Neurophysiological and Computational Principles of Cortical Rhythms in Cognition

By Xiao-Jing Wang. Physiological Reviews, 2010

1,918 citations recorded by the source corpus.

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brainwide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells, to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a recipropocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clock-like. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators, and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/4f2fa7c92840ddc323fa39e99f1d1826fa1c4f05

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Citations in the corpus

The Theta-Gamma Neural Code (2013, positive): “However, rhythmicity can be difficult to detect by analysis of spikes alone and is more easily detected by determining whether spikes are phase locked to the oscillations in the local field potential (Wang, 2010).”
Coherence a measure of the brain networks: past and present (2016, neutral): “histamine, serotonin and melatonin [24, 44, 48].”
The Metastable Brain (2014, neutral): “…has received by far themost attention in the literature (Ermentrout and Kopell, 1991; Bressler and 36 Neuron 81, January 8, 2014 ª2014 Elsevier Inc. Kelso, 2001; Bressler and Tognoli, 2006; Fries, 2005; Singer, 2005; Singer and Gray, 1995; Varela et al., 2001; Uhlhaas et al., 2009; Wang, 2010).”
Thalamic functions in distributed cognitive control (2017, neutral): “Niell, 2013, in Journal of Neuroscience, 33(11), 4642–4656.”
Working Memory 2.0 (2018, methodology): “Here, we provide a short summary (for detailed reviews, see Buzsáki and Wang, 2012; Fries, 2015; Wang, 2010).”
Cortical High-Density Counterstream Architectures (2013, background): “These differences in coherence reflect differences in interareal synchronization ( 73 – 76 ), which are thought to facilitate effective communication ( 74 , 77 ).”
Cognitive dysfunction in psychiatric disorders: characteristics, causes and the quest for improved therapy (2012, background): “Finally, representing a level of integration that is intermediate between cells and cerebral circuits, neurons do not generally act in isolation; rather, they operate as synchronized and rhythmically active assemblies to encode, transmit and modulate information underpinning cognitive functio”
A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula (2016, background): “potential for understanding brain oscillations, which are increasingly thought to underlie many important cognitive processes [Schnitzler and Gross, 2005; Singer, 2013; Thut et al., 2012; Wang, 2010].”
Neural Cross-Frequency Coupling: Connecting Architectures, Mechanisms, and Functions. (2015, neutral): “Essentially, the challenge that experimentalists are facing is to infer the underlying network structure from CFC neural signatures measured from brain recordings [1,26].”