Trends in Neurosciences
OpinionLow-frequency neuronal oscillations as instruments of sensory selection
Introduction
Over 75 years ago, Bishop [1] raised the fundamental proposition that neuroelectric oscillations reflect cyclical variations in neuronal excitability. In the ensuing decades, increasingly specific linkages have been drawn between neuronal oscillations in defined frequency bands and a variety of cognitive functions. Linkages include (i) theta-band oscillations with phase-encoding of spatial information in hippocampus [2] and with formation of mnemonic neuronal representations [3], (ii) alpha-band oscillations with ‘internally-directed’ cognitive processes [4] and (iii) gamma-band oscillations with feature binding [5] and attention or sensory selection [6]. Thus, although the issue is not without controversy (e.g. Ref. [7]), there is gathering consensus that neuronal oscillations have an important role in brain operations to the extent that understanding of neuronal oscillation ‘rhythms’ now seems to be essential to our understanding of brain function 8, 9.
We explore and advance the proposition that neuronal oscillations serve as crucial instruments of active input selection at the level of primary sensory cortex. Paradoxically, delta-band oscillations, long considered to index states of deep sleep and/or conditions of brain compromise [10], are at the heart of this phenomenon. In considering this proposition, we review findings about oscillations in four key areas: (i) their control of neuronal excitability, (ii) their mechanistic role in the amplification of sensory inputs, (iii) their control and utilization by attention and (iv) their variable modes of operation in response to task demands. We then describe how the conceptual framework generated by these findings converges with other theoretical positions and offers new explanation of prior behavioral and neurophysiological findings.
Section snippets
Oscillations control neuronal excitability
Local field potentials (LFPs) and their more macroscopic manifestations in the scalp electroencephalogram (EEG) are mainly generated by transmembrane currents occurring synchronously in ensembles of neurons 11, 12. Analysis of LFP distributions across cortical layers shows that the regular variations or ‘oscillations’ of voltage measured at any single point in the extracellular medium reflect the rhythmic (and synchronous) alternation of inward and outward transmembrane current flow in the
Rhythmic processing: converging theory and retrospection on earlier findings
A proposition most akin to rhythmic-mode operation and pre-dating it by several years is the dynamic attending theory. As developed by Jones, Large and colleagues 35, 40, 41 and in a more ‘motor perspective’ by Praamstra and colleagues [37], the idea is that attending itself can be an oscillatory process that entrains to environmental rhythms, thus improving discriminative performance (Figure 3c). Nobre and colleagues 42, 43, 44 suggest that ‘attention to time’ is one of several attentional
Generality of rhythmic-mode processing?
The foregoing framework makes numerous empirical predictions. Sensory selection in a typical ERP spatial attention paradigm, for example, could be accomplished by entraining low-frequency oscillations in the neuronal representations of the relevant locations to the basic rhythm of stimulus presentation. The representations of all other locations could be left to wander in random phase, thus passively and stochastically degrading the processing of an irrelevant event stream, or could be pushed
Concluding comments
Natural stimulation acquired through our own motor behavior or produced by that of another animal is usually rhythmic, in part because motor behavior is itself patterned by oscillatory mechanisms such as the 10 Hz mu rhythm 59, 60. In these and other common circumstances, when there is a relevant stimulus rhythm(s) that intrinsic brain oscillations can entrain to, attention operates in a rhythmic mode putting the range of ambient neuronal oscillations to work in amplifying relevant inputs and
Acknowledgements
We are grateful to our colleagues in the Columbia University Oscillation Journal Club for helpful commentary. We thank J.M. Palva, P. Fries, T. Womelsdorf, R. Desimone, E.G. Jones, E.W. Large and M.R. Jones for advice and help in preparing illustrations. This work is supported the National Institute of Mental Health (MH 060358 and MH 061989; www.nimh.nih.gov).
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