# Show Reference: "Self-Organizing Neural Population Coding for improving robotic visuomotor coordination"

Self-Organizing Neural Population Coding for improving robotic visuomotor coordination In Neural Networks (IJCNN), The 2011 International Joint Conference on (July 2011), pp. 1437-1444, doi:10.1109/ijcnn.2011.6033393 by Tao Zhou, Piotr Dudek, Bertram E. Shi
@inproceedings{zhou-et-al-2011,
abstract = {We present an extension of Kohonen's Self Organizing Map ({SOM}) algorithm called the Self Organizing Neural Population Coding ({SONPC}) algorithm. The algorithm adapts online the neural population encoding of sensory and motor coordinates of a robot according to the underlying data distribution. By allocating more neurons towards area of sensory or motor space which are more frequently visited, this representation improves the accuracy of a robot system on a visually guided reaching task. We also suggest a Mean Reflection method to solve the notorious border effect problem encountered with {SOMs} for the special case where the latent space and the data space dimensions are the same.},
author = {Zhou, Tao and Dudek, Piotr and Shi, Bertram E.},
booktitle = {Neural Networks (IJCNN), The 2011 International Joint Conference on},
doi = {10.1109/ijcnn.2011.6033393},
institution = {Dept. of Electron. \& Comput. Eng., Hong Kong Univ. of Sci. \& Technol., Kowloon, China},
isbn = {978-1-4244-9635-8},
issn = {2161-4393},
keywords = {learning, population-coding, som},
month = jul,
pages = {1437--1444},
posted-at = {2013-02-11 10:13:02},
priority = {2},
publisher = {IEEE},
title = {{Self-Organizing} Neural Population Coding for improving robotic visuomotor coordination},
url = {http://dx.doi.org/10.1109/ijcnn.2011.6033393},
year = {2011}
}