" of the network is due to interactions between synaptic and cellular characteristics of individual fast-spiking, inhibitory interneurons. Our simulations quantify characteristics underlying hippocampal SPAs. In particular, for hippocampal SPAs to occur, we predict that individual fast-spiking interneurons should have frequency-current (f-I) curves that exhibit a suitably sized kink where the slope of the curve decreases more abruptly in the gamma frequency range with increasing current. We also predict that these interneurons should be well connected with one another. Our mathematical analyses show that the combination of synaptic and intrinsic conditions, as predicted by our simulations, promotes network multistability. Population slow timescales occur when excitatory fluctuations drive the network between different stable network firing states. Since many of the parameters we use are extracted from experiments and subsequent measurements of experimental f-I curves of fast-spiking interneurons exhibit characteristics as predicted, we propose that our network models capture a fundamental operating mechanism in biological hippocampal networks.