在人工和生物系统中跨模式知识转移的理论框架

Francesco Orabona, B. Caputo, A. Fillbrandt, F. Ohl
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引用次数: 6

摘要

从与不同类型的传感器相关的感觉模式中学习对生物系统来说是至关重要的,因为它允许它们应对复杂的环境,在这些环境中事件很少以相同的方式出现两次。在本文中,我们想研究在生物和人工系统中,在一种模态中形成的感知类别如何转移到另一种模态。我们首先提出了一项关于蒙古沙鼠的研究,该研究显示了从听觉模态到视觉模态的知觉类别知识转移的明确证据。然后,我们引入一种算法,在在线学习框架中模仿啮齿动物的行为。在模拟数据上的实验取得了令人满意的结果,表明了我们的方法的针对性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A theoretical framework for transfer of knowledge across modalities in artificial and biological systems
Learning from sensory patterns associated with different kinds of sensors is paramount for biological systems, as it permits them to cope with complex environments where events rarely appear twice in the same way. In this paper we want to investigate how perceptual categories formed in one modality can be transferred to another modality in biological and artificial systems. We first present a study on Mongolian gerbils that show clear evidence of transfer of knowledge for a perceptual category from the auditory modality to the visual modality. We then introduce an algorithm that mimics the behavior of the rodents within the online learning framework. Experiments on simulated data produced promising results, showing the pertinence of our approach.
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