Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Spatial and temporal processing in central auditory networks
Methods in neuronal modeling
Self-organizing maps
Modelling Speech Processing and Recognition in the Auditory System with a Three-Stage Architecture
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Stimulus related data analysis by structured neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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The Multilevel Hypermap Architecture (MHA) is an extension of the hypermap introduced by Kohonen. By means of the MHA it is possible to analyze structured or hierarchical data (data with priorities, data with context, time series, data with varying exactness), which is difficult or impossible to do with known self-organizing maps so far. In the first section of this chapter the theorectical work of the previous years about the MHA and its learning algorithm are summarized. After discussion of a simple example, which demonstrates the behavior of the MHA, results from MHA applications for classification of moving objects and analysis of images from functional Magnetic Resonance Imaging (fMRI) are given. In the second section on application using the MHA with in a system for speech processing and recognition will be explained in detail. Our approach to the implementation of this system is the simulation of the human auditory system model. The goal of this system is to combine two different abstraction levels, a more biological level for peripheral auditory processing and the abstract behavior of an artificial neural network. The multistage model consists of the coupled models of neural signal processing at three different levels of the auditory system. A model of peripheral auditory signal processing by the cochlea forms the input stage of the overall model. This model is capable of generating spatio-temporal firiing rate patterns of the auditory nerve for simple acoustic as well as speech stimuli. An uniform lateral inhibitory neural network (LIN) system performs an estimation of the spectrum of the speech stimuli by spatial processing of the cochlear model's neural response patterns. Finally, the Multilevel Hypermap Architecture is used for learning and recognition of the spectral representations of teh speech stimuli provided by the LIN system