  Spiking neural networks can compensate for quantization error by encoding
information either in the temporal domain, or by processing discretized
quantities in hidden states of higher precision. In theory, a wide dynamic
range state-space enables multiple binarized inputs to be accumulated together,
thus improving the representational capacity of individual neurons. This may be
achieved by increasing the firing threshold, but make it too high and sparse
spike activity turns into no spike emission. In this paper, we propose the use
of `threshold annealing' as a warm-up method for firing thresholds. We show it
enables the propagation of spikes across multiple layers where neurons would
otherwise cease to fire, and in doing so, achieve highly competitive results on
four diverse datasets, despite using binarized weights. Source code is
available at https://github.com/jeshraghian/snn-tha/
