Abstract
Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks. Highly inspired by natural computing in the brain and recent advances in neurosciences, they derive their strength and interest from an accurate 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 develo** models with an exponential capacity to memorize and a strong ability to do fast adaptation. Today, the main challenge is to discover efficient learning rules that might take advantage of the specific features of SNNs while kee** the nice properties (general-purpose, easy-to-use, available simulators, etc.) of traditional connectionist models. This chapter relates the history of the “spiking neuron” in Sect. 1 and summarizes the most currently-in-use models of neurons and synaptic plasticity in Sect. 2. The computational power of SNNs is addressed in Sect. 3 and the problem of learning in networks of spiking neurons is tackled in Sect. 4, with insights into the tracks currently explored for solving it. Finally, Sect. 5 discusses application domains, implementation issues and proposes several simulation frameworks.
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Paugam-Moisy, H., Bohte, S. (2012). Computing with Spiking Neuron Networks. In: Rozenberg, G., Bäck, T., Kok, J.N. (eds) Handbook of Natural Computing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92910-9_10
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