October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Neural mechanism of orientation selectivity for distinct gamma oscillations in cat V1
Author Affiliations & Notes
  • Chuanliang Han
    Beijing Normal University
  • Bin Wang
    Beijing Normal University
  • Guanzhong Yang
    Beijing Normal University
  • Weifeng Dai
    Beijing Normal University
  • Yi Yang
    Beijing Normal University
  • Tian Wang
    Beijing Normal University
  • Yang Li
    Beijing Normal University
  • Chun-I Yeh
    National Taiwan University
  • Dajun Xing
    Beijing Normal University
  • Footnotes
    Acknowledgements  This work was supported by National Key Basic Research Program of China 2014CB846100 and 2014CB846101, National Natural Science Foundation of China Grant (31371110), and the BNU Interdisciplinary Research Foundation for the First-Year Doctoral Candidates (BNUXKJC1909)
Journal of Vision October 2020, Vol.20, 1116. doi:https://doi.org/10.1167/jov.20.11.1116
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    • Get Citation

      Chuanliang Han, Bin Wang, Guanzhong Yang, Weifeng Dai, Yi Yang, Tian Wang, Yang Li, Chun-I Yeh, Dajun Xing; Neural mechanism of orientation selectivity for distinct gamma oscillations in cat V1. Journal of Vision 2020;20(11):1116. doi: https://doi.org/10.1167/jov.20.11.1116.

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

  • Supplements

Gamma-band (30-100Hz) activity in the local field potential (LFP) is commonly observed in different brain structures and thought to play important roles in information processing. Recent studies showed that two distinct oscillatory components peaking in gamma band (low gamma and high gamma) exist in the LFP of primary visual cortex (V1). However, the neural mechanisms for generating the two gamma components and their distinct response properties are poorly understood. Here, we simultaneously recorded from visual area 18 and the lateral geniculate nucleus (LGN) of anesthetized cats and collected their neural activity (both LFP and multi-unit activity, MUA) to drifting grating stimuli with different orientations. We found two gamma components (low gamma, 50-65Hz, high gamma, 70-100Hz) coexist in cat V1, and the orientation tunings of the two gamma components and spike activity (measured by MUA) are significantly different. Orientation selectivity, quantified as the circular variance (CV), of MUA is significantly higher than the selectivity of both low gamma component (average CV difference is 0.12; t-test: t=6.6, p<0.001) and high gamma components (average CV difference is 0.23; t-test: t=14.9, p<0.001); but the CV of low gamma is more similar to the CV of MUA. Interestingly we found that the ratio of low-gamma power and high-gamma power is positively correlated with circular variance of MUA (r=0.55, p<0.001), low-gamma (r=0.64, p<0.001) and high-gamma (r=0.4, p<0.001). Moreover, only high gamma component is found in the LGN, indicating that low gamma component originates within V1, possibly through recurrent neural network. As the strength of recurrent neural network increases, the orientation selectivity of V1 output gets weaker. Our work demonstrates multiple sources for gamma oscillation and this property might give us a useful tool to probe feedforward and recurrent connections to functional properties in the brain.


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