A universal abstract-time platform for real-time neural networks
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Scalable event-driven native parallel processing: the SpiNNaker neuromimetic system
Proceedings of the 7th ACM international conference on Computing frontiers
Scalable communications for a million-core neural processing architecture
Journal of Parallel and Distributed Computing
Hi-index | 0.00 |
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.