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Synchronization of FitzHugh-Nagumo reaction-diffusion systems via one-dimensional linear control law

Ouannas Adel, Mesdoui Fatiha, Momani Shaher, Batiha Iqbal, Grassi Giuseppe

Archives of Control Sciences

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2021

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Vol. 31, No. 2

333--345

EN

The Fitzhugh-Nagumo model (FN model), which is successfully employed in modeling the function of the so-called membrane potential, exhibits various formations in neuronal networks and rich complex dynamics. This work deals with the problem of control and synchronization of the FN reaction-diffusion model. The proposed control law in this study is designed to be uni-dimensional and linear law for the purpose of reducing the cost of implementation. In order to analytically prove this assertion, Lyapunov’s second method is utilized and illustrated numerically in one- and/or two-spatial dimensions.

2

Significance of the neuronal morphology for network dynamics

Meyrand P.

Biocybernetics and Biomedical Engineering

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2006

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Vol. 26, no. 2

15-23

EN

One of the striking features of the Nervous System is the complexity and diversity of the neuronal shapes. Extensive morphological studies associated with modeling approaches have emphasized a close relationship between the neuron structure and its bioelectrical properties. However, structural implications of the synaptic connectivity (spatial distribution, number of synapses) in the context of the network operation are far less studied. To explore this issue, the chemical inhibitory synapses between identified motoneurones belonging to the stomatogastric nervous system of the lobster Homarus gammarus were examined. Using multiple intracelIular dyes injected in both pre- and post synaptic motoneurones, the sites of the appositions between these two celIs were localized under the laser-scanning confocal microscopy, and then the synaptic release sites were visualized under the electron microscopy. Such analysis indicates that only one or two zones of apposition exist between the two celIs studied, and only one release site exists per zone. Moreover, these synaptic sites are always located in the same position within the dendritic arbor. Modeling studies suggest that such a specific localization may play an important role in the operation of the network.

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Komputerowe wspomaganie klasyfikacji wad identyfikowanych metodami radiograficznymi w odlewach ze stopów aluminium

Dobrzański L. A., Krupiński M., Maniara R., Kasprzak W., Sokołowski J.

Rudy i Metale Nieżelazne

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2005

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R. 50, nr 5

229-236

PL

W artykule przedstawiono komputerową metodykę klasyfikacji wad (tabl. 2), powstałych w stopach Al w trakcie wykonywania z nich elementów silników samochodowych produkowanych metodą niskociśnieniowego odlewania. Identyfikacji wad dokonano na podstawie danych uzyskanych z cyfrowych obrazów rejestrowanych metodami rentgenowskiej analizy defektoskopowej (rys. 1). Do rozwiązania tego zagadnienia wykorzystano opracowaną metodykę i związane z nią programy komputerowe do analizy obrazów rentgenowskich (rys. 2÷3), przygotowania danych wejściowych do sieci neuronowych oraz samą kontrolę jakości odlewów. Z zastosowanych w badaniach sieci neuronowych, w pracy przedstawiono wyniki klasyfikacji wad dla najlepszej sieci każdego typu. Parametry sieci o najlepszych wynikach klasyfikacji przedstawiono w tablicy 3. Zagadnienia klasyfikacyjne oceniano analizując, wyznaczoną dla danych testowych, liczbą poprawnych klasyfikacji (rys. 6÷7).

EN

In the paper a computer aided methodology of classification of defects (tabl. 2), which are formed in aluminium alloys during production of elements for car engines with the low-pressure casting method is presented. The defect identification was performed on the basis of digital recorded data registered by the use of X-ray image analysis method (fig. 1). In order to solve this problem an elaborated methodology and related computer programmes for X-ray images analysis (fig. 2÷ 3), as well as the preparation of entrance data for neuronal networks and also the quality cast control were used. The classification results of the best type of every network investigated are presented. The network parameters with the best classification results are showed in table 3. Analysing a number of correct classifications of pointed out test data (fig. 6÷7), the classifying problems are evaluated.