Approximate reconstruction of bandlimited functions for the integrate and fire sampler

Authors:
Hans G. Feichtinger;José C. Príncipe;José Luis Romero;Alexander Singh Alvarado;Gino Angelo Velasco
Affiliations:
Faculty of Mathematics, University of Vienna, Vienna, Austria 1090;Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA 32611;Departamento de Matemática, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Capital Federal, Argentina 1428 and CONICET, Buenos Aires, Argentina;Department of Electrical and Computer Engineering, University of Florida, Gainesville, USA 32611;Faculty of Mathematics, University of Vienna, Vienna, Austria 1090 and Institute of Mathematics, University of the Philippines, Diliman, Quezon City, Philippines
Venue:
Advances in Computational Mathematics
Year:
2012

Citing 4
Cited 1

Spikes: exploring the neural code

Spikes: exploring the neural code
Spiking Neuron Models: An Introduction

Spiking Neuron Models: An Introduction
Nonuniform Sampling and Reconstruction in Shift-Invariant Spaces

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

EURASIP Journal on Advances in Signal Processing - Special issue on statistical signal processing in neuroscience

A radial basis function spike model for indirect learning via integrate-and-fire sampling and reconstruction techniques

Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper we study the reconstruction of a bandlimited signal from samples generated by the integrate and fire model. This sampler allows us to trade complexity in the reconstruction algorithms for simple hardware implementations, and is specially convenient in situations where the sampling device is limited in terms of power, area and bandwidth. Although perfect reconstruction for this sampler is impossible, we give a general approximate reconstruction procedure and bound the corresponding error. We also show the performance of the proposed algorithm through numerical simulations.