Embedded Spiking Neural Network
Roshni Kishan1, Siri A2, Meghana G. R3, Meghana S4
1Roshni Kishan, Department of Telecommunication Engineering, BMSCE, Bangalore, India.
2Siri A, Department of Telecommunication Engineering, BMSCE, Bangalore, India.
3Meghana G. R, Department of Telecommunication Engineering, BMSCE, Bangalore, India.
4Meghana S, Department of Telecommunication Engineering, BMSCE, Bangalore, India.
Manuscript received on July 24, 2014. | Revised Manuscript received on August 01, 2014. | Manuscript published on August 30, 2014. | PP: 215-217 | Volume-3 Issue-6, August 2014. | Retrieval Number: F3399083614/2013©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: NEURAL networks are computational models of the brain. These networks are excellent at solving problems for which a solution seems easy to obtain for the brain, but requires a lot of efforts using standard algorithmic techniques. Examples of such problems are pattern recognition, perception, generalization and non-linear control. In the brain, all communication between neurons occur using action potentials or spikes. In classical neural models these individual spikes are averaged out in time and all interaction is identified by the mean firing rate of the neurons. Recently there has been an increasing interest in more complex models, which take the individual spikes into account. This sudden interest is catalyzed by the fact that these more realistic models are very well suited for hardware implementations, more specifically embedded systems. In addition they are computationally stronger than classic neural networks.
Keywords: Embedded systems, Neural network, Neurons, spikes.