A new family of self-organizing maps, the Winner-Relaxing Kohonen Algorithm, is introduced as a generalization of a variant given by Kohonen in 1991. The magnification behaviour is calculated analytically. For the original variant a magnification exponent of 4/7 is derived; the generalized version allows to steer the magnification in the wide range from exponent 1/2 to 1 in the one-dimensional ...

Self-Organizing Feature-Mapping (SOFM) algorithm is frequently used for visualization of high-dimensional (input) data in a lower-dimensional (target) space. This algorithm is based on adaptation of parameters in local neighborhoods and therefore does not lead to the best global visualization of the input space data clusters. SOFM is compared here with alternative methods of global visualizatio...

The Self-Organizing Map (SOM) with its related extensions is the most popular artificial neural algorithm for use in unsupervised learning, clustering, classification and data visualization. Over 5000 publications have been reported in the open literature, and many commercial projects employ the SOM as a tool for solving hard real-world problems. Every two years, the “Workshop on Self-Organizin...

Image segmentation is a very crucial step in the field of image processing which helps us to simplify the representation of the image, to make it easier to analyze. This paper deals with the comparison of image segmentation techniques based on unsupervised artificial neural network technique, known as Kohonen’s Self Organizing Maps (SOM). We first present image segmentation using Kohonen’s Self...

Nowadays, one of the main challenges in computer architectures is scalability; indeed, novel processor can include thousands processing elements on a single chip and using them efficiently remains big issue. An interesting source inspiration for handling scalability mammalian brain different works neuromorphic computation have attempted to address this question. The Self-configurable 3D Cellula...