In order to control a prostheses by means of biological nerve signals, a self-organizing map (SOM) has been used to classify nerve signals recorded by a regeneration type neurosensor. The trained SOM contains the information about the relation between the recorded nerve signal and the winning neuron of the SOM. Classes of nerve signals red by de ned axons can be found in cluster on the SOM. For...

Self-organizing map (SOM) has been studied as a model of map formation in the brain cortex. However, the original model present several oversimplifications. For example, neurons in the cortex present a refractory period in which they are not able to be activated, restriction that should be included in the SOM if a better model is to be achieved. Although several modifications have been studied ...

We propose a new method called C-SOM using a Self-Organizing Map (SOM) for function approximation. C-SOM takes care about the output values of the «win-ning» neuron's neighbors of the map to compute the output value associated with the input data. Our work extends the standard SOM with a combination of Local Linear Mapping (LLM) and cubic spline based interpolation techniques to improve its gen...

Labile, 'high-quality', plant litters are hypothesized to promote soil organic matter (SOM) stabilization in mineral soil fractions that are physicochemically protected from rapid mineralization. However, the effect of litter quality on SOM stabilization is inconsistent. High-quality litters, characterized by high N concentrations, low C/N ratios, and low phenol/lignin concentrations, are not c...

The Kohonen self organizing map is an excellent tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp bound...

In this study self-organizing maps (SOM) were utilized for spatiotemporal analysis and classification of body surface potential mapping (BSPM) data. Altogether 86 cardiac depolarization (QRS) sequences paced by a catheter in 18 patients were included. Spatial BSPM distributions at every 5 ms over the QRS complex were first presented to an untrained SOM. The learning process of the SOM units org...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. Visual inspection of the SOM can be used to list potential dependencies between variables, that are then validated with more principled statistical methods. In this paper we discuss the use of the SOM in searc hing for dependencies in the data. We poin t out that simple use of the SOM may lead to excessive number ...

Multistrategy Learning of Self-Organizing Map (SOM) and Particle Swarm Optimization (PSO) is commonly implemented in clustering domain due to its capabilities in handling complex data characteristics. However, some of these multistrategy learning architectures have weaknesses such as slow convergence time always being trapped in the local minima. This paper proposes multistrategy learning of SO...

The peptide substances P (SP) and somatostatin (SOM) are present in small-diameter neurones of dorsal root ganglia (DRG) and in small-diameter fibres that project to the spinal cord dorsal horn. It is not known whether SP or SOM coexist with other transmitter molecules but, since both peptides can be released from sensory neurones and both can alter neuronal firing rates in the dorsal horn, it ...

Objective(s):This study examined whether conjugated linoleic acid (CLA) supplementation affects insulin sensitivity and peroxisome proliferator-activated receptor gamma (PPAR-γ) and glucose transporter type 4 (GLUT-4) protein expressions in the skeletal muscles of rats during endurance exercise. Materials and Methods:Sprague-Dawley male rats were randomly divided into HS (high-fat diet (HFD) s...