It's not quite correct to say this is only for achieving deep learning. Gradient-based parameter optimisation is still a useful tool, even for small shallow networks that would be ideal for event-based signal processing.
Even for small-network tasks, training spiking networks has been non-trivial. This paper provides a way to get exact gradients, implying probably faster optimisation than using surrogate gradients or other approximation methods for SNNs.
Even for small-network tasks, training spiking networks has been non-trivial. This paper provides a way to get exact gradients, implying probably faster optimisation than using surrogate gradients or other approximation methods for SNNs.