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Methods of Spiking Neural Networks

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Book cover Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence

Part of the book series: Springer Series on Bio- and Neurosystems ((SSBN,volume 7))

Abstract

Spiking neural networks (SNN) are biologically inspired ANN where information is represented as binary events (spikes), similar to the event potentials in the brain, and learning is also inspired by principles in the brain. SNN are also universal computational mechanisms (Maass in Math Found Comput Sci 1998, 72–83, 1998 [1]). These and many other reasons that are discussed in this chapter make SNN a preferred computational paradigm for modelling temporal and spatio-temporal data and for building brain-inspired AI. This chapter gives the background information for SNN that is further used in the rest of the book.

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Acknowledgements

Along with traditional SNN models, this chapter presents original work by the author on probabilistic spiking neurons and neurogenetic spiking neurons, the latter being developed in collaboration with L. Benuskova [36]. The original work of spike-pattern association neurons (SPAN) was a team work by A. Mohemmed, S. Schliebs, S. Matsuda and the author [56, 57]. I acknowledge the contribution to the presentation of the spike encoding algorithms of Neelave Sengupta [70]. The software for the selection and parameter optimisation of spike encoding algorithm was developed by Balint Petro and available from: https://kedri.aut.ac.nz/R-and-D-Systems/neucube (Spiker).

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    Nikola K. Kasabov

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Correspondence to Nikola K. Kasabov .

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Kasabov, N.K. (2019). Methods of Spiking Neural Networks. In: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence . Springer Series on Bio- and Neurosystems, vol 7. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-57715-8_4

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