This paper discusses biological aspects of self-organising maps (SOMs) which includes a brief review of neurophysiological findings and classical models of neurophysiological SOMs. We then discuss some simulation studies on the role of topographic map representation for training mapping networks and on top-down control of map plasticity.

Deafness, hearing, and balance disorders are common worldwide. The inner ear sensory hair cells are the mechanoreceptors that detect sound, head motion, and linear acceleration. Loss of or damage to hair cells is the major cause of hearing and balance disorders in humans. Nonmammalian vertebrates (birds, fish, and amphibians) have the ability to regenerate sensory hair cells, whereas mammals ca...

Self-organizing maps (SOMs) are widely used in several fields of application, from neurobiology to multivariate data analysis. In that context, this paper presents variants of the classic SOM algorithm. With respect to the traditional SOM, the modifications regard the core of the algorithm, (the learning rule), but do not alter the two main tasks it performs, i.e. vector quantization combined w...

We study the application of self-organizing maps (SOMs) for the analyses of remote sensing spectral images. Advanced airborne and satellite-based imaging spectrometers produce very high-dimensional spectral signatures that provide key information to many scientific investigations about the surface and atmosphere of Earth and other planets. These new, sophisticated data demand new and advanced a...

MOTIVATION Sampling the conformational space of biological macromolecules generates large sets of data with considerable complexity. Data-mining techniques, such as clustering, can extract meaningful information. Among them, the self-organizing maps (SOMs) algorithm has shown great promise; in particular since its computation time rises only linearly with the size of the data set. Whereas SOMs ...

This paper presents an application of Kohonen's self-organizing feature maps (SOM) for solving the problem of color constancy. The main problem is to evaluate the transformation between collections of color-points forming diierently shaped clouds in color space under changing illumination. The main idea is to embed appropriate 3D coordinate systems into these clouds by self-organization, and so...

Artificial intelligence is getting a foothold in medicine for disease screening and diagnosis. While typical machine learning methods require large labeled datasets training validation, their application limited clinical fields since ground truth information can hardly be obtained on sizeable cohort of patients. Unsupervised neural networks – such as Self-Organizing Maps (SOMs) represent an alt...