Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition.

ABSTRACT: 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 recogniti

SUBMITTER: Bouanane MS 

PROVIDER: S-EPMC10704147 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature