Queensland Brain Institute and School of Mathematics and Physics, The University of Queensland, Brisbane, QLD, Australia.
Front Neural Circuits. 2019 Jan 28;13:1. doi: 10.3389/fncir.2019.00001. eCollection 2019.
A key problem in systems neuroscience is to characterize how populations of neurons encode information in their patterns of activity. An understanding of the encoding process is essential both for gaining insight into the origins of perception and for the development of brain-computer interfaces. However, this characterization is complicated by the highly variable nature of neural responses, and thus usually requires probabilistic methods for analysis. Drawing on techniques from statistical modeling and machine learning, we review recent methods for extracting important variables that quantitatively describe how sensory information is encoded in neural activity. In particular, we discuss methods for estimating receptive fields, modeling neural population dynamics, and inferring low dimensional latent structure from a population of neurons, in the context of both electrophysiology and calcium imaging data.
系统神经科学中的一个关键问题是描述神经元群体如何在其活动模式中对信息进行编码。对编码过程的理解对于深入了解感知的起源和脑机接口的发展都是至关重要的。然而，由于神经反应的高度可变性，这种描述变得复杂，因此通常需要概率方法进行分析。本文借鉴统计建模和机器学习技术，综述了最近用于提取重要变量的方法，这些变量定量描述了感觉信息是如何在神经活动中编码的。特别是，我们讨论了在电生理学和钙成像数据的背景下，用于估计感受野、对神经群体动力学建模以及从神经元群体中推断低维潜在结构的方法。
