• Top page
  • Timetable
  • Per session
  • Per presentation
  • How to
  • Meeting Planner



Functional dissection of neural circuits through coupling between experimental and theoretical approaches

開催日 2014/9/12
時間 9:00 - 11:00
会場 Room F(302)
Chairperson(s) 石井 信 / Shin Ishii (京都大学大学院情報学研究科 システム科学専攻 / Department of Systems Science, Graduate School of Informatics, Kyoto University, Japan)
能瀬 聡直 / Akinao Nose (東京大学大学院新領域創成科学研究科複雑理工学専攻 / Department of Complexity Science and Engineering, Graduate School of Frontier Sciences, The University of Tokyo, Japan)

Dynamics of cortical neuronal activity during learning of a motor task

  • S2-F-1-1
  • 松崎 政紀 / Masanori Matsuzaki:1,2 正水 芳人 / Yoshito Masamizu:1 田中 康裕 / Yasuhiro R Tanaka:1 田中 康代 / Yasuyo H Tanaka:1 平 理一郎 / Riichiro Hira:1,2 大久保 文貴 / Fuki Ohkubo:1,2 
  • 1:自然科学研究機構基礎生物学研究所 / Div. of Brain Circuits., National Institute for Basic Biology, Japan 2:総合研究大学院大学 / SOKENDAI, Japan 

The primary motor cortex (M1) is the most prominent motor-output area of the cerebral cortex. In M1, layers 2/3 (L2/3) and 5a (L5a) constitute intermediate layers upstream of layer 5b (L5b), the major motor-output layer. During motor learning, the microcircuits of M1 are thought to self-organize to integrate various types of signals. However, it remains unknown how the neuronal activities of L2/3 and L5a of M1 are reorganized during learning of a motor task. We conducted two-photon calcium imaging in the mouse M1 during two-week training sessions of a self-initiated lever-pull task (requiring forelimb use). We quantified the movement information carried by the neurons as the mutual information between predicted and recorded lever trajectories. In L2/3, the accuracy of neuronal ensemble prediction of lever trajectory remained unchanged globally throughout the training period. However, in L5a, the ensemble prediction accuracy steadily improved and one-third of neurons evolved to contribute substantially to ensemble prediction in the late stage of learning. The L2/3 network may represent coordination of signals from other areas throughout learning, whereas L5a may participate in the evolving network representing well-learned movements. The distinct dynamic networks in L2/3 and L5a would be core elements that drive the L5b motor output for well-learned movements.

Copyright © Neuroscience2014. All Right Reserved.