Encoding and Decoding Mixed Bandlimited Signals Using Spiking Integrate-and-Fire Neurons
Karen Adam, Adam Scholefield, Martin Vetterli
Abstract
Conventional sampling focuses on encoding and decoding bandlimited signals by recording signal amplitudes at known time points. Alternately, sampling can be approached using biologically-inspired schemes. Among these are integrate- and-fire time encoding machines (IF-TEMs). They behave like simplified versions of spiking neurons and encode their input using spike times rather than amplitudes. When multiple of these neurons jointly process a set of mixed signals, they form one layer in a feedforward spiking neural network. In this paper, we investigate the encoding and decoding potential of such a layer. We propose a setup to sample a set of bandlimited signals formed by summing a finite number of sincs, by mixing them and sampling the result using different IF-TEMs. We provide conditions for perfect recovery of the set of signals from the samples in the noiseless case, and suggest an algorithm to perform the reconstruction.
BibTeX
@inproceedings{icassp2020_encodinganddecod,
title = {Encoding and Decoding Mixed Bandlimited Signals Using Spiking Integrate-and-Fire Neurons},
author = {Karen Adam and Adam Scholefield and Martin Vetterli},
booktitle = {ICASSP 2020},
year = {2020}
}