Tabak Joël, Mascagni Michael, Bertram Richard
Dept. of Biological Science, BRF 206, Florida State Univ., Tallahassee, FL 32306, USA.
J Neurophysiol. 2010 Apr;103(4):2208-21. doi: 10.1152/jn.00857.2009. Epub 2010 Feb 17.
Spontaneous episodic activity is a fundamental mode of operation of developing networks. Surprisingly, the duration of an episode of activity correlates with the length of the silent interval that precedes it, but not with the interval that follows. Here we use a modeling approach to explain this characteristic, but thus far unexplained, feature of developing networks. Because the correlation pattern is observed in networks with different structures and components, a satisfactory model needs to generate the right pattern of activity regardless of the details of network architecture or individual cell properties. We thus developed simple models incorporating excitatory coupling between heterogeneous neurons and activity-dependent synaptic depression. These models robustly generated episodic activity with the correct correlation pattern. The correlation pattern resulted from episodes being triggered at random levels of recovery from depression while they terminated around the same level of depression. To explain this fundamental difference between episode onset and termination, we used a mean field model, where only average activity and average level of recovery from synaptic depression are considered. In this model, episode onset is highly sensitive to inputs. Thus noise resulting from random coincidences in the spike times of individual neurons led to the high variability at episode onset and to the observed correlation pattern. This work further shows that networks with widely different architectures, different cell types, and different functions all operate according to the same general mechanism early in their development.
自发阵发性活动是发育中神经网络的一种基本运作模式。令人惊讶的是，一阵活动的持续时间与之前的静息间隔长度相关，而与之后的间隔无关。在这里，我们使用一种建模方法来解释发育中神经网络的这一尚未得到解释的特征。由于在具有不同结构和成分的网络中都观察到了这种相关模式，一个令人满意的模型需要生成正确的活动模式，而不管网络架构的细节或单个细胞的特性如何。因此，我们开发了简单的模型，该模型纳入了异质神经元之间的兴奋性耦合以及活动依赖的突触抑制。这些模型稳健地生成了具有正确相关模式的阵发性活动。这种相关模式是由于阵发性活动在从抑制中恢复的随机水平上被触发，而在大致相同的抑制水平上终止。为了解释阵发性活动起始和终止之间的这种根本差异，我们使用了一个平均场模型，其中只考虑平均活动和从突触抑制中恢复的平均水平。在这个模型中，阵发性活动的起始对输入高度敏感。因此，单个神经元放电时间的随机巧合产生的噪声导致了阵发性活动起始时的高变异性以及观察到的相关模式。这项工作进一步表明，具有广泛不同架构、不同细胞类型和不同功能的网络在其发育早期都按照相同的一般机制运作。
