Weber Andrea P, Hahnloser Richard H R
Institute of Neuroinformatics UZH/ETH Zurich, Zurich, Switzerland.
PLoS Comput Biol. 2007 Dec;3(12):e249. doi: 10.1371/journal.pcbi.0030249.
The relationships between neural activity at the single-cell and the population levels are of central importance for understanding neural codes. In many sensory systems, collective behaviors in large cell groups can be described by pairwise spike correlations. Here, we test whether in a highly specialized premotor system of songbirds, pairwise spike correlations themselves can be seen as a simple corollary of an underlying random process. We test hypotheses on connectivity and network dynamics in the motor pathway of zebra finches using a high-level population model that is independent of detailed single-neuron properties. We assume that neural population activity evolves along a finite set of states during singing, and that during sleep population activity randomly switches back and forth between song states and a single resting state. Individual spike trains are generated by associating with each of the population states a particular firing mode, such as bursting or tonic firing. With an overall modification of one or two simple control parameters, the Markov model is able to reproduce observed firing statistics and spike correlations in different neuron types and behavioral states. Our results suggest that song- and sleep-related firing patterns are identical on short time scales and result from random sampling of a unique underlying theme. The efficiency of our population model may apply also to other neural systems in which population hypotheses can be tested on recordings from small neuron groups.
单细胞水平和群体水平的神经活动之间的关系对于理解神经编码至关重要。在许多感觉系统中，大细胞群体中的集体行为可以通过成对的尖峰相关性来描述。在这里，我们测试在鸣禽高度特化的运动前系统中，成对的尖峰相关性本身是否可以被视为潜在随机过程的简单推论。我们使用一个独立于详细单神经元特性的高级群体模型，测试关于斑胸草雀运动通路中连接性和网络动力学的假设。我们假设在歌唱过程中神经群体活动沿着有限的一组状态演变，并且在睡眠期间群体活动在歌曲状态和单个静息状态之间随机来回切换。通过将特定的放电模式（如爆发性放电或紧张性放电）与每个群体状态相关联来生成单个尖峰序列。通过对一两个简单控制参数的整体修改，马尔可夫模型能够重现不同神经元类型和行为状态下观察到的放电统计和尖峰相关性。我们的结果表明，与歌曲和睡眠相关的放电模式在短时间尺度上是相同的，并且是由一个独特潜在主题的随机抽样产生的。我们群体模型的有效性可能也适用于其他神经系统，在这些系统中，可以在小神经元群体的记录上测试群体假设。
