Information model of resonance phenomena in brain neural networks

Zdeněk Votruba, Petr Moos, Miroslav Svítek, Mirko Novak


The paper presents an information model for representation of brain linear and nonlinear resonance phenomena based on information nullors. In the brain functions the rhythms and quasi periodicity of processes in neural networks play the outstanding role. It is why adaptive resonance theory (ART) including resonant effects is for long time studied by many authors. The periodicity in the transfers of signals between the long-term memory (LTM) and short-term memory (STM) creates possibility of resonance system structure. LTM with information content representing expectations and STM covering sensory information in resonance process offer an effective learning. Nonlinear adaptive resonance creates condition for a new knowledge, or inventory observation. In the paper this feature is newly modelled by information gyrator that best suits for these linear and non-linear phenomena. 


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