Cortical State and Attention

By Kenneth D. Harris, Alexander Thiele. Nature Reviews Neuroscience, 2011

929 citations recorded by the source corpus.

The brain continuously adapts its processing machinery to behavioural demands. To achieve this, it rapidly modulates the operating mode of cortical circuits, controlling the way that information is transformed and routed. This article will focus on two experimental approaches by which the control of cortical information processing has been investigated: the study of state-dependent cortical processing in rodents and attention in the primate visual system. Both processes involve a modulation of low-frequency activity fluctuations and spiking correlation, and are mediated by common receptor systems. We suggest that selective attention involves processes that are similar to state change, and that operate at a local columnar level to enhance the representation of otherwise non-salient features while suppressing internally generated activity patterns.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/ccc87e174d4763f70e1d3277d3e0ebf54a3e4fce

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Citations in the corpus

Combining non-invasive transcranial brain stimulation with neuroimaging and electrophysiology: Current approaches and future perspectives (2016, positive): “the 8–12Hz alpha band), has proven as useful indicator, if not constituting element, of the level of vigilance or selective attention which fluctuates spontaneously but is also modulated in a task-related manner (Harris and Thiele, 2011; Jensen et al., 2011; Ros et al., 2014).”
Correlations and Neuronal Population Information (2016, background): “Similarly, the allocation of attention can reduce correlations strongly (Cohen & Maunsell 2009, Mitchell et al. 2009, Harris & Thiele 2011, Herrero et al. 2013) but in other contexts may increase correlations (Ruff & Cohen 2014).”
Neuromodulation of brain states. (2012, neutral): “…of transient (e.g., a brief sound or tactile stimulus) versus sustained (e.g., drifting gratings) sensory stimuli, which evoke different degrees of neuronal adaptation (Harris and Thiele, 2011), as strong adaptation is observed primarily in behaviorally inactive states (Castro-Alamancos, 2004a).”
Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding (2014, positive): “The effects of arousal observed here thus overlap with those of focused spatial attention in primates, where attention is associated with increased temporal patterning in the gamma band and decreased noise correlations (Cohen and Maunsell, 2009; Fries et al., 2001; Harris and Thiele, 2011; Mitchell ”
Pupil fluctuations track fast switching of cortical states during quiet wakefulness (2014, positive): “Responses to external stimuli are strongly modulated by the brain’s internal dynamics, which are organized around characteristic states that vary with arousal, attention, and behavioral context (Harris and Thiele, 2011; Iriki et al., 1996; Kahneman, 1973; Lee and Dan, 2012).”
Flexible sensorimotor computations through rapid reconfiguration of cortical dynamics (2018, neutral): “, 2018) or modulates the synaptic properties or activation functions of a subset of cortical neurons (Harris and Thiele, 2011; Nadim and Bucher, 2014; Dipoppa et al., 2018).”
Waking State: Rapid Variations Modulate Neural and Behavioral Responses (2015, neutral): “…as arousal, attention, and awareness are ostensibly less dramatic, and therefore the mechanisms of waking state variations have remained more elusive (reviewed by Harris and Thiele, 2011; Kanwisher, 2001; Lamme, 2003; Maunsell and Treue, 2006; Reynolds and Heeger, 2009; Zagha and McCormick, 2014).”
Decorrelation of Neural-Network Activity by Inhibitory Feedback (2012, background): “In other words, the fluctuations in the population activity serve as a measure of pairwise spike-train correlations [32]: small (large) population averaged spike-train correlations are accompanied by small (large) fluctuations in the population rate (see lower panels in Fig.”
The Convallis Rule for Unsupervised Learning in Cortical Networks (2013, background): “Recurrent spiking network models can exhibit multiple global patterns of population activity, of which the asynchronous irregular state provides the closest match to in vivo cortical activity in alert animals [45–47].”