A Kohonen map representation to avoid misleading interpretations

Kohonen Feature Map Associative Memory with Area Representation

In this paper, we propose a Kohonen feature map associative memory with area representation for sequential patterns. This model is based on the Kohonen feature map associative memory with area representation and the Kohonen feature map associative memory for temporal sequences. The proposed model can learn sequential patterns successively, and has robustness for damaged neurons. We carried out ...

Novel Representation of Multidimensional Datasets: The Framework nD-EVM/Kohonen

In this paper we are going to describe the steps that conform a novel approach for representation of multidimensional datasets through the proposed Framework nD-EVM/Kohonen. In this sense two phases are going to be taken in account: 1) the application of 1-Dimensional Kohonen Self-Organizing Maps (1D-KSOMs) in order to achieve (n-1)D hypervoxelizations' segmentations, n ≥ 2, taking in account t...

Kohonen Feature Map Associative Memory with Refractoriness based on Area Representation

Towards an Information Density Measure for Neural Feature Maps

Many neural models have been suggested for the development of feature maps in cortical areas. Undoubtedly the most popular model is the Kohonen self-organizing map (SOM). Once the map has been learned, this network uses a competitive winner-take-all (WTA) approach to choose a singlèbest' output neuron on a (typically) 2D grid for each presented input pattern. Cortical maps in biological organis...

Using artificial neural networks for solving chemical problems Kohonen self-organising feature maps and Hopfield networks

This second part of a Tutorial on neural networks focuses on the Kohonen self-organising feature map and the Hopfield network. First a theoretical description of each type is given. The practical issues concerning applications of the networks are then discussed. For each network, a description is given of the types of problems which can be tackled by the specific neural network, followed by a p...