Temporal Information Categorizing and Learning Map

My photographTICALM is an advanced recurrent self-organizing neural network which is capable of a multidimensional signal recognition. TICALM is based on enhanced version of Categorizing and Learnin Map (CALM) developed for static pattern categorization. TICALM embeds creates spatial and temporal model of an input signal (temporal sequence of spatial patterns) that can be exploited in signal modelling recognition and prediction.


  • Koutnik J., Snorek M.: Self-Organizing Neural Networks for Signal Recognition. In: 16th International Conference on Artificial Neural Networks Proceedings (ICANN 2006), Part I, p. 406-414, Springer Berlin / Heidelberg, 2006. ISBN 978-3-540-38625-4 BibTex, poster

    In this paper we introduce a self-organizing neural network that is capable of recognition of temporal signals. Conventional self-organizing neural networks like recurrent variant of Self-Organizing Map provide clustering of input sequences in space and time but the identification of the sequence itself requires supervised recognition process, when such network is used. In our network called TICALM the recognition is expressed by speed of convergence of the network while processing either learned or an unknown signal. TICALM network capabilities are shown on an experiment with handwriting recognition.

  • Koutnik J., Snorek M.: Neural Network Generating Hidden Markov Chain. In: Adaptive and Natural Computing Algorithms - Proceedings of the International Conference in Coimbra, p. 518-521, Wien: Springer, 2005. ISBN BibTex

    In this paper we introduce technique how a neural network can generate a Hidden Markov Chain. We use neural network called Temporal Information Categorizing and Learning Map. The network is an enhanced version of standard Categorizing and Learning Module (CALM). Our modifications include Euclidean metrics instead of weighted sum formerly used for categorization of the input space. Construction of the Hidden Markov Chain is provided by turning steady weight internal synapses to associative learning synapses. Result obtained from testing on simple artificial data promises applicability in a real problem domain. We present a visualization technique of the obtained Hidden Markov Chain and the method how the results can be validated. Experiments are being performed.

  • Koutnik J., Snorek M.: Single Categorizing and Learning Module for Temporal Sequences. In: Proceedings of the International Joint Conference on Neural Networks, p. 2977-2982, Piscataway: IEEE, 2004. ISBN 0-7803-8360-5 BibTex, PDF

    Modifications of an existing neural network called Categorizing and Learning Module (CALM) that allow learning of temporal sequences are introduced in this paper. We embedded an associative learning mechanism which allows to look into the past when classifying present stimuli. We have built in the Euclidean metrics instead of the weighted sum found in the original learning rule. This improvement allows better discrimination in case of learning low dimensional patterns in the temporal sequences. Results were obtained from testing the enhanced module on simple artificial data. These experiments promise applicability of the enhanced module in a real problem domain.