August 2016
Volume 16, Issue 12
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2016
Temporal Summation and Adaptation in Human Visual Cortex
Author Affiliations
  • Jingyang Zhou
    Department of Psychology, New York University
  • Noah Benson
    Department of Psychology, New York University
  • Kendrick Kay
    Department of Radiology, University of Minnesota
  • Jonathan Winawer
    Department of Psychology, New York University
Journal of Vision September 2016, Vol.16, 1228. doi:
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      Jingyang Zhou, Noah Benson, Kendrick Kay, Jonathan Winawer; Temporal Summation and Adaptation in Human Visual Cortex. Journal of Vision 2016;16(12):1228.

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

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INTRODUCTION. Neuroimaging studies have characterized human visual cortex and subcortical nuclei using population receptive field ('pRF') models. This approach captures systematic visual properties such as scaling of spatial summation (pRF size) with eccentricity. Here, we extend pRF models to the temporal domain, building models that describe how temporally modulated stimuli relate to fMRI signals. METHODS. In each trial, subjects viewed a large-field contrast image either as a single pulse of variable duration (17-533 ms), or as two pulses (133 ms each) separated by variable duration (17-533 ms). Our temporal pRF model comprised a linear term (stimulus time-course convolved with an impulse response function) and a normalization term (linear term after low-pass temporal filtering). We divide the linear term by the normalization term and convolve the result with a hemodynamic response function ('HRF') to predict the fMRI signal. The linear and normalization terms are fitted to each visual area. We interpret the two terms as capturing temporal summation and temporal adaptation, respectively. RESULTS. The model provided excellent fits: cross-validated predictions explained 70% (temporal-occipital maps) to 97% (V1–V3) of the variance in response amplitudes across stimuli. Temporal summation length was shorter in V1 (time to peak, ~20 ms) than V3 or hV4 (~50-90 ms), whereas the temporal extent of normalization did not differ systematically between maps. Model fits to the fMRI data were also used to generate predictions at the millisecond scale (omitting convolution with the HRF); these predictions were in good qualitative agreement to time-varying broadband potentials measured by human electrocorticography in a separate dataset. CONCLUSION. We extended the pRF approach from space to time, accurately predicting responses across visual areas to stimuli with a range of temporal profiles. The increasing length of temporal summation in extrastriate maps compared to V1 parallels the increasing size of spatial receptive fields.

Meeting abstract presented at VSS 2016


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