A survey of FPGA implementations of artificial spiking neurons models

• Autorzy: Maślanka M. , Gorgoń M.
• Języki publikacji: EN

Abstrakty

EN

Spiking Neural Networks (SNNs) seems to be now the best way to model and simulate brain structures and functions. SNNs give also possibilities to better understanding of mechanism that are responsible for consciousness and abstract thinking. Furthermore they can also change our look on information processing and modern computing. Most common software implementations need great computing power and because of that they are not suitable for real time applications. Additionally, biological neurons process information in parallel which is impossible with simulation on conventional computer. Thus we present alternative way to implement models of SNNs incorporating FPGAs. In this paper we compared most common models that are used to implement SNNs in reconfigurable hardware and also we made review of recent works that were done in this subject.

Słowa kluczowe

EN

Spiking Neural Networks; SNN; FPGA

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2012
• Tom: Vol. 8, no. 1
• Strony: 77--87
• Opis fizyczny: Bibliogr. 14 poz., rys., tab.

Twórcy

autor

Maślanka M.

• AGH University of Science and Technology, Institute of Automatics, Mickiewicza Ave. 30, 30-059 Kraków, Poland

autor

Gorgoń M.

• AGH University of Science and Technology, Institute of Automatics, Mickiewicza Ave. 30, 30-059 Kraków, Poland

Bibliografia

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• 12. Glackin B., McGinnity T.M., Maguire L.P., Wu Q.X., Belatreche A. (2009): Emulating Spiking Neural Networks for edge detection on FPGA hardware, Proc. of Field Programmable Logic and Applications 2009, 670-673.
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