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Ji Won Bang, Yuka Sasaki, Takeo Watanabe, Dobromir Rahnev; Evidence for awake replay in human visual cortex after training. Journal of Vision 2017;17(10):35. doi: https://doi.org/10.1167/17.10.35.
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© ARVO (1962-2015); The Authors (2016-present)
Understanding how the human brain learns is a fundamental goal of neuroscience. A large body of animal research shows that awake replay -- the repetition of neuronal patterns exhibited during learning -- plays a critical role in memory formation. However, very few studies have tested whether awake replay occurs in humans and none have employed non-hippocampus-dependent tasks. Here, we examined whether awake replay occurs in the human visual cortex immediately after extensive training on a visual task. We trained participants on one of two Gabor patch orientations (45° vs. 135°) using a two-interval forced choice (2IFC) detection task. Critically, using functional MRI, we obtained participants' spontaneous brain activity both before and after the vision training. We then tested whether the post-training spontaneous activity in early visual cortex appeared more similar to the trained than untrained stimulus (to classify the spontaneous activity we first constructed a decoder that could distinguish the patterns of activity for each Gabor orientation). Consistent with the existence of awake replay, we found that immediately after vision training, the activation patterns in areas V1 and V3 were more likely to be classified as the trained orientation. No such difference was found for the pre-training spontaneous activity. In addition, behavioral performance on the trained orientation significantly improved after training demonstrating the effectiveness of the training. Taken together, these results demonstrate that a process of awake replay occurs immediately after visual learning. Our findings are the first to demonstrate the phenomenon of awake replay in non-hippocampus-dependent tasks. We speculate that awake replay may be fundamental to all types of learning.
Meeting abstract presented at VSS 2017
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