Rich Scott, Zochowski Michal, Booth Victoria
Applied and Interdisciplinary Mathematics Program, University of Michigan, Ann Arbor, MI, USA.
Departments of Physics and Biophysics, University of Michigan, Ann Arbor, MI, USA.
J Nonlinear Sci. 2020 Oct;30(5):2171-2194. doi: 10.1007/s00332-017-9438-6. Epub 2018 Jan 4.
Acetylcholine (ACh), one of the brain's most potent neuromodulators, can affect intrinsic neuron properties through blockade of an M-type potassium current. The effect of ACh on excitatory and inhibitory cells with this potassium channel modulates their membrane excitability, which in turn affects their tendency to synchronize in networks. Here, we study the resulting changes in dynamics in networks with inter-connected excitatory and inhibitory populations (E-I networks), which are ubiquitous in the brain. Utilizing biophysical models of E-I networks, we analyze how the network connectivity structure in terms of synaptic connectivity alters the influence of ACh on the generation of synchronous excitatory bursting. We investigate networks containing all combinations of excitatory and inhibitory cells with high (Type I properties) or low (Type II properties) modulatory tone. To vary network connectivity structure, we focus on the effects of the strengths of inter-connections between excitatory and inhibitory cells (E-I synapses and I-E synapses), and the strengths of intra-connections among excitatory cells (E-E synapses) and among inhibitory cells (I-I synapses). We show that the presence of ACh may or may not affect the generation of network synchrony depending on the network connectivity. Specifically, strong network inter-connectivity induces synchronous excitatory bursting regardless of the cellular propensity for synchronization, which aligns with predictions of the PING model. However, when a network's intra-connectivity dominates its inter-connectivity, the propensity for synchrony of either inhibitory or excitatory cells can determine the generation of network-wide bursting.
乙酰胆碱（ACh）是大脑中最有效的神经调质之一，它可以通过阻断M型钾电流来影响内在神经元特性。ACh对具有这种钾通道的兴奋性和抑制性细胞的作用会调节它们的膜兴奋性，进而影响它们在网络中同步的倾向。在这里，我们研究了在大脑中普遍存在的具有相互连接的兴奋性和抑制性群体的网络（E-I网络）中由此产生的动力学变化。利用E-I网络的生物物理模型，我们分析了就突触连接而言的网络连接结构如何改变ACh对同步兴奋性爆发产生的影响。我们研究了包含具有高（I型特性）或低（II型特性）调制音调的兴奋性和抑制性细胞的所有组合的网络。为了改变网络连接结构，我们关注兴奋性和抑制性细胞之间（E-I突触和I-E突触）以及兴奋性细胞之间（E-E突触）和抑制性细胞之间（I-I突触）的连接强度的影响。我们表明，ACh的存在可能会也可能不会影响网络同步的产生，这取决于网络连接性。具体而言，强大的网络互连会诱导同步兴奋性爆发，而不管细胞的同步倾向如何，这与PING模型的预测一致。然而，当网络的内部连接主导其互连时，抑制性或兴奋性细胞的同步倾向可以决定全网络爆发的产生。
