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}
}

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The SOM can be modified to take into account the variance of the input dimensions wrt. each other.

Zhou et al. use an approach similar to that of Bauer et al. They do not use pairwise cross-correlation between input modalities, but simply variances of individual modalities. It is unclear how they handle the case where one modality essentially becomes ground truth to the algorithm.

A SOM models a population and each unit has a response to a stimulus; it is therefore possible to read out a population code from a SOM. This population code is not very meaningful in the standard SOM. Given a more statistically motivated distance function, the population code can be made more meaningful.

Zhou et al.'s and Bauer et al.'s statistical SOM variants assume Gaussian noise in the input.