Volume 10 Supplement 1

Eighteenth Annual Computational Neuroscience Meeting: CNS*2009

Open Access

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

BMC Neuroscience200910(Suppl 1):P62

DOI: 10.1186/1471-2202-10-S1-P62

Published: 13 July 2009

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.

Authors’ Affiliations

(1)
Department of Computer Science and Engineering, Texas A&M University

References

  1. Miikkulainen R, Bednar JA, Choe Y, Sirosh J: Computational Maps in the Visual Cortex. 2005, New York: SpringerGoogle Scholar
  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. 2007Google Scholar

Copyright

© Park et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.

Advertisement