Show Reference: "Transmission of population-coded information."

Transmission of population-coded information. Neural Computation, Vol. 24, No. 2. (24 February 2012), pp. 391-407, doi:10.1162/neco_a_00227 by Alfonso Renart, Mark C. van Rossum
    abstract = {As neural activity is transmitted through the nervous system, neuronal noise degrades the encoded information and limits performance. It is therefore important to know how information loss can be prevented. We study this question in the context of neural population codes. Using Fisher information, we show how information loss in a layered network depends on the connectivity between the layers. We introduce an algorithm, reminiscent of the water filling algorithm for Shannon information that minimizes the loss. The optimal connection profile has a center-surround structure with a spatial extent closely matching the neurons' tuning curves. In addition, we show how the optimal connectivity depends on the correlation structure of the trial-to-trial variability in the neuronal responses. Our results explain how optimal communication of population codes requires the center-surround architectures found in the nervous system and provide explicit predictions on the connectivity parameters.},
    author = {Renart, Alfonso and van Rossum, Mark C.},
    day = {24},
    doi = {10.1162/neco\_a\_00227},
    issn = {1530-888X},
    journal = {Neural Computation},
    keywords = {ann, information\_theory, math, model, population-coding, probability},
    month = feb,
    number = {2},
    pages = {391--407},
    pmid = {22023200},
    posted-at = {2012-09-24 09:00:25},
    priority = {2},
    publisher = {MIT Press},
    title = {Transmission of population-coded information.},
    url = {\_a\_00227},
    volume = {24},
    year = {2012}

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It's hard to unambiguously interpret Ma et al.'s paper, but it seems that, according to Renart and van Rossum, any other non-flat profile would also transmit the information optimally, although the decoding scheme would maybe have to be different.

Renart and van Rossum discuss optimal connection weight profiles between layers in a feed-forward neural network. They come to the conclusion that, if neurons in the input population have broad tuning curves, then Mexican-hat-like connectivity profiles are optimal.

Renart and van Rossum state that any non-flat connectivity profile between input and output layers in a feed-forward network yields optimal transmission if there is no noise in the output.