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

Razieh Falahian, M. M. Dastjerdi, S. Gharibzadeh
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引用次数: 1

Abstract

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 bio-systems, 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.
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由人工智能操纵的生物系统复杂动力学的真实建模
近年来,生物和医学科学的飞速发展是对生物系统的行为及其对各种刺激的反应进行精确建模的大量努力的结果。生物系统各组成部分之间以及与环境之间复杂的相互作用使其成为极其复杂的非线性系统。当代一些相关研究的结果显示了它们混乱的行为模式。为了模拟生物系统的这种特定行为,我们采用了一种特殊的多层前馈神经网络。该方法的一个显著特点是利用从待研究时间序列中提取的混沌映射来训练所选择的神经网络,使其在建模技术中占据主导地位。本文简要介绍了我们的结果,证实了指定的神经网络确实具有模拟生物系统混沌动力学的潜力。即使在有噪音的情况下。为了评估我们的模型,我们通过一些记录的视网膜电图数据来评估和建模大脑对闪烁光的混沌响应。
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