Roach James P, Eniwaye Bolaji, Booth Victoria, Sander Leonard M, Zochowski Michal R
Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, United States.
Department of Physics, University of Michigan, Ann Arbor, MI, United States.
Front Syst Neurosci. 2019 Nov 12;13:64. doi: 10.3389/fnsys.2019.00064. eCollection 2019.
Rate coding and phase coding are the two major coding modes seen in the brain. For these two modes, network dynamics must either have a wide distribution of frequencies for rate coding, or a narrow one to achieve stability in phase dynamics for phase coding. Acetylcholine (ACh) is a potent regulator of neural excitability. Acting through the muscarinic receptor, ACh reduces the magnitude of the potassium M-current, a hyperpolarizing current that builds up as neurons fire. The M-current contributes to several excitability features of neurons, becoming a major player in facilitating the transition between Type 1 (integrator) and Type 2 (resonator) excitability. In this paper we argue that this transition enables a dynamic switch between rate coding and phase coding as levels of ACh release change. When a network is in a high ACh state variations in synaptic inputs will lead to a wider distribution of firing rates across the network and this distribution will reflect the network structure or pattern of external input to the network. When ACh is low, network frequencies become narrowly distributed and the structure of a network or pattern of external inputs will be represented through phase relationships between firing neurons. This work provides insights into how modulation of neuronal features influences network dynamics and information processing across brain states.
速率编码和相位编码是大脑中两种主要的编码模式。对于这两种模式，网络动力学要么具有广泛的频率分布以进行速率编码，要么具有狭窄的频率分布以在相位编码的相位动力学中实现稳定性。乙酰胆碱（ACh）是神经兴奋性的有效调节剂。通过毒蕈碱受体起作用，ACh会降低钾离子M电流的幅度，M电流是一种在神经元放电时积累的超极化电流。M电流对神经元的几种兴奋性特征有贡献，成为促进1型（整合器）和2型（谐振器）兴奋性之间转变的主要因素。在本文中，我们认为这种转变能够随着ACh释放水平的变化在速率编码和相位编码之间实现动态切换。当网络处于高ACh状态时，突触输入的变化将导致整个网络的放电率分布更广泛，并且这种分布将反映网络结构或网络的外部输入模式。当ACh水平较低时，网络频率分布变窄，网络结构或外部输入模式将通过放电神经元之间的相位关系来表示。这项工作为神经元特征的调制如何影响跨脑状态的网络动力学和信息处理提供了见解。
