Learning and adaptation in a recurrent model of V1 orientation selectivity

Learning and adaptation in a recurrent model of V1 orientation selectivity
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DOI:
10.1152/jn.00970.2002
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发表时间:
2003-04-01
影响因子:
2.5
通讯作者:
Qian, N
Qian, N
中科院分区:
医学3区
文献类型:
--
作者:
Teich, AF;Qian, N

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取向加工领域的学习与适应是目前研究最多的课题之一。然而,很少有人致力于通过基于生理学的模型来解释各种各样的实验结果。我们已经开始在V1定向选择性循环模型的框架下解决这个问题,并发现报告的V1定向调整曲线在学习和适应后的变化都可以用该模型来解释。具体来说,学习后在训练取向附近的取向调谐曲线的锐化可以通过稍微减少与训练取向周围细胞的净兴奋连接来解释,而适应后调谐曲线的增宽和峰移可以通过适当缩小适应取向周围的兴奋和抑制来再现。此外,我们利用信号检测理论研究了由学习和适应引起的调谐曲线变化的感知后果。我们发现,在学习的情况下,生理变化可以很好地解释心理物理数据。然而,在适应的情况下,清醒的人类受试者的心理物理数据与麻醉的动物的生理数据之间存在明显的差异。相反,人类适应研究可以通过行为动物的学习数据来更好地解释。我们的研究表明,行为主体的适应可能被视为一种短期的学习形式。
Learning and adaptation in the domain of orientation processing are among the most studied topics in the literature. However, little effort has been devoted to explaining the diverse array of experimental findings via a physiologically based model. We have started to address this issue in the framework of the recurrent model of V1 orientation selectivity and found that reported changes in V1 orientation tuning curves after learning and adaptation can both be explained with the model. Specifically, the sharpening of orientation tuning curves near the trained orientation after learning can be accounted for by slightly reducing net excitatory connections to cells around the trained orientation, while the broadening and peak shift of the tuning curves after adaptation can be reproduced by appropriately scaling down both excitation and inhibition around the adapted orientation. In addition, we investigated the perceptual consequences of the tuning curve changes induced by learning and adaptation using signal detection theory. We found that in the case of learning, the physiological changes can account for the psychophysical data well. In the case of adaptation, however, there is a clear discrepancy between the psychophysical data from alert human subjects and the physiological data from anesthetized animals. Instead, human adaptation studies can be better accounted for by the learning data from behaving animals. Our work suggests that adaptation in behaving subjects may be viewed as a short-term form of learning.