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Texture Signals in Whisker Vibrations

¹University/ETH Zurich, Institute of Neuroinformatics, Zurich, Switzerland.; ²International School for Advanced Studies, Cognitive Neuroscience Sector, Trieste, Italy; ³Max Planck Institute for Biological Cybernetics, Physiology of Cognitive Processes, Tubingen, Germany; and ⁴University of Osnabruck, Institute of Cognitive Science, Department of Neurobiopsychology, Osnabruck, Germany

Submitted 19 October 2005; accepted in final form 29 November 2005

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.

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