Structural transformations of incoming signal by a single nonlinear oscillatory neuron or by an artificial nonlinear neural network
Автор: Roman Peleshchak, Vasyl Lytvyn, Oksana Bihun, Ivan Peleshchak
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 8 vol.11, 2019 года.
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Structural transformations of incoming informational signal by a single nonlinear oscillatory neuron or an artificial nonlinear neural network are investigated. The neurons are modeled as threshold devices so that the artificial nonlinear neural network under consideration are systems of nonlinear van der Pol type oscillatory neurons. The neurons are coupled by synaptic weight coefficients to endow the systems with the configuration topology of a chain or a ring. It is shown that the morphology of the outgoing signal – with respect to the shape, amplitude and time dependence of the instantaneous frequency of the signal – at the output of such a neural network has a higher degree of stochasticity than the morphology of the signal at the output of a single neuron. We conclude that the process of coding by a single neuron or an entire chain-like or circular neural network may be considered in terms of frequency modulations, which are known in Physics as a way to transmit information. We conjecture that frequency modulations constitute one of the ways of coding of information by the neurons in these types of neural networks.
Nonlinear neuron, artificial nonlinear neural network, coding of information
Короткий адрес: https://sciup.org/15016611
IDR: 15016611 | DOI: 10.5815/ijisa.2019.08.01
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