Reconstruction of sensory stimuli encoded with integrate-and-fire neurons with random thresholds

Authors:
Aurel A. Lazar;Eftychios A. Pnevmatikakis
Affiliations:
Department of Electrical Engineering, Columbia University, New York, NY;Department of Electrical Engineering, Columbia University, New York, NY
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience
Year:
2009

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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.