14-05-2012, 12:45 PM
pulse-coupled neural network (PCNN)
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INTRODUCTION
During the last few years there was a shift of the emphasis in the artificial neural network community toward spiking or pulse-coupled neural networks. Motivated by biological discoveries, many studies consider pulse coupled neural networks with spike-timing as an essential component in information processing by the brain.
Pulse-coupled neural networks (PCNN) were introduced as a simple model for the cortical neurons in the visual area of the cat's brain.These neural models are proposed by Eckhorn and Johnson.
. The essential model of PCNN, is described with details, that can be implemented to perform a number of digital image processing applications.
WHAT IS PCNN ?
A PCNN is a two-dimensional neural network. They are treated as the third generation of NN models, that takes in to account spiking nature of neurons. Each neuron in the processing layer is directly tied to an image pixel or a set of neighboring image pixels, the two linking and feeding inputs are iteratively processed and together to produce a pulse image with features, that can be changed by varying the PCNN parameters.
WHY WE USE PCNN ?
In the field of digital image processing and pattern recognition , traditional models are
either subject to problems determined by geometric transforms (scaling, translation or rotation) or to high computational complexity.
Moreover, it is known today that parallel processing could solve determined by geometric transforms to take advantage of it we need parallelisable models.
Neural models fits this requirement.
STRUCTURE OF PCNN
• The number of neurons in the network is equal to the number of input image. One-to-one correspondence exists between image pixels and neurons.
• Each pixel is connected to a unique neuron and each neuron is connected with the
surrounding neurons with a radius of linking field.
• The neuron receives input signals from other neurons and
from external sources through the receptive fields.