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Low dimensional components of the neural dynamics during vis...

Low dimensional components of the neural dynamics during visual perceptual learning

作     者:Malte J.Rasch Yin Yan Wu Si Li Wu 

会议名称:《中国神经科学学会第十届全国学术会议》

会议日期:2013年

学科分类:1001[医学-基础医学(可授医学、理学学位)] 10[医学] 

摘      要:Perceptual learning is an important form of adaption in sensory systems and *** in the neural dynamics in response to repetitively solving a visual task has been shown to occur in the visual cortex of awake ***,how the modifications of the temporal dynamics of a network of many neurons can be understood remains vastly ***,we record simultaneously from a large number of neurons in the visual cortex of awake monkeys while performing a visual pattern detection *** monkey has to find an elongated contour in one of two presented stimuli consisting of otherwise randomly oriented short *** spiking responses were recorded over a time of 2-3 weeks of learning to perform this *** using components analysis and dimensional reduction techniques derived from the machine learning community,we first show that the high dimensional neural population responses can be described by the dynamics of only two main components.(1) A facilitatory component involving cells having receptive fields nearby the contour to be detected,and(2) a suppressive component involving cells having receptive fields in the *** describe further the temporal response dynamics of these components and find that their strength depends on the length of the contour to be detected and that suppressive activity systematically lags the facilatory ***,we found that the weight of these components change during the course of *** weighting of both components is increased over the days and correlates well with performance of the *** better understand the mechanism of the learning induced network changes we devised a simple neuron network *** found that a simple rate-based model can replicate the general dynamics of the two components thereby suggesting a neural mechanism for how perceptual learning might induced changes to the network activity.

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