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The Deferred Event Model for Hardware-Oriented Spiking Neural Networks

Alexander Rast, Xin Jin, Mukaram Khan, Steve Furber


Real-time modelling of large neural systems places critical demands on the processing system's dynamic model. With spiking neural networks it is convenient to abstract each spike to a point event. In addition to the representational simplification, the event model confers the ability to defer state updates, if the model does not propagate the effects of the current event instantaneously. Using the SpiNNaker dedicated neural chip multiprocessor as an example system, we develop models for neural dynamics and synaptic learning that delay actual updates until the next input event while performing processing in background between events, using the difference between ``electronic time" and ``neural time" to achieve real-time performance. The model relaxes both local memory and update scheduling requirements to levels realistic for the hardware. The delayed-event model represents a useful way to recast the real-time updating problem into a question of time to the next event.