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Bayesian Reconstruction of Perceptual Experiences from Human Brain Activity

机译:来自人脑活动的知觉经验的贝叶斯重建

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A method for decoding the subjective contents of perceptual systems in the human brain would have broad practical utility for communication and as a brain-machine interface. Previous approaches to this problem in vision have used linear classifiers to solve specific problems, but these approaches were not general enough to solve complex problems such as reconstructing subjective perceptual states. We have developed a new approach to these problems based on quantitative encoding models that explicitly describe how visual stimuli are (nonlinearly) transformed into brain activity. We then invert these encoding models in order to decode activity evoked by novel images or movies, providing reconstructions with unprecedented fidelity. Here we briefly review these results and the potential uses of perceptual decoding devices.
机译:一种用于解码人脑中的感知系统的主观内容的方法将具有广泛的通信实用性和作为脑机接口。视觉中此问题的先前方法已使用线性分类器解决特定问题,但这些方法还不足以解决复杂问题,例如重建主观感知状态。我们基于定量编码模型开发了解决这些问题的新方法,该模型明确描述了视觉刺激如何(非线性)转化为大脑活动。然后,我们将这些编码模型求逆,以便对小说图像或电影引起的活动进行解码,从而为重建提供了前所未有的保真度。在这里,我们简要回顾一下这些结果以及感知解码设备的潜在用途。

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