Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations
Membrane potential resonance in non-oscillatory neurons interacts with synaptic connectivity to produce network oscillations
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DOI：
10.1007/s10827-019-00710-y
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发表时间：
2019-04-01
影响因子：
1.2
通讯作者：
Rotstein, Horacio G.
中科院分区：
文献类型：
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作者：
Bel, Andrea;Rotstein, Horacio G.
几种神经元类型已被证明表现出（阈下）膜电位共振（MPR），定义为在其电压振幅响应振荡输入电流在一个优选的（谐振）频率的峰值的出现。MPR已经在实验和理论上进行了研究。然而，MPR是否仅仅是一种附带现象，或者它对神经元网络振荡的产生起着功能性作用，以及个体非振荡细胞中存在的潜伏时间尺度如何影响它们嵌入的振荡网络的性质是一个悬而未决的问题。我们解决这些问题，通过调查一个最小的网络模型组成的（i）一个非振荡的线性谐振器（带通滤波器）与二维动态，（ii）一个被动的细胞（低通滤波器）与一维线性动态，和（iii）非线性分级突触连接（兴奋性或抑制性）与瞬时动态。我们证明，（i）网络振荡至关重要地依赖于谐振器中MPR的存在，（ii）它们被放大的网络连接，（iii）他们开发的相互抑制/激发足够高的水平的弛豫振荡，和（iv）网络频率单调地依赖于谐振器的谐振频率。我们解释这些现象，使用一个减少适应版本的经典相平面分析，有助于揭示类型的有效网络非线性，有助于产生网络振荡。我们将我们的结果扩展到具有2D动态细胞的网络。我们的研究结果对放电率类型的网络模型和其他生物振荡网络（例如，生物化学，遗传）有直接的影响。
Several neuron types have been shown to exhibit (subthreshold) membrane potential resonance (MPR), defined as the occurrence of a peak in their voltage amplitude response to oscillatory input currents at a preferred (resonant) frequency. MPR has been investigated both experimentally and theoretically. However, whether MPR is simply an epiphenomenon or it plays a functional role for the generation of neuronal network oscillations and how the latent time scales present in individual, non-oscillatory cells affect the properties of the oscillatory networks in which they are embedded are open questions. We address these issues by investigating a minimal network model consisting of (i) a non-oscillatory linear resonator (band-pass filter) with 2D dynamics, (ii) a passive cell (low-pass filter) with 1D linear dynamics, and (iii) nonlinear graded synaptic connections (excitatory or inhibitory) with instantaneous dynamics. We demonstrate that (i) the network oscillations crucially depend on the presence of MPR in the resonator, (ii) they are amplified by the network connectivity, (iii) they develop relaxation oscillations for high enough levels of mutual inhibition/excitation, and (iv) the network frequency monotonically depends on the resonators resonant frequency. We explain these phenomena using a reduced adapted version of the classical phase-plane analysis that helps uncovering the type of effective network nonlinearities that contribute to the generation of network oscillations. We extend our results to networks having cells with 2D dynamics. Our results have direct implications for network models of firing rate type and other biological oscillatory networks (e.g, biochemical, genetic).