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Zhang R, Wang Z, Wu T, Cai Y, Tao L, Xiao ZC, Li Y. Learning spiking neuronal networks with artificial neural networks: neural oscillations. J Math Biol 2024; 88:65. [PMID: 38630136 DOI: 10.1007/s00285-024-02081-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 06/30/2023] [Accepted: 03/05/2024] [Indexed: 04/19/2024]
Abstract
First-principles-based modelings have been extremely successful in providing crucial insights and predictions for complex biological functions and phenomena. However, they can be hard to build and expensive to simulate for complex living systems. On the other hand, modern data-driven methods thrive at modeling many types of high-dimensional and noisy data. Still, the training and interpretation of these data-driven models remain challenging. Here, we combine the two types of methods to model stochastic neuronal network oscillations. Specifically, we develop a class of artificial neural networks to provide faithful surrogates to the high-dimensional, nonlinear oscillatory dynamics produced by a spiking neuronal network model. Furthermore, when the training data set is enlarged within a range of parameter choices, the artificial neural networks become generalizable to these parameters, covering cases in distinctly different dynamical regimes. In all, our work opens a new avenue for modeling complex neuronal network dynamics with artificial neural networks.
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Affiliation(s)
- Ruilin Zhang
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, 100871, China
- Yuanpei College, Peking University, 100871, Beijing, China
| | - Zhongyi Wang
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, 100871, China
- School of Mathematical Sciences, Peking University, 100871, Beijing, China
| | - Tianyi Wu
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, 100871, China
- School of Mathematical Sciences, Peking University, 100871, Beijing, China
| | - Yuhang Cai
- Department of Mathematics, University of California, 94720, Berkeley, CA, USA
| | - Louis Tao
- Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, School of Life Sciences, Peking University, Beijing, 100871, China.
- Center for Quantitative Biology, Peking University, 100871, Beijing, China.
| | - Zhuo-Cheng Xiao
- Courant Institute of Mathematical Sciences, New York University, 10003, New York, NY, USA.
| | - Yao Li
- Department of Mathematics and Statistics, University of Massachusetts Amherst, 01003, Amherst, MA, USA.
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Sharma S, Padhy PK. Extended B-polynomial neural network for time-delayed system modeling using sampled data. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-210580] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The combination of machine learning and artificial intelligent has already proved its potential in achieving remarkable results for modeling unknown systems. These techniques commonly use enough data samples to train and optimize their architectures. In the present era, with the availability of enough storage and computation power, the machine learning based data-driven system modeling approaches are getting popular as they do not interrupt the normal system operations and work solely on collected data. This work proposes a data-driven parametric neural network technique for modeling time-delayed systems, which is demanding but challenging area of research and comes under nonlinear optimization problem. The key contribution of this work is the inclusion of an extended B-polynomial into the network structure for estimating time-delayed first and second order system models. These type of models extensively used for addressing simulations, predictions, controlling and monitoring related issues. Also, an adaptive learning based convergence of the proposed algorithm is proved with the help of the Lyapunov stability theory. The proposed algorithm compared with existing techniques on some well-known example problems. A real practical system plant is also included for validating the proposed concept.
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Affiliation(s)
- Sudeep Sharma
- Department of Electronics and Communication, PDPMIIITDM, Jabalpur, Madhya Pradesh, India
| | - Prabin K. Padhy
- Department of Electronics and Communication, PDPMIIITDM, Jabalpur, Madhya Pradesh, India
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Post-Processing of High Formwork Monitoring Data Based on the Back Propagation Neural Networks Model and the Autoregressive—Moving-Average Model. Symmetry (Basel) 2021. [DOI: 10.3390/sym13081543] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Many high formwork systems are currently equipped with health monitoring systems, and the analysis of the data obtained can determine whether high formwork is a hazard. Therefore, the post-processing of monitoring data has become an issue of widespread concern. In this paper, we discussed the fitting effect of the symmetrical high formwork monitoring data using the autoregressive–moving-average (ARMA) model and the back propagation neural networks (BPNN) combined model to process. In the actual project, the symmetry of the high formwork system allows the analysis of local monitoring results to be well extended to the whole. For the establishment of the ARMA model, the accurate judgment of the model order has a significant impact. In this paper, back propagation neural networks (BPNN) are used to simulate the ARMA process. The order of the ARMA model is estimated by determining the optimal neural network structure, which is suitable for linear or nonlinear sequences. We validated this approach from the ARMA model data simulated in Monte Carlo and compared it with the Akaike information criterion (AIC) and Bayesian information criterion (BIC). The length of the sequence, the coefficients and the order of the ARMA model are considered as factors that influence the judgment effect. Under different conditions, the BPNN always shows an accuracy rate of more than 90%, while the BIC only has a higher accuracy rate when the model order is low and the judgment efficiency of the AIC is below 50%. Finally, the proposed method successfully modeled the stress sequence and obtained the stress change trend. Compared with AIC and BIC, the efficiency of the processing time series is increased by about 50% when an order is obtained by BPNN.
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Prasertsakul T, Charoensuk W. Determination of Postural Control Mechanism in Overweight Adults Using The Artificial Neural Networks System and Nonlinear Autoregressive Moving Average Model. ADVANCED BIOMEDICAL ENGINEERING 2020. [DOI: 10.14326/abe.9.154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
| | - Warakorn Charoensuk
- Department of Biomedical Engineering, Faculty of Engineering, Mahidol University
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Arterial blood pressure feature estimation using photoplethysmography. Comput Biol Med 2018; 102:104-111. [PMID: 30261404 DOI: 10.1016/j.compbiomed.2018.09.013] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2018] [Revised: 09/13/2018] [Accepted: 09/15/2018] [Indexed: 11/23/2022]
Abstract
Continuous and noninvasive monitoring of blood pressure has numerous clinical and fitness applications. Current methods of continuous measurement of blood pressure are either invasive and/or require expensive equipment. Therefore, we investigated a new method for the continuous estimation of two main features of blood pressure waveform: systolic and diastolic pressures. The estimates were obtained from a photoplethysmography signal as input to the fifth order autoregressive moving average models. The performance of the method was evaluated using beat-to-beat full-wave blood pressure measurements from 15 young subjects, with no known cardiovascular disorder, in supine position as they breathed normally and also while they performed a breath-hold maneuver. The level of error in the modeling and prediction estimates during normal breathing and breath-hold maneuvers, as measured by the root mean square of the residuals, were less than 5 mmHg and 11 mm Hg, respectively. The mean of model residuals both during normal breathing and breath-hold maneuvers was considered to be less than 3.2 mmHg. The dependency of the accuracy of the estimates on the subject data was assessed by comparing the modeling errors for the 15 subjects. Less than 1% of the models showed significant differences (p < 0.05) from the other models, which indicates a high level of consistency among the models.
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Paul C, Vishwakarma GK. Back propagation neural networks and multiple regressions in the case of heteroskedasticity. COMMUN STAT-SIMUL C 2017. [DOI: 10.1080/03610918.2016.1212066] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Chinmoy Paul
- Department of Applied Mathematics, Indian School of Mines, Dhanbad, India
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Singh UP, Jain S. Optimization of neural network for nonlinear discrete time system using modified quaternion firefly algorithm: case study of Indian currency exchange rate prediction. Soft comput 2017. [DOI: 10.1007/s00500-017-2522-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Pawlus W, Karimi HR, Robbersmyr KG. Data-based modeling of vehicle collisions by nonlinear autoregressive model and feedforward neural network. Inf Sci (N Y) 2013. [DOI: 10.1016/j.ins.2012.03.013] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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LING SH, SAN PP, NGUYEN HT, LEUNG FHF. NON-INVASIVE NOCTURNAL HYPOGLYCEMIA DETECTION FOR INSULIN-DEPENDENT DIABETES MELLITUS USING GENETIC FUZZY LOGIC METHOD. INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2012. [DOI: 10.1142/s1469026812500253] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Hypoglycemia, or low blood glucose, is the most common complication experienced by Type 1 diabetes mellitus (T1DM) patients. It is dangerous and can result in unconsciousness, seizures and even death. The most common physiological parameter to be effected from hypoglycemic reaction are heart rate (HR) and correct QT interval (QTc) of the electrocardiogram (ECG) signal. Based on physiological parameters, a genetic algorithm based fuzzy reasoning model is developed to recognize the presence of hypoglycemia. To optimize the parameters of the fuzzy model in the membership functions and fuzzy rules, a genetic algorithm is used. A validation strategy based adjustable fitness is introduced in order to prevent the phenomenon of overtraining (overfitting). For this study, 15 children with 569 sampling data points with Type 1 diabetes volunteered for an overnight study. The effectiveness of the proposed algorithm is found to be satisfactory by giving better sensitivity and specificity compared with other existing methods for hypoglycemia detection.
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Affiliation(s)
- S. H. LING
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - P. P. SAN
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - H. T. NGUYEN
- Faculty of Engineering and Information Technology, University of Technology, Sydney, NSW, 2007, Australia
| | - F. H. F. LEUNG
- Department of Electronic and Information of Engineering, The Hong Kong Polytechnic University, Hong Kong
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Patra JC, Kot AC. Nonlinear dynamic system identification using Chebyshev functional link artificial neural networks. ACTA ACUST UNITED AC 2012; 32:505-11. [PMID: 18238146 DOI: 10.1109/tsmcb.2002.1018769] [Citation(s) in RCA: 223] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.
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Karimi HR, Pawlus W, Robbersmyr KG. Signal reconstruction, modeling and simulation of a vehicle full-scale crash test based on Morlet wavelets. Neurocomputing 2012. [DOI: 10.1016/j.neucom.2012.04.010] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
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Ling SH, Nguyen HT. Natural occurrence of nocturnal hypoglycemia detection using hybrid particle swarm optimized fuzzy reasoning model. Artif Intell Med 2012; 55:177-84. [PMID: 22698854 DOI: 10.1016/j.artmed.2012.04.003] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2010] [Revised: 04/19/2012] [Accepted: 04/25/2012] [Indexed: 01/28/2023]
Abstract
INTRODUCTION Low blood glucose (hypoglycemia) is a common and serious side effect of insulin therapy in patients with diabetes. This paper will make a contribution to knowledge in the modeling and design of a non-invasive hypoglycemia monitor for patients with type 1 diabetes mellitus (T1DM) using a fuzzy-reasoning system. METHODS Based on the heart rate and the corrected QT interval of the electrocardiogram (ECG) signal, we have developed a hybrid particle-swarm-optimization-based fuzzy-reasoning model to recognize the presence of hypoglycemic episodes. To optimize the fuzzy rules and the fuzzy-membership functions, a hybrid particle-swarm-optimization with wavelet mutation operation is investigated. RESULTS From our clinical study of 16 children with T1DM, natural occurrence of nocturnal-hypoglycemic episodes was associated with increased heart rates and increased corrected QT intervals. All the data sets were collected from the Government of Western Australia's Department of Health. All data were organized randomly into a training set (8 patients with 320 data points) and a testing set (another 8 patients with 269 data points). To prevent the phenomenon of overtraining, we separated the training set into 2 sets (4 patients in each set) and a fitness function was introduced for this training process. The testing performances of the proposed algorithm for detection of advanced hypoglycemic episodes (sensitivity=85.71% and specificity=79.84%) and hypoglycemic episodes (sensitivity=80.00% and specificity=55.14%) were given. CONCLUSION We have investigated the detection for the natural occurrence of nocturnal hypoglycemic episodes in T1DM using a hybrid particle-swarm-optimization-based fuzzy-reasoning model with physiological parameters. In this study, no restricted environment (e.g. patient's dietary requirements) is required. Furthermore, the sampling time is between 5 and 10 min. To conclude, we have shown that the testing performances of the proposed algorithm for detection of advanced hypoglycemic and hypoglycemic episodes for T1DM patients are satisfactory.
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Affiliation(s)
- Sai Ho Ling
- Centre for Health Technologies, Faculty of Engineering and Information Technology, University of Technology Sydney, 1 Broadway, Ultimo, NSW 2007, Australia.
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Xiao X, Mukkamala R, Sheynberg N, Grenon SM, Ehrman MD, Mullen TJ, Ramsdell CD, Williams GH, Cohen RJ. Effects of simulated microgravity on closed-loop cardiovascular regulation and orthostatic intolerance: analysis by means of system identification. J Appl Physiol (1985) 2004; 96:489-97. [PMID: 14514703 DOI: 10.1152/japplphysiol.00602.2003] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Abstract
Microgravity-induced orthostatic intolerance (OI) continues to be a primary concern for the human space program. To test the hypothesis that exposure to simulated microgravity significantly alters autonomic nervous control and, thus, contributes to increased incidence of OI, we employed the cardiovascular system identification (CSI) technique to evaluate quantitatively parasympathetic and sympathetic regulation of heart rate (HR). The CSI method analyzes second-to-second fluctuations in noninvasively measured HR, arterial blood pressure, and instantaneous lung volume. The coupling mechanisms between these signals are characterized by using a closed-loop model. Parameters reflecting parasympathetic and sympathetic responsiveness with regard to HR regulation can be extracted from the identified coupling mechanisms. We analyzed data collected from 29 human subjects before and after 16 days of head-down-tilt bed rest (simulated microgravity). Statistical analyses showed that parasympathetic and sympathetic responsiveness was impaired by bed rest. A lower sympathetic responsiveness and a higher parasympathetic responsiveness measured before bed rest identified individuals at greater risk of OI before and after bed rest. We propose an algorithm to predict OI after bed rest from measures obtained before bed rest.
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Affiliation(s)
- Xinshu Xiao
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge 02139, USA
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Lu S, Ju KH, Chon KH. A new algorithm for linear and nonlinear ARMA model parameter estimation using affine geometry. IEEE Trans Biomed Eng 2001; 48:1116-24. [PMID: 11585035 DOI: 10.1109/10.951514] [Citation(s) in RCA: 72] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
A linear and nonlinear autoregressive (AR) moving average (MA) (ARMA) identification algorithm is developed for modeling time series data. The new algorithm is based on the concepts of affine geometry in which the salient feature of the algorithm is to remove the linearly dependent ARMA vectors from the pool of candidate ARMA vectors. For noiseless time series data with a priori incorrect model-order selection, computer simulations show that accurate linear and nonlinear ARMA model parameters can be obtained with the new algorithm. Many algorithms, including the fast orthogonal search (FOS) algorithm, are not able to obtain correct parameter estimates in every case, even with noiseless time series data, because their model-order search criteria are suboptimal. For data contaminated with noise, computer simulations show that the new algorithm performs better than the FOS algorithm for MA processes, and similarly to the FOS algorithm for ARMA processes. However, the computational time to obtain the parameter estimates with the new algorithm is faster than with FOS. Application of the new algorithm to experimentally obtained renal blood flow and pressure data show that the new algorithm is reliable in obtaining physiologically understandable transfer function relations between blood pressure and flow signals.
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Affiliation(s)
- S Lu
- Department of Electrical Engineering and Center for Biomedical Engineering, City College of the City University of New York, NY 10031, USA
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Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges. WATER SCIENCE AND TECHNOLOGY LIBRARY 2000. [DOI: 10.1007/978-94-015-9341-0_15] [Citation(s) in RCA: 16] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
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Chon KH, Mukkamala R, Toska K, Mullen TJ, Armoundas AA, Cohen RJ. Linear and nonlinear system identification of autonomic heart-rate modulation. IEEE ENGINEERING IN MEDICINE AND BIOLOGY MAGAZINE : THE QUARTERLY MAGAZINE OF THE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY 1997; 16:96-105. [PMID: 9313086 DOI: 10.1109/51.620500] [Citation(s) in RCA: 22] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- K H Chon
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, USA.
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