在许多不同物种和大脑区域的各种成像和电生理研究表明,与不同行为模式和认知任务相关的神经元动力学具有类似序列的结构,即使在编码固定概念时。这些神经元序列的特征在于稳健且可再现的时空激活模式。这表明神经元序列的作用可能比通常认为的更重要。此外,认为大脑不仅仅是一个被动的观察者,而是一个感觉输入的主动预测者,在人类行为学和生理学等领域得到了大量证据的支持,除了神经科学。因此,这篇综述的一个中心方面是说明如何将神经元序列理解为概率预测信息处理的关键,以及什么动力学原理可以用作神经元序列的发生器。此外,因为来自神经科学和计算模型的不同证据表明,大脑是按时间尺度的功能层次结构组织的,我们还将回顾如何将基于序列生成原则的模型嵌入到这样的层次结构中,形成感官输入识别和预测的生成模型。我们很快引入了贝叶斯大脑假设,作为一个突出的数学描述,即,快,认可,和预测可以由大脑计算。最后,我们简要讨论了机器学习的一些最新进展,其中时空结构化方法(类似于神经元序列)和分层网络已独立开发用于广泛的任务。我们得出的结论是,对顺序大脑活动的特定动力学和结构原理的研究不仅有助于我们了解大脑如何处理信息并生成预测,但也告诉我们关于神经科学原理可能有用的设计更有效的人工神经网络的机器学习任务。
Various imaging and electrophysiological studies in a number of different species and brain regions have revealed that neuronal dynamics associated with diverse behavioral patterns and cognitive tasks take on a sequence-like structure, even when encoding stationary concepts. These neuronal sequences are characterized by robust and reproducible spatiotemporal activation patterns. This suggests that the role of neuronal sequences may be much more fundamental for brain function than is commonly believed. Furthermore, the idea that the brain is not simply a passive observer but an active predictor of its sensory input, is supported by an enormous amount of evidence in fields as diverse as human ethology and physiology, besides neuroscience. Hence, a central aspect of this review is to illustrate how neuronal sequences can be understood as critical for probabilistic predictive information processing, and what dynamical principles can be used as generators of neuronal sequences. Moreover, since different lines of evidence from neuroscience and computational modeling suggest that the brain is organized in a functional hierarchy of time scales, we will also review how models based on sequence-generating principles can be embedded in such a hierarchy, to form a generative model for recognition and prediction of sensory input. We shortly introduce the Bayesian brain hypothesis as a prominent mathematical description of how online, i.e., fast, recognition, and predictions may be computed by the brain. Finally, we briefly discuss some recent advances in machine learning, where spatiotemporally structured methods (akin to neuronal sequences) and hierarchical networks have independently been developed for a wide range of tasks. We conclude that the investigation of specific dynamical and structural principles of sequential brain activity not only helps us understand how the brain processes information and generates predictions, but also informs us about neuroscientific principles potentially useful for designing more efficient artificial neuronal networks for machine learning tasks.