Phase resetting curves and oscillatory stability in interneurons of rat somatosensory cortex
Phase resetting curves and oscillatory stability in interneurons of rat somatosensory cortex
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DOI：
10.1529/biophysj.106.088021
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发表时间：
2007-01-01
影响因子：
3.4
通讯作者：
Robinson, H. P. C.
中科院分区：
文献类型：
--
作者：
Tateno, T.;Robinson, H. P. C.
神经活动的同步振荡存在于大脑皮层的大部分区域。据信，特定的中间神经元群体在通过相互突触和间隙连接相互作用产生这些同步振荡中发挥重要作用。然而，关于特定类型的中间神经元及其局部网络如何稳定地维持振荡的机制，我们知之甚少。为了更深入地了解这一点，我们测量了膜电位的小电流脉冲扰动的反应，在定期发射，构建相位重置曲线（PRCs）的三种类型的中间神经元：非锥体的定期尖峰（NPRS），低阈值尖峰（LTS），和快速尖峰（FS）细胞。在每种细胞类型中，观察到单相和双相PRC，但比例和对扰动幅度的敏感性与细胞类型明显相关。然后，我们分析了实验测量的PRCs预测振荡稳定性，或。环的可靠性，细胞的复杂随机输入，发生在体内。为了做到这一点，我们使用了随机动力系统理论的方法来估计圆上的简化相位模型的李雅普诺夫指数。结果表明，LTS和NPRS细胞有更大的振荡稳定性（更可靠地夹带）在小噪声输入比FS细胞，这是一致的，其不同类型的阈值动力学。
Synchronous oscillations in neural activity are found over wide areas of the cortex. Specific populations of interneurons are believed to play a significant role in generating these synchronized oscillations through mutual synaptic and gap-junctional interactions. Little is known, though, about the mechanism of how oscillations are maintained stably by particular types of interneurons and by their local networks. To obtain more insight into this, we measured membrane-potential responses to small current-pulse perturbations during regular firing, to construct phase resetting curves (PRCs) for three types of interneurons: nonpyramidal regular-spiking (NPRS), low-threshold spiking (LTS), and fast-spiking (FS) cells. Within each cell type, both monophasic and biphasic PRCs were observed, but the proportions and sensitivities to perturbation amplitude were clearly correlated to cell type. We then analyzed the experimentally measured PRCs to predict oscillation stability, or. ring reliability, of cells for a complex stochastic input, as occurs in vivo. To do this, we used a method from random dynamical system theory to estimate Lyapunov exponents of the simplified phase model on the circle. The results indicated that LTS and NPRS cells have greater oscillatory stability ( are more reliably entrained) in small noisy inputs than FS cells, which is consistent with their distinct types of threshold dynamics.