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視覚 3
Visual System 3

開催日 2014/9/11
時間 18:00 - 19:00
会場 Room G(303)
Chairperson(s) 藤田 一郎 / Ichiro Fujita (大阪大学大学院生命機能研究科 / Osaka University, Graduate School of Frontier Biosciences, Japan)
稲場 直子 / Naoko Inaba (京都大学 学際融合教育研究推進センター / Center for the Promotion of Interdisciplinary Education and Research, Kyoto University, Japan)

On-line optical operant conditioning of cortical activity

  • O1-G-3-2
  • Yuya Kanemoto:1 Michael Hausser:1 
  • 1:University College London, London, UK 

Animals can learn to modify their voluntary behavior to gain rewards in the positive reinforcement form of operant conditioning. It has been shown that animals can also learn to modify neuronal activity by combining electrophysiological recordings with direct reward of activity. While electrophysiological approaches exhibit excellent temporal resolution, they do not permit recordings from the same identified neurons in dense local circuits over multiple days. In contrast, two-photon population calcium imaging makes it possible to observe the activity of the same population of identified neurons in behaving animals over long time periods. Here we introduce a platform for analyzing calcium imaging data on-line, and providing rewards to behaving animals based on the neural activity. We have used this approach to investigate how animals modify population activity during operant conditioning. We transfected neurons with adeno-associated virus encoding for the genetically encoded calcium indicator GCaMP6s. Spiking events in multiple neurons could be rapidly inferred based on population calcium imaging. Rewards were given to animals in response to inferred events. We found that single neurons in layer 2/3 of motor cortex could be trained to increase activity in a specific manner, and that this increase primes operant conditioning of the same neurons over subsequent days. By identifying the neurons that trigger operant conditioning, the approach we have introduced should be useful for localizing plastic changes and determining the parameters that lead to these plastic changes in the dynamics of neuronal populations during learning. This approach may also provide a framework for training optically based brain-machine interfaces.

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