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

Authors:
Alexander G. Dimitrov;Graham I. Cummins;Aditi Baker;Zane N. Aldworth
Affiliations:
Department of Mathematics and WSU Vancouver Science Programs, Washington State University, Vancouver, USA 98686;Department of Mathematics and WSU Vancouver Science Programs, Washington State University, Vancouver, USA 98686;Swiss Re, New York, USA 10036;Department of Biology, University of Washington, Seattle, USA 98195
Venue:
Journal of Computational Neuroscience
Year:
2011

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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.