Predicting temporal sequences using an event-based spiking neural network incorporating learnable delays

This paper presents a novel paradigm for a spiking neural network to forecast temporal sequences. The key to the approach is a new model of a spiking neuron that can make multi-step predictions, using learnable temporal delays at both dendrites and axons. This model is able to learn the temporal structure of space-time events, adaptable to multiple scales, with the neurons able to function asynchronously to predict future events in a video sequence. This approach contrasts with conventional neural network approaches that use fixed time steps and iterative prediction. Simulations were conducted to compare the new model to a conventional iterative paradigm on motion sequences from a frame-free event-driven Dynamic Vision Sensor (DVS128, 16k pixels), showing that the new approach consistently has a low prediction error while the iterative paradigm is affected by propagated errors.