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

Lazar, Aurel A.; Pnevmatikakis, Eftychios

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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Also Published In

EURASIP Journal on Advances in Signal Processing

More About This Work

Academic Units
Electrical Engineering
Published Here
September 9, 2014
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