Visual attention modulates neuronal response through changing the pathway current among neuronal populations: a computational study using multicolumnar model

カテゴリ: 部門大会

論文No: SS2-2

グループ名: 【C】2022年電気学会電子・情報・システム部門大会

発行日: 2022/08/24

タイトル(英語): Visual attention modulates neuronal response through changing the pathway current among neuronal populations: a computational study using multicolumnar model

著者名: Zheng Tianyi（東京大学）,Sugino Masato（東京大学）,Li Bin（東京大学）,Kotani Kiyoshi（東京大学）,Jimbo Yasuhiko（東京大学）

著者名(英語): Tianyi Zheng (The University of Tokyo),Masato Sugino (The University of Tokyo),Bin Li (The University of Tokyo),Kiyoshi Kotani (The University of Tokyo),Yasuhiko Jimbo (The University of Tokyo)

キーワード: Visual attention|Column model|Mean-field approximation|Signal pathway

要約(日本語): Visual attention controls the selection of relevant and filtering out irrelevant information in the visual field. Empirical studies found that prompting attention to the spatial location of preferred stimuli before the presentation of preferred and non-preferred stimuli would recover the suppression of neuronal response due to non-preferred stimulus. This work studies the computational properties of signal pathways supporting such phenomena in visual cortex by analyzing a multicolumnar model. By employing the mean-field approximation method based on Ott-Antonsen Ansatz, signal pathways among neuronal populations in the multicolumnar model can be determined and analyzed. Finally, the dynamics of the pathway current were found to cause the suppression and recovery of the neuronal response.