Hipp Joerg, Arabzadeh Ehsan, Zorzin Erik, Conradt Jorg, Kayser Christoph, Diamond Mathew E, König Peter
Institute of Neuroinformatics, University/ETH Zurich, Winterthurerstrasse 190, 8057 Zurich, Switzerland.
J Neurophysiol. 2006 Mar;95(3):1792-9. doi: 10.1152/jn.01104.2005. Epub 2005 Dec 7.
Rodents excel in making texture judgments by sweeping their whiskers across a surface. Here we aimed to identify the signals present in whisker vibrations that give rise to such fine sensory discriminations. First, we used sensors to capture vibration signals in metal whiskers during active whisking of an artificial system and in natural whiskers during whisking of rats in vivo. Then we developed a classification algorithm that successfully matched the vibration frequency spectra of single trials to the texture that induced it. For artificial whiskers, the algorithm correctly identified one texture of eight alternatives on 40% of trials; for in vivo natural whiskers, the algorithm correctly identified one texture of five alternatives on 80% of trials. Finally, we asked which were the key discriminative features of the vibration spectra. Under both artificial and natural conditions, the combination of two features accounted for most of the information: The modulation power-the power of the part of the whisker movement representing the modulation due to the texture surface-increased with the coarseness of the texture; the modulation centroid-a measure related to the center of gravity within the power spectrum-decreased with the coarseness of the texture. Indeed, restricting the signal to these two parameters led to performance three-fourths as high as the full spectra. Because earlier work showed that modulation power and centroid are directly related to neuronal responses in the whisker pathway, we conclude that the biological system optimally extracts vibration features to permit texture classification.
啮齿动物通过将胡须扫过表面来出色地进行质地判断。在这里,我们旨在识别胡须振动中产生这种精细感官辨别的信号。首先,我们使用传感器在人工系统的主动胡须运动过程中捕获金属胡须中的振动信号,以及在大鼠活体胡须运动过程中捕获自然胡须中的振动信号。然后,我们开发了一种分类算法,该算法成功地将单个试验的振动频谱与诱发它的质地相匹配。对于人工胡须,该算法在40%的试验中正确识别出八种备选质地中的一种;对于活体自然胡须,该算法在80%的试验中正确识别出五种备选质地中的一种。最后,我们询问振动频谱的关键判别特征是什么。在人工和自然条件下,两种特征的组合占了大部分信息:调制功率——胡须运动中代表因质地表面而产生调制的部分的功率——随着质地粗糙度的增加而增加;调制质心——与功率谱内重心相关的一种度量——随着质地粗糙度的增加而减小。事实上,将信号限制在这两个参数上导致的性能高达完整频谱的四分之三。因为早期的研究表明调制功率和质心与胡须通路中的神经元反应直接相关,所以我们得出结论,生物系统最佳地提取振动特征以进行质地分类。