Seminar Papers Pharmacy Presentations technical IEEE ppts topics abstract on Embedded Spiking Neural Network (Seminar Paper Presentations)
Abstract:Neural networks are computational models of the brain. These networks are good 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 occurs 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. In addition they are computationally stronger than classic neural networks.
Abstract:Neural networks are computational models of the brain. These networks are good 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 occurs 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. In addition they are computationally stronger than classic neural networks.
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