A Hierarchy of Time-Scales and the Brain

Abstract

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.

What controls gain in gain control? Mismatch negativity (MMN), priors and system biases

“…Reactivation of a former standard/deviant role 2.4 minutes later (as per slow sequence data) falls into the time-frame of pattern extraction proposed for more rostral brain areas such as prefrontal cortex (Kiebel et al 2008). Given that the auditory cortical response ultimately overwrites the prior, and yet it is still reactivated or re-imposed on the system, it is possible that the prior is stored in a different location (perhaps at a higher level in the processing hierarchy as suggested in computational models, Friston 2005, Garrido et al 2009) and it is only updated when a marked contextual break occurs.…”

“…Given that the auditory cortical response ultimately overwrites the prior, and yet it is still reactivated or re-imposed on the system, it is possible that the prior is stored in a different location (perhaps at a higher level in the processing hierarchy as suggested in computational models, Friston 2005, Garrido et al 2009) and it is only updated when a marked contextual break occurs. The possibility of superordinate pattern learning extends the time periods of regularity extraction even further as the system would need to store regularities that emerge over tens of minutes, which is necessarily reliant on prefrontal cortex (see Kiebel et al 2008 for review). Indeed, it may even be possible that the disappearance of the bias observed by Todd et al (2013) was due to a tertiary level pattern extraction or third order learning.…”

Predictive coding under the free-energy principle

“…In the following, we see an example of this when considering the perceptual categorization of different sequences of chirps subtending birdsongs. This attribute of hierarchically coupled attractors enables the representation of arbitrarily long sequences of sequences and suggests that neuronal representations in the brain will change more slowly at higher levels (Kiebel et al 2008; see also Botvinick 2007;Hasson et al 2008). One can turn this argument on its head and use the fact that we are able to recognize sequences of sequences (e.g.…”

A Neuronal Model of Predictive Coding Accounting for the Mismatch Negativity

“…While mathematical models of predictive coding have been proposed (Garrido et al, 2007;Kiebel et al, 2008Kiebel et al, , 2009, including some attributing distinct functions to the various cortical layers (Friston, 2005), none of them has yet led to a precise neuronal implementation of the generators of the MMN, in terms of realistic receptors, synapses, and spiking neurons. Nor has there been a systematic comparison of the predictions of the models with actual experimental results.…”