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This article is part of the supplement: Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access Poster presentation

Textural-input-driven self-organization of tactile receptive fields

Choonseog Park*, Heeyoul Choi and Yoonsuck Choe

Author Affiliations

Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77840, USA

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BMC Neuroscience 2009, 10(Suppl 1):P62  doi:10.1186/1471-2202-10-S1-P62

The electronic version of this article is the complete one and can be found online at:

Published:13 July 2009

© 2009 Park et al; licensee BioMed Central Ltd.

Poster presentation

Sensory neurons in the primary sensory cortices preferentially respond to specific patterns of input. Our hypothesis is that tactile receptive fields (TRFs) can be self-organized using the same cortical development mechanism found in the visual cortex, simply by exposing it to texture-like inputs. We used the LISSOM model of visual cortical development [1] to test our hypothesis. The results showed that texture-like inputs lead to the self-organization of TRFs while natural-scene-like inputs lead to visual receptive fields (VRFs). We analyzed the effectiveness of the TRFs and VRFs in representing texture, using kernel Fisher discriminant analysis (KFD) [2]. The responses to different classes of textural input were more clearly separable for the TRF than for the VRF. To quantify the merit of the different RF types in dealing with textural input, we measured classification performance. We ran the experiment for 30 times and for each experiment 50% of data set were randomly used as training set and the rest as testing set. As a classifier, k-nearest neighbor (kNN) was used. Average classification rates were 89.8% (for TRF-based) and 83.4% (for VRF-based) respectively. The main results suggest that tactile RFs can be self-organized by texture-like input using a general cortical development model (LISSOM) initially inspired by the visual cortex, and that the representations from tactile RFs are better than vision-based ones for texture tasks. We expect our results to help us better understand the nature of texture as a fundamentally tactile property.


  1. Miikkulainen R, Bednar JA, Choe Y, Sirosh J: Computational Maps in the Visual Cortex. New York: Springer; 2005. OpenURL

  2. Khurd P, Baloch S, Gur R, Davatzikos C, Verma R: Manifold learning techniques in image analysis of high-dimensional diffusion tensor magnetic resonance images.

    IEEE Conference on CVPR 2007. OpenURL