Intrinsic dynamics in neuronal networks. I. Theory
Intrinsic dynamics in neuronal networks. I. Theory
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DOI：
10.1152/jn.2000.83.2.808
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发表时间：
2000-02-01
影响因子：
2.5
通讯作者：
Nirenberg, S
中科院分区：
文献类型：
--
作者：
Latham, PE;Richmond, BJ;Nirenberg, S
哺乳动物神经系统中的许多网络在没有刺激的情况下仍然活跃。这种活动福尔斯分为两种主要模式：低频率的稳定放电和有节奏的爆发。这些射击模式是如何产生的？具体来说，兴奋性和抑制性神经元之间的动态相互作用是如何产生这些放电模式的，以及网络是如何从一种放电模式切换到另一种放电模式的？我们通过研究大型神经元网络的内在动力学，从理论上研究了这些问题。使用半解析模型的基础上的平均放电率动态和模拟与大型神经网络，我们发现的动态，从而放电模式，主要是由一个参数，内源性活性细胞的分数控制。当没有内源性活跃细胞存在时，网络要么沉默要么以高速率发射;随着内源性活跃细胞数量的增加，有一个向爆发的过渡;并且，随着进一步的增加，有第二个向低速率稳定发射的过渡。第二个角色是网络连接，它决定了活动是以恒定的平均放电率发生还是在平均值附近振荡。这些结论只需要传统的假设：神经元的兴奋性输入增加其放电率，抑制性输入降低它，神经元表现出尖峰频率适应。这些结论也导致了两个实验可检验的预测：1）以低速率发射的孤立网络必须包含内源性活性细胞; 2）这种网络中内源性活性细胞比例的减少必须导致爆裂。
Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.