Shao Yitian, Hayward Vincent, Visell Yon
Department of Electrical and Computer Engineering, Media Arts and Technology Program, Department of Mechanical Engineering, and California NanoSystems Institute, University of California, Santa Barbara, Santa Barbara, CA, USA.
Sorbonne Université, Institut des Systèmes Intelligents et de Robotique, F-75005 Paris, France.
Sci Adv. 2020 Apr 15;6(16):eaaz1158. doi: 10.1126/sciadv.aaz1158. eCollection 2020 Apr.
A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.
感官研究中的一个关键问题是描述感觉器官如何从环境物理中提取感知信息。我们之前观察到动态触觉会引发在整个手部传播的机械波。在此，我们表明这些波能对触觉信息进行高效编码。对数千种自然发生的触觉刺激进行最优编码的计算产生了一个紧凑的原始波型词典，该词典稀疏地表示了整个数据集，使得触觉交互的分类准确率超过95%。原始触觉模式反映了手部解剖结构与波动物理学之间的相互作用。值得注意的是，当我们将高效编码标准应用于模拟触觉传入神经元群体的尖峰数据时，出现了类似的模式。这一发现表明，手部的生物力学通过对触觉信息进行神经元前压缩来实现高效的感知处理。
