Exploration of spiking neurones leakages and network recurrences for spike-based-temporal pattern recognition.

Abstract

Brain-inspired computing is being explored to imitate the astonishing capabilities of biological brains to perform robust and efficient computations. To map these capa- bilities into hardware, a growing number of neuromorphic computers are being built to emulate biological neural networks. These developments created a need to ad- dress the lack in understanding of different neuronal behaviours that can enable us to find the right level of abstraction from biology and get the best performance in accurate, efficient and fast inference. Aiming at addressing this problem, we give a detailed overview of the concerned spiking neuron models and surrogate gradient methods, which are used to study the impact of synaptic and membrane leakages in feed-forward, as well as recurrent network topologies on learning visual and auditory information. We also investigate whether or not heterogeneity at the neuronal level plays a functional learning role. We found out that leakages are important when we have both temporal information and a recurrently connected topology. We also found that heterogeneity slightly improves performance on temporal information. The re- sults we obtained will provide more insight on developing efficient neuromorphic hardware.