We propose a mechanism for unsupervised learning in networks of spiking neurons which is based on the timing of single ring events. Our results show that a topology preserving behaviour quite similar to that of Kohonen's self-organizing map can be achieved using temporal coding. In contrast to previous approaches, which use rate coding, the winner among competing neurons can be determined fast ...

To solve the mapping problem for the mobile robots in the unknown environment, a dynamic growing self-organizing map with growing-threshold tuning automatically algorithm (DGSOMGT) based on Self-organizing Map is proposed. It introduces a value of spread factor to describe the changing process of the growing threshold dynamically. The method realizes the network structure growing by training th...

Monitoring of chemical processes is becoming increasingly difficult as a result of growing complexity and larger scale of operation. In this paper, Kohonen self-organizing map s (SOM) is used to monitor the operation of a lab-scale distillation column and to identify process states. SOM projects high -dimensional data to a lower two dimensional grid maps while preserving the metric relations of...

Analysis of process data makes it possible to obtain useful information of processes or phenomena that are analytically difficult to deal with. To demonstrate the possibilities of this kind of approach, an analysis of the faults that occurred in a continuous digester is presented. The process data are fed to an artificial neural network, the Self-Organizing Map (SOM), which is used to form visu...

Content-based video navigation is an efficient method for browsing video information. A common approach is to cluster shots into groups and visualize them afterwards. In this paper, we present a prototype that follows in general this approach. Unlike the existing systems, the clustering is based on a growing self-organizing map algorithm. We focus on studying the applicability of SOMs for video...

Estimation of emotions is an essential aspect in developing intelligent systems intended for crowded environments. However, emotion estimation in crowds remains a challenging problem due to the complexity in which human emotions are manifested and the capability of a system to perceive them in such conditions. This paper proposes a hierarchical Bayesian model to learn in unsupervised manner the...

Generating sounds for music composition with the desired timbral characteristics has been a challenge ever since the dawn of electroacoustic music. Timbre is a remarkably complex phenomenon that has puzzled researchers for a long time. Actually, the nature of musical signals is not fully understood yet. In this paper, we present a sound synthesis technique that uses Kohonen’s one-dimensional se...

Semantic multimedia organization is an open challenge. In this chapter, we present an innovative way of automatically organizing multimedia information to facilitate content-based browsing. It is based on self-organizing maps. The visualization capabilities of the self-organizing map provide an intuitive way of representing the distribution of data as well as the object similarities. The main i...

Abstract Identifying community structure is an important issue in network science and has attracted attention of researchers in many fields. It is relevant for social tasks, biological inquires, and technological problems. In this paper, we proposed a new approach based on self-organizing map to community detection. By using a proper weight-updating scheme, a network can be organized into dense...