Neuronal Sequence Models for Bayesian Online Inference

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.

Revealing human sensitivity to a latent temporal structure of changes

“…Sequential activity of neuronal assemblies is one of principled neuronal operations that support higher level cognitive functions Eichenbaum (2014) ; Buzsáki and Llinás (2017) and allow humans to form complex spatio-temporal representation of our every day environment Frölich et al (2021) . Akin to grid cells known to support representation of both spatial and nonspatial task states Fu et al (2021) , time cells have been linked to temporal representation of state sequences critical for memory and decision-making Eichenbaum (2014) .…”

Revealing human sensitivity to a latent temporal structure of changes

“…Sequential activity of neuronal assemblies is one of principled neuronal operations that support higher level cognitive functions (Eichenbaum, 2014 ; Buzsáki and Llinás, 2017 ) and allow humans to form complex spatio-temporal representation of our every day environment (Frölich et al, 2021 ). Akin to grid cells known to support representation of both spatial and non-spatial task states (Fu et al, 2021 ), time cells have been linked to temporal representation of state sequences critical for memory and decision-making (Eichenbaum, 2014 ).…”

Cognitive effort and active inference

“…Slowly changing neural activity, of the sort we would anticipate being associated with these beliefs, is often associated with the prefrontal cortices ( Fuster, 1973 ). Beliefs about sequences and narratives (omitted from the figure) are sometimes associated with the hippocampi ( Foster and Wilson, 2007 ; Frölich et al, 2021 ; Huerta and Rabinovich, 2004 ; Pezzulo, Kemere and van der Meer, 2017 ), which share reciprocal connections with the prefrontal cortices. At the second level, beliefs about the instruction are used to formulate beliefs about the intended response modality under each alternative policy, and the (first level) consequences of these alternative choices, perhaps involving the anterior cingulate cortex ( Scherbaum et al, 2012 ; Shenhav et al, 2013 , 2017 ).…”