Individual neurons often produce highly variable responses over nominally identical trials, reflecting a mixture of intrinsic "noise" and systematic changes in the animal's cognitive and behavioral state. Disentangling these sources of variability is of great scientific interest in its own right, but it is also increasingly inescapable as neuroscientists aspire to study more complex and naturalistic animal behaviors. In these settings, behavioral actions never repeat themselves exactly and may rarely do so even approximately. Thus, new statistical methods that extract reliable features of neural activity using few, if any, repeated trials are needed. Accurate statistical modeling in this severely trial-limited regime is challenging, but still possible if simplifying structure in neural data can be exploited. We review recent works that have identified different forms of simplifying structure -- including shared gain modulations across neural subpopulations, temporal smoothness in neural firing rates, and correlations in responses across behavioral conditions -- and exploited them to reveal novel insights into the trial-by-trial operation of neural circuits.
翻译:个体神经元往往对名义上相同的试验产生高度不同的反应,反映了动物认知和行为状态的内在“噪音”和系统变化的混合体。分离这些变异源本身具有极大的科学利益,但随着神经科学家渴望研究更复杂和自然的动物行为,也越来越不可避免。在这些环境中,行为行动从来不会完全重复,甚至可能很少这样做。因此,需要采用新的统计方法,利用极少(如果有的话)的重复试验来得出神经活动的可靠特征。在这个严格有限的制度下,精确的统计模型具有挑战性,但如果能够利用神经数据结构的简化,仍然有可能。我们审查最近确定不同形式的简化结构的工作,包括神经亚群群的共享增益调节、神经发速的时平滑以及各种行为条件下的对应关系。我们利用这些方法来揭示对神经电路的实验操作的新洞察力。