September 2019
Volume 19, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2019
Fixational eye movements index slow fluctuations of activity in macaque visual cortex
Author Affiliations & Notes
  • Richard Johnston
    Department of Ophthalmology, University of Pittsburgh
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University
  • Matthew A Smith
    Department of Ophthalmology, University of Pittsburgh
    Center for the Neural Basis of Cognition, University of Pittsburgh and Carnegie Mellon University
    Department of Bioengineering, University of Pittsburgh
Journal of Vision September 2019, Vol.19, 85c. doi:https://doi.org/10.1167/19.10.85c
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      Richard Johnston, Matthew A Smith; Fixational eye movements index slow fluctuations of activity in macaque visual cortex. Journal of Vision 2019;19(10):85c. doi: https://doi.org/10.1167/19.10.85c.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Very recent research identified a fluctuation of activity in macaque visual cortex over the timescale of minutes to tens of minutes (Cowley et al., 2018, Society for Neuroscience). This “slow drift” was associated with behaviour on a perceptual task, and also very strongly associated with simultaneously recorded data in the prefrontal cortex, which suggests that slow drift may index a global aspect of cortical processing. However, the source underlying the slow drift is unclear. Another pervading hallmark of cortical processing is eye movement-related activity, which is seen in the neural activity across a wide span of cortex. Several studies have shown that small fixational eye movements, termed microsaccades, are related closely to moment-to-moment visual perception and attention. These findings motivated us to investigate if fixational eye movements index slow fluctuations of activity in visual cortex. We recorded from populations of neurons in visual cortex (area V4) of two macaque monkeys using chronically implanted microelectrode arrays. The animals performed an orientation change-detection task in which pairs of Gabor stimuli were repeatedly flashed with a fixed likelihood of a change in orientation on each flash. To identify slow drift, dimensionality reduction (PCA) was applied to population spiking data binned in large thirty minute bins. Microsaccade rate was calculated on each trial during a period of fixation prior to the onset of the stimulus sequence. As described above, slow drift was significantly associated with gradual changes in task performance over time (specifically, hit rate and false alarm rate). Interestingly, we also found that slow drift was significantly associated with gradual changes in microsaccade rate over time. These findings shed light on the underlying source of slow drift in visual cortex. They point to a strong link between slow drift and structures that have been implicated in eye-movement control and perceptual decision-making.

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