A Hierarchy of Time-Scales and the Brain

By Stefan J. Kiebel, Jean Daunizeau, Karl J. Friston. PLoS Comput. Biol., 2008

712 citations recorded by the source corpus.

In this paper, we suggest that cortical anatomy recapitulates the temporal hierarchy that is inherent in the dynamics of environmental states. Many aspects of brain function can be understood in terms of a hierarchy of temporal scales at which representations of the environment evolve. The lowest level of this hierarchy corresponds to fast fluctuations associated with sensory processing, whereas the highest levels encode slow contextual changes in the environment, under which faster representations unfold. First, we describe a mathematical model that exploits the temporal structure of fast sensory input to track the slower trajectories of their underlying causes. This model of sensory encoding or perceptual inference establishes a proof of concept that slowly changing neuronal states can encode the paths or trajectories of faster sensory states. We then review empirical evidence that suggests that a temporal hierarchy is recapitulated in the macroscopic organization of the cortex. This anatomic-temporal hierarchy provides a comprehensive framework for understanding cortical function: the specific time-scale that engages a cortical area can be inferred by its location along a rostro-caudal gradient, which reflects the anatomical distance from primary sensory areas. This is most evident in the prefrontal cortex, where complex functions can be explained as operations on representations of the environment that change slowly. The framework provides predictions about, and principled constraints on, cortical structure–function relationships, which can be tested by manipulating the time-scales of sensory input.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/87b6b6a94d1776d478730913ed803c4c111dc563

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

Citations in the corpus

How Do Expectations Shape Perception? (2018, background): “, by changing the baseline activity of a specific prediction in anticipation of sensory input [115,116]), predictive coding itself is a general theory of how the brain can efficiently encode information, and does not have the specific aim of explaining the cognitive process of prediction.”
Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations. (2015, background): “In addition, it has been demonstrated (for artiﬁcial simulated birdsongs) that the framework allows the learning of hierarchical models that represent dynamical signals at multiple time scales (Kiebel et al., 2008, 2009).”
Hierarchical Active Inference: A Theory of Motivated Control (2018, background): “Calling on early cybernetic models [16–18], the view that the brain uses control hierarchies has inspired many recent proposals [19–25]; for example, hierarchical temporal structures [26], hierarchical reinforcement learning [9], hierarchical mixture of experts [23], distributed adaptive control [8,”
Reinforcement Learning or Active Inference? (2009, methodology): “The same scheme used in this paper has been used to simulate a range of biological processes; ranging from perceptual categorisation of bird-song [57] to perceptual learning during the mismatch negativity paradigm [10].”
Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus (2009, methodology): “[16] use a predictive feedback framework to model temporal receptive fields, resulting in similar lower-level error signals when the actual input deviates from the expected temporal dynamics.”
Situating the default-mode network along a principal gradient of macroscale cortical organization (2016, background): “The DMN is at a maximal distance from systems involved in perception and action in both functional connectivity and anatomical space, indicating that the neural activity in these regions is likely to be comparably insulated from direct environmental input (49, 50).”
Navigating the Affordance Landscape: Feedback Control as a Process Model of Behavior and Cognition. (2016, positive): “Actions at different levels of abstraction [42] are reflected in multiscale brain dynamics and internal models [43], and predictive dynamics link across them (Box 2).”
Cortical representation of the constituent structure of sentences (2011, background): “This proposal would mesh well with the general assumption, made outside of the linguistic domain, that the cortex acts as a predictive system that constantly attempts to improve the predictability of its inputs and that ascending brain activation reflects the residual prediction error or “surprisal””
Topographic mapping of a hierarchy of temporal receptive windows using a narrated story (2011, background): “As a long-term goal, we hope to link the notion of TRWs with the influential bodies of research on working memory (Miller, 1960) and predictive coding (Rao and Ballard, 1999; Kiebel et al., 2008).”
Generative Models of Cortical Oscillations: Neurobiological Implications of the Kuramoto Model (2010, positive): “…studied as they often arise through mere construction, e.g., multiplication of one or more dynamical variables by an explicit time scale factor which functions to slow down the dynamics in the associated subspace (e.g., Fujimoto and Kaneko, 2003; Breakspear and Stam, 2005; Kiebel et al., 2008).”
The Natural Statistics of Audiovisual Speech (2009, background): “Finally, sensory and motor systems will be coupled so that stable features of the brain, body, and/or environment can be exploited to simplify perception and action [2,4,5].”
Ecological-Enactive Cognition as engaging with a field of relevant affordances (2018, background): “…recent years, changes in body and brain gained a lot of attention in research on what is called ‘the anticipating brain’ (see e.g. Friston 2010, 2011; Allen and Friston, 2016; Allen and Gallagher 2016; Kiebel, Daunizeau, and Friston 2008; Cisek 2007; Cisek and Kalaska 2010; Pezzulo and Cisek 2016).”
The graphical brain: Belief propagation and active inference (2017, result): “This implicit belief updating is consistent with invasive recordings in primates, which suggest an anteroposterior gradient of time constants (Kiebel, Daunizeau, & Friston, 2008; Murray et al., 2014).”
MUC (Memory, Unification, Control) and beyond (2013, background): “NMDA receptors are relatively frequent in supra-granular layers, where backward connections terminate (Sherman and Guillery, 1998; Sherman, 2007; Kiebel et al., 2008).”
Physiologically Inspired Model for the Visual Recognition of Transitive Hand Actions (2013, neutral): “In this that mainly treat the relationship between visual and motor representations (Erlhagen et al., 2006; Kiebel et al., 2008; Bonaiuto and Arbib, 2010; Chersi et al., 2011).”