The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we establish an Expanding SOM (ESOM) to detect and preserve better topology correspondence between the two spaces. Our experiment results demonstrate that the ESOM con...

This paper presents a new mutation operator called the Sobol Mutation (SOM) operator for enhancing the performance of Quantum Particle Swarm Optimization (QPSO) algorithm. The SOM operator unlike most of its contemporary mutation operators do not use the random probability distribution for perturbing the swarm population, but uses a quasi random Sobol sequence to find new solution vectors in th...

The self-organizing map (SOM) is a method that represents statistical data sets in an ordered fashion, as a natural groundwork on which the distributions of the individual indicators in the set can be displayed and analyzed. As a case study that instructs how to use the SOM to compare states of economic systems, the standard of living of different countries is analyzed using the SOM. Based on a...

Although the SOM algorithm has been widely used with vectorial data, its principle is not restricted to metric vector spaces. Indeed, any set of items for which a similarity or pseudo-distance measure is available could be mapped onto the SOM grid in an ordered fashion. As Kohonen and Somervuo (2002) pointed out, the optimal speed of shrinking of the neighbourhood range function on nonvectorial...

The Self-Organizing Map (SOM ) is a popular unsupervised neural network able to provide effective clustering and data visualization for data represented in multidimensional input spaces. In this paper we describe Fast Learning SOM (FLSOM ) which adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. We show that ...

One of the attractive features of Self-Organising Maps (SOM) is the so-called “topological preservation property”: observations that are close to each other in the input space (at least locally) remain close to each other in the SOM. In this work, we propose the use of a bootstrap scheme to construct a statistical significance test of the observed proximity among individuals in the SOM. While c...

The Self-Organizing Map (SOM) is one of the most popular neural network methods. It is a powerful tool in visualization and analysis of high-dimensional data in various engineering applications. The SOM maps the data on a two-dimensional grid which may be used as a base for various kinds of visual approaches for clustering, correlation and novelty detection. In this chapter, we present novel me...

The neuropeptide somatostatin (SOM) is widely expressed in rodent brain and somatostatin-IRES-Cre (SOM-cre) mouse strains are increasingly used to unravel the physiology of SOM-containing neurons. However, while knock-in targeting strategy greatly improves Cre-Lox system accuracy, recent reports have shown that genomic insertion of Cre construct per se can markedly affect physiological function...

In this paper, we introduce a new type of information-theoretic method called “information-theoretic active SOM”, based on the self-organizing maps (SOM) for training multi-layered neural networks. The SOM is one of the most important techniques in unsupervised learning. However, SOM knowledge is sometimes ambiguous and cannot be easily interpreted. Thus, we introduce the information-theoretic ...

Somatostatin-expressing inhibitory (SOM) neurons in the sensory cortex consist mostly of Martinotti cells, which project ascending axons to layer 1. Due to their sparse distribution, the representational properties of these neurons remain largely unknown. By two-photon imaging guided cell-attached recordings, we characterized visual response and receptive field (RF) properties of SOM neurons an...