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Full Paper on Embedded spiking Neural Networks

Embedded spiking neural networks pdf full presentation paper journal ppt
Abstract: Abstract|In this paper we introduce the ongoing research at our department concerning hardware implementations of spiking neural networks on embedded systems. Our goal is to implement a spiking neural network in reconfigurable hardware, more specifically embedded systems. Keywords| Hardware neural networks, embedded sys- tems, spike coding

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ieee Handwritten Character Recognition of South Indian Scripts using Neural networks paper full

This Paper is Submitted by R.Arun Kumar (Chaitanya Bharathi Institute of Technology) Paper Presentations ppts topics with full doc abstract on Handwritten Character Recognition of South Indian Scripts using Neural networks (Seminar Paper Presentations)
Abstract:Handwritten character recognition is always a frontier area of research in the field of pattern recognition and image processing and there is a large demand for OCR on hand written documents. Even though, sufficient studies have performed in foreign scripts like Chinese, Japanese and Arabic characters, only a very few work can be traced for handwritten character recognition of Indian scripts especially for the South Indian scripts. This paper provides an overview of offline handwritten character recognition in South Indian Scripts, namely Malayalam, Tamil, Kannada and Telugu. Keywords: Handwritten character recognition, south Indian script, Malayalam, Tamil, Kannada, Telugu.

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Artificial Neural Networks seminar topic full

Seminar Papers Presentations IEEE ppt topics abstract on Artificial Neural Networks (Seminar Paper Presentations)
Abstract: Neural networks, as used in artificial intelligence, have traditionally been viewed as simplified models of neural processing in the brain, even though the relation between this model and brain biological architecture is debated.
A subject of current research in theoretical neuroscience is the question surrounding the degree of complexity and the properties that individual neural elements should have to reproduce something resembling animal intelligence.
Historically, computers evolved from the von Neumann architecture, which is based on sequential processing and execution of explicit instructions. On the other hand, the origins of neural networks are based on efforts to model information processing in biological systems, which may rely largely on parallel processing as well as implicit instructions based on recognition of patterns of 'sensory' input from external sources.

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Semiar topic on Embedded Spiking Neural Networks papers

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.

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