Shimazaki Hideaki, Sadeghi Kolia, Ishikawa Tomoe, Ikegaya Yuji, Toyoizumi Taro
RIKEN Brain Science Institute, 2-1 Hirosawa, Wako-shi, Saitama 351-0198, Japan.
Commonwealth Computer Research Inc., 1422 Sachem Pl., Unit #1, Charlottesville, VA 22901, U.S.A.
Sci Rep. 2015 Apr 28;5:9821. doi: 10.1038/srep09821.
Activity patterns of neural population are constrained by underlying biological mechanisms. These patterns are characterized not only by individual activity rates and pairwise correlations but also by statistical dependencies among groups of neurons larger than two, known as higher-order interactions (HOIs). While HOIs are ubiquitous in neural activity, primary characteristics of HOIs remain unknown. Here, we report that simultaneous silence (SS) of neurons concisely summarizes neural HOIs. Spontaneously active neurons in cultured hippocampal slices express SS that is more frequent than predicted by their individual activity rates and pairwise correlations. The SS explains structured HOIs seen in the data, namely, alternating signs at successive interaction orders. Inhibitory neurons are necessary to maintain significant SS. The structured HOIs predicted by SS were observed in a simple neural population model characterized by spiking nonlinearity and correlated input. These results suggest that SS is a ubiquitous feature of HOIs that constrain neural activity patterns and can influence information processing.
神经群体的活动模式受潜在生物学机制的限制。这些模式不仅以个体活动率和成对相关性为特征,还以大于两个神经元的组之间的统计依赖性为特征,即高阶相互作用(HOIs)。虽然HOIs在神经活动中普遍存在,但其主要特征仍不清楚。在这里,我们报告神经元的同步沉默(SS)简洁地概括了神经HOIs。培养的海马切片中自发活动的神经元表现出的SS比根据其个体活动率和成对相关性预测的更为频繁。SS解释了数据中观察到的结构化HOIs,即在连续相互作用阶次上交替的符号。抑制性神经元对于维持显著的SS是必要的。在以尖峰非线性和相关输入为特征的简单神经群体模型中观察到了由SS预测的结构化HOIs。这些结果表明,SS是HOIs的一个普遍特征,它限制了神经活动模式并可能影响信息处理。