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Characterizing the fine structure of a neural sensory code through information distortion

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Abstract

We present an application of the information distortion approach to neural coding. The approach allows the discovery of neural symbols and the corresponding stimulus space of a neuron or neural ensemble simultaneously and quantitatively, making few assumptions about the nature of either code or relevant features. The neural codebook is derived by quantizing sensory stimuli and neural responses into small reproduction sets, and optimizing the quantization to minimize the information distortion function. The application of this approach to the analysis of coding in sensory interneurons involved a further restriction of the space of allowed quantizers to a smaller family of parametric distributions. We show that, for some cells in this system, a significant amount of information is encoded in patterns of spikes that would not be discovered through analyses based on linear stimulus-response measures.

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Acknowledgements

We would like to thank the two anonymous reviewers for their dedicated work and extremely useful comments that lead to the improvement of this article, and to Dr. John Miller for useful comments and hosting us at the Center for Computational Biology of Montana State University throughout most of the development of this work.

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Authors and Affiliations

  1. Department of Mathematics and WSU Vancouver Science Programs, Washington State University, Vancouver, WA, 98686, USA

    Alexander G. Dimitrov & Graham I. Cummins

  2. Swiss Re, 1114 Avenue of the Americas, New York, NY, 10036, USA

    Aditi Baker

  3. Department of Biology, University of Washington, PO Box 351800, Seattle, WA, 98195, USA

    Zane N. Aldworth

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  1. Alexander G. Dimitrov
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Correspondence to Alexander G. Dimitrov.

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Dimitrov, A.G., Cummins, G.I., Baker, A. et al. Characterizing the fine structure of a neural sensory code through information distortion. J Comput Neurosci 30, 163–179 (2011). https://doi.org/10.1007/s10827-010-0261-4

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