  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.
