Abstract Spiking Neuron Networks (SNNs) are often referred to as the 3rd gener- ation of neural networks. Highly inspired from natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an ac- curate modeling of synaptic interactions between neurons, taking into account the time of spike firing. SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while keeping the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the his- tory of the “spiking neuron” in Section 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Section 2. The computational power of SNNs is addressed in Section 3 and the problem of learning in networks of spiking neurons is tackled in Section 4, with insights into the tracks currently explored for solving it. Finally, Section 5 discusses application domains, implementation issues and proposes several simulation frameworks.
Additional Metadata
Keywords spiking neural networks
THEME Life Sciences (theme 5), Energy (theme 4)
Publisher Springer Verlag
Editor G. Rozenberg , T.H.W. Baeck (Thomas) , J.N. Kok (Joost)
Citation
Paugam-Moisy, H, & Bohte, S.M. (2012). Computing with Spiking Neuron Networks. In G Rozenberg, T.H.W Baeck, & J.N Kok (Eds.), Handbook of Natural Computing. Springer Verlag.