Nadim Farzan, Brezina Vladimir, Destexhe Alain, Linster Christiane
New Jersey Institute of Technology and Rutgers University, Newark, New Jersey 07102, USA.
J Neurosci. 2008 Nov 12;28(46):11806-13. doi: 10.1523/JNEUROSCI.3796-08.2008.
Emerging experimental evidence suggests that both networks and their component neurons respond to similar inputs differently, depending on the state of network activity. The network state is determined by the intrinsic dynamical structure of the network and may change as a function of neuromodulation, the balance or stochasticity of synaptic inputs to the network, and the history of network activity. Much of the knowledge on state-dependent effects comes from comparisons of awake and sleep states of the mammalian brain. Yet, the mechanisms underlying these states are difficult to unravel. Several vertebrate and invertebrate studies have elucidated cellular and synaptic mechanisms of state dependence resulting from neuromodulation, sensory input, and experience. Recent studies have combined modeling and experiments to examine the computational principles that emerge when network state is taken into account; these studies are highlighted in this article. We discuss these principles in a variety of systems (mammalian, crustacean, and mollusk) to demonstrate the unifying theme of state dependence of network output.
新出现的实验证据表明，无论是神经网络还是其组成神经元，对相似输入的反应都有所不同，这取决于网络活动的状态。网络状态由网络的内在动力学结构决定，并可能随神经调节、网络突触输入的平衡或随机性以及网络活动的历史而变化。关于状态依赖性效应的许多知识来自对哺乳动物大脑清醒和睡眠状态的比较。然而，这些状态背后的机制很难阐明。一些脊椎动物和无脊椎动物研究已经阐明了由神经调节、感觉输入和经验导致的状态依赖性的细胞和突触机制。最近的研究结合了建模和实验，以研究考虑网络状态时出现的计算原理；本文将重点介绍这些研究。我们在各种系统（哺乳动物、甲壳类动物和软体动物）中讨论这些原理，以展示网络输出状态依赖性的统一主题。
