Abstract. Artificial neural networks have been well developed so far. First two generations of neural networks have had a lot of successful applications. Spiking Neuron Networks (SNNs) are often referred to as the third generation of neural networks which have potential to solve problems related to biological stimuli. They derive their strength and interest from an accurate modeling of synaptic interactions between neurons, taking into account the time of spike emission.SNNs overcome the computational power of neural networks made of threshold or sigmoidal units. Based on dynamic event-driven processing, they open up new horizons for developing models with an exponential capacity of memorizing and a strong ability to fast adaptation. Moreover, SNNs add a new dimension, the temporal axis, to the representation capacity and the processing abilities of neural networks. In this chapter, we present how SNN can be applied with efficacy in image clustering, segmentation and edge detection. Results obtained confirm the validity of the approach.

No abstract

No abstract

This paper depicts the restructuring of different models of third generation of Artificial neural network ,that is, the spiking neural networks for image processing applications. The proposed work aims towards implementation of a novel algorithm using different models of Spiking Neural Networks which will improve upon the optimization results in the field of image processing . In this paper, we focus on various evaluation parameters like mean square error, mean absolute error peak signal to noise ratios as well as enhance the output using ANN as wellas Leaky Integrate and firing Model of Spiking Neural Networks .

No abstract