Abstract
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.
Highlights
In the neurosciences, one important experimental and theoretical finding of recent years was that many brain functions can be described as predictive (Rao and Ballard, 1999; Pastalkova et al, 2008; Friston and Kiebel, 2009; Aitchison and Lengyel, 2017)
A Hierarchy of Time Scales Importantly, all theories of cortical hierarchies of function share the common assumption that primary sensory regions encode rather quickly changing dynamics representing the fast features of sensory input, and that those regions are at the bottom of the hierarchy, while temporally more extended or more abstract representations are located in higher order cortices
We have reviewed the evidence that our brain senses its environment as sequential sensory input, and uses neuronal sequences for predicting future sensory input
Summary
One important experimental and theoretical finding of recent years was that many brain functions can be described as predictive (Rao and Ballard, 1999; Pastalkova et al, 2008; Friston and Kiebel, 2009; Aitchison and Lengyel, 2017). This means that the brain represents current states of the environment and potential states of the future to adaptively select its actions and behavior. To familiarize readers of different backgrounds with each of these topics, we first briefly give an overview of the topics of sequences, predictions, hierarchical structure, the so-called Bayesian brain hypothesis and provide a more precise definition of the kind of sequence-like neuronal dynamics that we consider in this review
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