  Biological spiking neural networks (SNNs) can temporally encode information
in their outputs, e.g. in the rank order in which neurons fire, whereas
artificial neural networks (ANNs) conventionally do not. As a result, models of
SNNs for neuromorphic computing are regarded as potentially more rapid and
efficient than ANNs when dealing with temporal input. On the other hand, ANNs
are simpler to train, and usually achieve superior performance. Here we show
that temporal coding such as rank coding (RC) inspired by SNNs can also be
applied to conventional ANNs such as LSTMs, and leads to computational savings
and speedups. In our RC for ANNs, we apply backpropagation through time using
the standard real-valued activations, but only from a strategically early time
step of each sequential input example, decided by a threshold-crossing event.
Learning then incorporates naturally also *when* to produce an output, without
other changes to the model or the algorithm. Both the forward and the backward
training pass can be significantly shortened by skipping the remaining input
sequence after that first event. RC-training also significantly reduces
time-to-insight during inference, with a minimal decrease in accuracy. The
desired speed-accuracy trade-off is tunable by varying the threshold or a
regularization parameter that rewards output entropy. We demonstrate these in
two toy problems of sequence classification, and in a temporally-encoded MNIST
dataset where our RC model achieves 99.19% accuracy after the first input
time-step, outperforming the state of the art in temporal coding with SNNs, as
well as in spoken-word classification of Google Speech Commands, outperforming
non-RC-trained early inference with LSTMs.
