The dissertation by Pavlo Olexandrovich Timkiv, conducted at the Ternopil National Technical University named after Ivan Puluj, focuses on the identification of parameters of a mathematical model of retinal response to low-intensity stimulation. The research aims to improve the efficiency of remote, automated control and support of the functional state of the human organism. The study addresses the challenge of identifying parameters of a mathematical model of the retinal response to low-intensity light stimulation, which is crucial for remote and automated diagnosis of neurotoxicity. The research introduces a novel method based on the Hooke-Jeeves algorithm, significantly reducing the time required for parameter identification compared to traditional methods. This method enhances the accuracy and efficiency of signal processing, particularly in low-intensity electroretinographic signals, which are more sensitive to noise and require precise filtering. The dissertation also presents a detailed analysis of the use of the Weber-Fechner law in low-intensity electroretinography, which helps in planning further scientific experiments and improving the accuracy of signal processing. The research includes the development of a method for the optimal filtering of low-intensity electroretinographic signals, which is essential for early detection of neurotoxic effects. Key findings include the successful application of the Hooke-Jeeves algorithm, which reduces the identification time by over 120 times. The study also demonstrates the effectiveness of the ROC analysis, showing higher sensitivity and specificity of the proposed method compared to traditional methods. The results have practical implications for the development of automated diagnostic systems and the improvement of signal processing techniques in low-intensity electroretinography. The research contributes to the field of mathematical modeling and computational methods, offering a new approach to parameter identification in retinal response models. The results have been published in various scientific journals and conferences, highlighting the significance and impact of the research. The dissertation is a significant contribution to the field of biomedical engineering and signal processing, providing a robust framework for the analysis and diagnosis of neurotoxic effects through low-intensity electroretinography.The dissertation by Pavlo Olexandrovich Timkiv, conducted at the Ternopil National Technical University named after Ivan Puluj, focuses on the identification of parameters of a mathematical model of retinal response to low-intensity stimulation. The research aims to improve the efficiency of remote, automated control and support of the functional state of the human organism. The study addresses the challenge of identifying parameters of a mathematical model of the retinal response to low-intensity light stimulation, which is crucial for remote and automated diagnosis of neurotoxicity. The research introduces a novel method based on the Hooke-Jeeves algorithm, significantly reducing the time required for parameter identification compared to traditional methods. This method enhances the accuracy and efficiency of signal processing, particularly in low-intensity electroretinographic signals, which are more sensitive to noise and require precise filtering. The dissertation also presents a detailed analysis of the use of the Weber-Fechner law in low-intensity electroretinography, which helps in planning further scientific experiments and improving the accuracy of signal processing. The research includes the development of a method for the optimal filtering of low-intensity electroretinographic signals, which is essential for early detection of neurotoxic effects. Key findings include the successful application of the Hooke-Jeeves algorithm, which reduces the identification time by over 120 times. The study also demonstrates the effectiveness of the ROC analysis, showing higher sensitivity and specificity of the proposed method compared to traditional methods. The results have practical implications for the development of automated diagnostic systems and the improvement of signal processing techniques in low-intensity electroretinography. The research contributes to the field of mathematical modeling and computational methods, offering a new approach to parameter identification in retinal response models. The results have been published in various scientific journals and conferences, highlighting the significance and impact of the research. The dissertation is a significant contribution to the field of biomedical engineering and signal processing, providing a robust framework for the analysis and diagnosis of neurotoxic effects through low-intensity electroretinography.