# Sampling and Reconstruction of Bandlimited Signals with Multi-Channel Time Encoding

## TL;DR

This paper demonstrates that multi-channel time encoding can perfectly reconstruct bandlimited signals without knowing the shifts between channels, offering a more natural and robust sampling method inspired by biological neurons.

## Contribution

It introduces a multi-channel time encoding framework that allows perfect signal reconstruction without shift knowledge, extending classical sampling theory to biologically inspired methods.

## Key findings

- Multi-channel time encoding reconstructs signals with M times the bandwidth.
- Reconstruction algorithm converges in noiseless conditions without shift information.
- Multi-channel approach simplifies and improves robustness over classical sampling.

## Abstract

Sampling is classically performed by recording the amplitude of an input signal at given time instants; however, sampling and reconstructing a signal using multiple devices in parallel becomes a more difficult problem to solve when the devices have an unknown shift in their clocks. Alternatively, one can record the times at which a signal (or its integral) crosses given thresholds. This can model integrate-and-fire neurons, for example, and has been studied by Lazar and T\'oth under the name of ``Time Encoding Machines''. This sampling method is closer to what is found in nature. In this paper, we show that, when using time encoding machines, reconstruction from multiple channels has a more intuitive solution, and does not require the knowledge of the shifts between machines. We show that, if single-channel time encoding can sample and perfectly reconstruct a $\mathbf{2\Omega}$-bandlimited signal, then $\mathbf{M}$-channel time encoding with shifted integrators can sample and perfectly reconstruct a signal with $\mathbf{M}$ times the bandwidth. Furthermore, we present an algorithm to perform this reconstruction and prove that it converges to the correct unique solution, in the noiseless case, without knowledge of the relative shifts between the integrators of the machines. This is quite unlike classical multi-channel sampling, where unknown shifts between sampling devices pose a problem for perfect reconstruction.