  For energy-efficient computation in specialized neuromorphic hardware, we
present spiking neural coding, an instantiation of a family of artificial
neural models grounded in the theory of predictive coding. This model, the
first of its kind, works by operating in a never-ending process of
"guess-and-check", where neurons predict the activity values of one another and
then adjust their own activities to make better future predictions. The
interactive, iterative nature of our system fits well into the continuous time
formulation of sensory stream prediction and, as we show, the model's structure
yields a local synaptic update rule, which can be used to complement or as an
alternative to online spike-timing dependent plasticity. In this article, we
experiment with an instantiation of our model consisting of leaky
integrate-and-fire units. However, the framework within which our system is
situated can naturally incorporate more complex neurons such as the
Hodgkin-Huxley model. Our experimental results in pattern recognition
demonstrate the potential of the model when binary spike trains are the primary
paradigm for inter-neuron communication. Notably, spiking neural coding is
competitive in terms of classification performance and experiences less
forgetting when learning from task sequence, offering a more computationally
economical, biologically-plausible alternative to popular artificial neural
networks.
