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Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds

Abstract

We present a general approach to the reconstruction of sensory stimuli encoded with leaky integrate-and-fire neurons with random thresholds. The stimuli are modeled as elements of a Reproducing Kernel Hilbert Space. The reconstruction is based on finding a stimulus that minimizes a regularized quadratic optimality criterion. We discuss in detail the reconstruction of sensory stimuli modeled as absolutely continuous functions as well as stimuli with absolutely continuous first-order derivatives. Reconstruction results are presented for stimuli encoded with single as well as a population of neurons. Examples are given that demonstrate the performance of the reconstruction algorithms as a function of threshold variability.

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Correspondence to Eftychios A. Pnevmatikakis.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Lazar, A.A., Pnevmatikakis, E.A. Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds. EURASIP J. Adv. Signal Process. 2009, 682930 (2009). https://doi.org/10.1155/2009/682930

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Keywords

  • Hilbert Space
  • Continuous Function
  • Information Technology
  • Quantum Information
  • Optimality Criterion