Artificial neural networks: powerful tools for modeling chaotic behavior in the nervous system

Molaie, Malihe; Falahian, Razieh; Gharibzadeh, Shahriar; Jafari, Sajad; Sprott, Julien Clinton

doi:10.3389/fncom.2014.00040

Cited by 35 publications

(16 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This bifurcation occurs in a host of nonlinear systems, particularly the biological ones. This pattern has been observed in the human ERG as well as in the ERG of other animals such as salamander, rat, and rabbit [20,[27][28][29][30][31].…”

Section: B Modeling Of Chaotic Response Of Brainmentioning

confidence: 68%

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

Falahian

Dastjerdi

Gharibzadeh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

Self Cite

View full text Add to dashboard Cite

The recent meteoric significant developments in the biological and medical sciences have been the culmination of substantial efforts devoted to precisely modeling the behavior of biological systems and their responses to various stimuli. The complicated interactions within varied components of biological systems as well as with their environments make them extremely complex nonlinear systems. The results of several contemporary relevant investigations have manifested their chaotic behavioral patterns. With the aim of modeling this specific behavior of biosystems, we employ a particular multilayer feed-forward neural network. The distinctive feature of our modeling method, which makes it dominant within the modeling techniques, is training the select neural network with the chaotic map extracted from the under-study time series. Our results, which are briefly represented in this paper, confirm that the specified neural network does possess the potentiality to model the chaotic dynamics of biological systems, even in the presence of noise. In pursuance of evaluating our model, we assess and model the chaotic response of the brain to the flicker light through some recorded electroretinogram data.

show abstract

Section: B Modeling Of Chaotic Response Of Brainmentioning

confidence: 68%

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

Falahian

Dastjerdi

Gharibzadeh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

Self Cite

View full text Add to dashboard Cite

show abstract

“…A third MLP introduced in [26] is also used herein. It consists of six layers, with different numbers of neurons.…”

Section: Hidden Layers Geometric Pyramid Rulementioning

confidence: 99%

“…Characteristics of three MLPs: the one from[26] and the others designed by applying the geometric pyramid rule with five and six layers.…”

mentioning

confidence: 99%

FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

et al. 2018

View full text Add to dashboard Cite

Many biological systems and natural phenomena exhibit chaotic behaviors that are saved in time series data. This article uses time series that are generated by chaotic oscillators with different values of the maximum Lyapunov exponent (MLE) to predict their future behavior. Three prediction techniques are compared, namely: artificial neural networks (ANNs), the adaptive neuro-fuzzy inference system (ANFIS) and least-squares support vector machines (SVM). The experimental results show that ANNs provide the lowest root mean squared error. That way, we introduce a multilayer perceptron that is implemented using a field-programmable gate array (FPGA) to predict experimental chaotic time series.

show abstract

“…It appears that the brain response to some stimuli such as the flicker light is an example of chaotic reaction [17][18][19][20]. This characteristic makes the modeling of this response a particularly challenging task, specifically owing to the lack of thorough knowledge about this system.…”

Section: Introductionmentioning

confidence: 99%

Artificial neural network-based modeling of brain response to flicker light

et al. 2015

Self Cite

View full text Add to dashboard Cite

Not only does the modeling of dynamical systems, for instance the biological systems, play an important role in the accurate perception and analysis of these systems, but it also makes the prediction and control of their behavior straightforward. The results of multiple researches in the field of the modeling of biological systems have indicated that the chaotic behavior is a prevalent feature of most complex interactive biological systems. Our results demonstrate that the artificial neural network provides us an effective means to model the underlying dynamics of these systems. In this paper, at first, we represent the results of the use of a multilayer feed-forward neural network to model some famous chaotic systems. The specified neural network is trained with the return maps extracted from the time series. We proceed with the paper by evaluating the accuracy and robustness of our model. The ability of the select neural network to model the dynamics of chosen chaotic systems is verified, even in the presence of noise. Afterwards, we model the brain response to the flicker light. It is known that the brain response to some stimuli such as the flicker light recorded as electroretinogram is an exemplar of chaotic behavior. The need remains, however, for realistic modeling of this behavior of the brain. In this paper, we represent the results of the modeling of this chaotic response by utilizing the proposed neural network. The capability of the neural network to model this specific brain response is confirmed.

show abstract

Artificial neural networks: powerful tools for modeling chaotic behavior in the nervous system

Cited by 35 publications

References 12 publications

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

Authentic modeling of complex dynamics of biological systems by the manipulation of artificial intelligence

FPGA-Based Implementation of a Multilayer Perceptron Suitable for Chaotic Time Series Prediction

Artificial neural network-based modeling of brain response to flicker light

Contact Info

Product

Resources

About