A survey of FPGA implementations of Artificial Spiking Neurons Models
-
Michał Maślanka
und
Marek Gorgoń
Abstract
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.
©Jagiellonian University, Medical College, Kraków, Poland, 2012
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- User Response to the Simulation of a Virtual Patient with Cranial Nerve Injury
- Automatic Protein Abbreviations Discovery and Resolution from Full-Text Scientific Papers: The PRAISED Framework
- PROTEIN FOLD CLASSIFICATION BASED ON MACHINE LEARNING PARADIGM - A REVIEW
- A survey of FPGA implementations of Artificial Spiking Neurons Models
- Computer modelling of pentoxifylline release from a cylinder-shaped hydroxylapatite carrier
- HEXAPOD - SIX-LEGGED WALKING ROBOT CONTROLLED WITH TODA-RAYLEIGH LATTICE
- Realistic model of spine geometry in the human skeleton in the Vicon system
- Use and Usability of Health related E-services among the Senior Citizens
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- Research on the changes in voice quality caused by tonsillectomy
