RIKEN Brain Science Institute, Wako-Shi, Japan.
Neural Netw. 2010 Aug;23(6):705-12. doi: 10.1016/j.neunet.2010.05.004. Epub 2010 May 12.
The efficient detection of higher-order synchronization in massively parallel data is of great importance in understanding computational processes in the cortex and represents a significant statistical challenge. To overcome the combinatorial explosion of different spike patterns taking place as the number of neurons increases, a method based on population measures would prove very useful. Following previous work in this direction, we examine the distribution of spike counts across neurons per time bin ('complexity distribution') and devise a method to reliably extract the size and temporal precision of synchronous groups of neurons, even in the presence of strong rate covariations.
高效检测大规模并行数据中的高阶同步对于理解皮层中的计算过程非常重要，同时也代表了一个重大的统计挑战。为了克服随着神经元数量增加而出现的不同尖峰模式的组合爆炸，基于群体度量的方法将非常有用。基于这一方向的先前工作，我们研究了每个时间-bin 中神经元的尖峰计数分布（“复杂度分布”），并设计了一种方法来可靠地提取同步神经元组的大小和时间精度，即使在存在强烈的速率协变的情况下也是如此。
