  Spiking neural networks coupled with neuromorphic hardware and event-based
sensors are getting increased interest for low-latency and low-power inference
at the edge. However, multiple spiking neuron models have been proposed in the
literature with different levels of biological plausibility and different
computational features and complexities. Consequently, there is a need to
define the right level of abstraction from biology in order to get the best
performance in accurate, efficient and fast inference in neuromorphic hardware.
In this context, we explore the impact of synaptic and membrane leakages in
spiking neurons. We confront three neural models with different computational
complexities using feedforward and recurrent topologies for event-based visual
and auditory pattern recognition. Our results show that, in terms of accuracy,
leakages are important when there are both temporal information in the data and
explicit recurrence in the network. In addition, leakages do not necessarily
increase the sparsity of spikes flowing in the network. We also investigate the
impact of heterogeneity in the time constant of leakages, and the results show
a slight improvement in accuracy when using data with a rich temporal
structure. These results advance our understanding of the computational role of
the neural leakages and network recurrences, and provide valuable insights for
the design of compact and energy-efficient neuromorphic hardware for embedded
systems.
