  The biologically inspired spiking neurons used in neuromorphic computing are
nonlinear filters with dynamic state variables -- very different from the
stateless neuron models used in deep learning. The next version of Intel's
neuromorphic research processor, Loihi 2, supports a wide range of stateful
spiking neuron models with fully programmable dynamics. Here we showcase
advanced spiking neuron models that can be used to efficiently process
streaming data in simulation experiments on emulated Loihi 2 hardware. In one
example, Resonate-and-Fire (RF) neurons are used to compute the Short Time
Fourier Transform (STFT) with similar computational complexity but 47x less
output bandwidth than the conventional STFT. In another example, we describe an
algorithm for optical flow estimation using spatiotemporal RF neurons that
requires over 90x fewer operations than a conventional DNN-based solution. We
also demonstrate promising preliminary results using backpropagation to train
RF neurons for audio classification tasks. Finally, we show that a cascade of
Hopf resonators - a variant of the RF neuron - replicates novel properties of
the cochlea and motivates an efficient spike-based spectrogram encoder.
