  Neural coupled oscillators are a useful building block in numerous models and
applications. They were analyzed extensively in theoretical studies and more
recently, in biologically realistic simulations of spiking neural networks. The
advent of mixed-signal analog/digital neuromorphic electronic circuits provides
new means for implementing neural coupled oscillators on compact low-power
spiking neural network hardware platforms. However, their implementation on
this noisy, low-precision and inhomogeneous computing substrate raises new
challenges with regards to stability and controllability. In this work, we
present a robust, spiking neural network model of neural coupled oscillators
and validate it with an implementation on a mixed-signal neuromorphic
processor. We demonstrate its robustness showing how to reliably control and
modulate the oscillator's frequency and phase shift, despite the variability of
the silicon synapse and neuron properties. We show how this ultra-low power
neural processing system can be used to build an adaptive cardiac pacemaker
modulating the heart rate with respect to the respiration phases and compare it
with surface ECG and respiratory signal recordings of dogs at rest. The
implementation of our model in neuromorphic electronic hardware shows its
robustness on a highly variable substrate and extends the toolbox for
applications requiring rhythmic outputs such as pacemakers.
