Reconstruction of Sensory Stimuli Encoded with Integrate-and-Fire Neurons with Random Thresholds.
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.
Department of Electrical Engineering, Columbia University, New York, NY 10027, USA.
Aurel A
AA
Lazar
Eftychios A
EA
Pnevmatikakis