Biological Systems Modeling
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 17, Issue 2 , September 2023, , Pages 100-110
Abstract
Neuronal synchronization as a significant cognitive phenomenon of the human brain, has attracted the interest of neuroscience researchers in recent years. This phenomenon is generally investigated in discrete and continuous neuronal models or experimentally recorded signals of the brain. In this study, ...
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Neuronal synchronization as a significant cognitive phenomenon of the human brain, has attracted the interest of neuroscience researchers in recent years. This phenomenon is generally investigated in discrete and continuous neuronal models or experimentally recorded signals of the brain. In this study, for the first time, we investigate the weight synchronization instead of neuronal synchrony, in the training step of the artificial feedforward neural networks. The findings of the study show that the generalized weight synchronization occurs both during the training mode and in the non-training mode. Furthermore, as the training is completed, the synchronization increases between the weights. In this study, a new method is introduced in order to detect synchrony patterns using signal derivative and hierarchical clustering. We have also presented a criterion to quantify weight synchronization in different layers of the neural network. Accordingly, the results demonstrate that the lower layers of the network have a significantly higher level of weight synchrony than the upper layers.
Neural Network / Biological & Artificial Neural Network / BNN & ANN
Hossein Banki-Koshki; Seyyed Ali Seyyedsalehi
Volume 15, Issue 3 , December 2021, , Pages 199-209
Abstract
The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. ...
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The presentation of new neuronal models to simulate cognitive phenomena in the brain has attracted the research interests in recent years. In this study, a new neural model based on the chaotic behavior of weights of artificial neural networks during training by back-propagation algorithm is presented. This model is the first discrete neuronal model with learning ability and shows complex and chaotic behaviors. The learning ability of this model has enabled it to simulate cognitive phenomena such as neuronal synchronization in near-realistic conditions. The model, which is derived from a simple three-layered feed-forward neural network, has several coexisting attractors that make learning possible in various basins of attraction. The study of model parameters shows that bifurcation occurs not only by changing the learning rate, but also external stimulation can change the model behavior and bifurcation pattern. This point that can be used in modeling and designing new therapies for cognitive disorders.
Bioelectrics
Seyed Hojat Sabzpoushan; Tina Ghodsi Asnaashari; Fateme Pourhasanzade
Volume 11, Issue 1 , May 2017, , Pages 41-49
Abstract
Cancer is one of the most important causes of mortality in human society; therefore, scientists are always looking for new ways to cope with the disease. Understanding more about the dynamics of cancerous tumors in body can help researches. Therefore, making simple models for tumor growth is important. ...
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Cancer is one of the most important causes of mortality in human society; therefore, scientists are always looking for new ways to cope with the disease. Understanding more about the dynamics of cancerous tumors in body can help researches. Therefore, making simple models for tumor growth is important. Various models have been proposed for the dynamics of cancer cell growth in the body. In some models, the interaction of different types of cells in the cancerous system is mentioned. The cells in the cancerous system include tumor, healthy, and the immune system cells. Generally, the previous models based on these three cell populations couldn’t simulate chaotic behaviors, while the biology of cancer has confirmed chaos in the system. In this paper, a model of three variables is presented and it’s shown that for some values of parameters the system can simulate chaotic behaviors. Model parameters are defined based on biological relationships, each of which plays a particular role in the dynamics of the system. To analyze the role of the parameters, a specific interval is assigned to each parameter, and by plotting the bifurcation diagram, behavioral changes of the system is observed. The results show that some of the parameters have less role in the system's behavior, and by adjusting some of them, free tumor system can be provided. Also, by setting other parameters, the system can lead to a malignant tumor. The parameters of the immune system equation have the least effect on the system’s dynamics. Regarding this finding, it can be said that applying a therapeutic approach that changes the parameters of the immune system will play a minor role in treatment. While applying therapies that change the parameters of healthy cells has the greatest effect on treatment.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mina Hemmatian; Ali Maleki
Volume 9, Issue 2 , July 2015, , Pages 163-178
Abstract
The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion ...
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The humans’ heart is a chaotic system so use of fractal dimension to identify cardiac arrhythmias has been considered. Cardiac arrhythmias are prevalent diseases that is very important to be diagnosed. Hurst index which is calculated using rescaled range analysis method, is used as a criterion to evaluate chaotic systems and to quantify the fractal dimensions. Previous studies have shown that classical Hurst index is not appropriate for classification of cardiac arrhythmias because not only selection of algorithm parameters affect the value of determined Hurst index, but also it significantly varies as the heart rate changes. In this paper, modified multiple Hurst index has been proposed to classify the cardiac arrhythmias. The presented index is resistant against changes in heart rate and can be used to identify appropriate features to classify the cardiac arrhythmias. 80 signal from four types of ECG beats obtained from the MIT-BIH Arrhythmia dataset has been used to validate the algorithm. Results show that this method is able to detect normal rhythm and right bundle branch block (RBBB), left bundle branch block (LBBB) and atrial premature complex (APC) arrhythmias with accuracy of 100%, 96.25% and 88.75% using artificialneural network, k nearest neighbor and LDA classifiers respectively.
Biological Computer Modeling / Biological Computer Simulation
Seyed Hojat Sabzpoushan; Niloofar Shahidi; Azadeh Ghajarjazy
Volume 9, Issue 4 , February 2015, , Pages 351-360
Abstract
Abnormal oscillations of ventricular cell action potential can lead to cardiac arrhythmias. Early afterdepolarizations (EADs) is one kind of these oscillations that have been widely studied in the field of cardiac arrhythmias diagnosis and therapies. Nowadays although ventricular cell models have been ...
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Abnormal oscillations of ventricular cell action potential can lead to cardiac arrhythmias. Early afterdepolarizations (EADs) is one kind of these oscillations that have been widely studied in the field of cardiac arrhythmias diagnosis and therapies. Nowadays although ventricular cell models have been developed, yet dynamical mechanisms of EADs remain unknown that need more researches. In this paper, using phase plane analysis of a minimal model of ventricular cell, we show that EADs are occurred as a result of Hopf and homoclinic bifurcations in ventricular cell. We also show that during period pacing, chaos happens at the transition from no EAD to EADs. This result provides a distinct explanation for the EAD behavior of the cardiac cells and also explains EADs dynamics in accordance with experiment results. While this research was performed for ventricular cells, but the achieved results can be extend to other excitable systems and used in the prediction of oscillation due to the changes of system parameters.
Bioelectromagnetics
Hadi Tavakoli; Ali Motie Nasrabadi; Seyed Mohammad Firouzabadi; Mehri Kaviyani Moghaddam
Volume 6, Issue 2 , June 2012, , Pages 123-131
Abstract
During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing ...
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During recent years, the environment has been enormously changed by the wide range of magnetic fields. Therefore, comprehensive studies are being done for investigating their biological effects. The effects such as inhibition of bioelectric activity of neurons which is shown by evidence, like decreasing in the firing frequency or decreasing in the amplitude of action potential, have been shown. To notify and investigate these effects, the theory of “biological windows” have been proposed and considered. The effects of amplitude and/or frequency of magnetic field have been pointed in some research. In this study, regarding the behavior of nervous system, which has non-linear dynamic behavior, we study the behavior of nervous system under exposure to magnetic field. We investigate whether the low frequency field is able to affect the dynamic of nerve cells and to have influence on non-linear features of signal. We used 6 environmental intensities and 6 cells have been used in each intensity, and by calculating some of non-linear features of action potential such as Higuchi Dimension and Return map of signal, during the time and in some different intensities of magnetic fields, It was observed that all intensities magnetic fields lead to increasing in Higuchi Dimension and increasing in the scattering of the Return map of signal. Of course these effects has been more observed in the middle band of frequency which has been confirmed by the theory of ‘frequency window’ effect of magnetic fields, which it has been noticed and discussed in last two decades.
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Reza Nourouzi; Mohammad Javad Yazdanpanah
Volume 1, Issue 1 , June 2007, , Pages 53-62
Abstract
Ventricular Fibrillation (VF) is a dangerous abnormality in the heart activity. During the VF, well known shape of electrocardiogram (ECG) signal changes to a pseudo-noise waveform. Recent researches have depicted that VF is not a noisy signal. The characteristics of VF and chaotic signals are the same. ...
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Ventricular Fibrillation (VF) is a dangerous abnormality in the heart activity. During the VF, well known shape of electrocardiogram (ECG) signal changes to a pseudo-noise waveform. Recent researches have depicted that VF is not a noisy signal. The characteristics of VF and chaotic signals are the same. In this research, these characteristics were studied and used for discriminating the VF signal from the other electrocardiogram signals. Three types of electrocardiogram signals including VF, Tachycardia and Normal ECG were used for training and testing a back propagation neural network. We used these signals in three stages. At the first stage, the power spectrum of signals was used for training and testing the neural network. Time Series signals were used in the second stage. The result of the first experience was better than the second. At the third stage, we used surrogate technique to enrich the training signals in the time domain. The surrogate technique is a method which has been used in the chaotic systems. By using these new generated signals for training the neural network, the results of classification were extremely improved. Furthermore, the results of simulations showed that the chaotic dynamic of VF signal is a time dependant one.
