The self-organizing map (SOM) algorithm produces artificial neural maps by simulating competition and cooperation among neurons. We study the consequences of input background activity on simulated self-organization, using the SOM, of the retinotopic map in the superior colliculus. The colliculus not only represents its inputs but also uses them to localize saccadic targets. Using the colliculus...

The Self-Organizing Map (SOM) is an artificial neural network model based on unsupervised learning. In this paper, the use of the SOM in natural language processing is considered. The main emphasis is on natural features of natural language including contextuality of interpretation, and the communicative and social aspects of natural language learning and usage. The SOM is introduced as a gener...

No gold standard exists for assessing the risk of individual patients in cardiovascular medicine. The medical data used for such purposes is, itself, inconsistent over a history of patients at any one clinical site, and not always immediately useable. In this paper the clustering of data using Self Organizing Maps (SOM) is described. This method is an unsupervised neural network developed by Te...

DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organi...

In text management tasks, the dimensionality reduction becomes necessary to computation and interpretability of the results generated by machine learning algorithms. This paper describes a feature extraction method called semantic mapping. Semantic mapping, sparse random mapping and PCA are applied to self-organization of document collections using self-organizing map (SOM). The behaviors of th...

Artificial General Intelligence (AGI) is a term that describes a variant of a Strong AI revival in the mind sciences. Irrespective of its definition limits, and leaving aside the non-scientific metaphysical or philosophical aspirations, AGI studies the feasibility and implementation aspects of artificial systems that would have the capacity of domain non-specific (domaingeneral) human-level int...

With the increase of computer usage, the number of digital documents is reaching values that make unviable conducting the tasks of text organization by humans. There is a demand for text organization tools that can operate with little human intervention and that can display the results of the organization in the most commonly used visual interface: the two-dimensional (2D) plane. One of the tec...

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects highdimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. Despite the popular use of the algorithm for clustering and information visualisation, a system has been lacking that combines the fast ...

With increases in the amounts of available DNA sequence data, it has become increasingly important to develop tools for comprehensive systematic analysis and comparison of species-specific characteristics of protein-coding sequences for a wide variety of genomes. In the present study, we used a novel neural-network algorithm, a self-organizing map (SOM), to efficiently and comprehensively analy...

The large volume of nowadays document collections has increased the need of fast trainable document organization systems. This paper presents and evaluates a hybrid system to self-organization of massive document collections based on self-organizing map (SOM). The hybrid system uses prototypes generated by a clustering algorithm to train the document maps, thus reducing the training time of lar...