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31st International Conference on Electrical Engineering, ICEE 2023

作者： Nazari, Malihe Bahrami, Fariba Yazdanpanah, Mohammad Javad School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Advanced Control Systems Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab Tehran Iran School of Electrical and Computer Engineering College of Engineering University of Tehran Advanced Control Systems Lab Tehran Iran

ISBN: (纸本)9798350312560

The third generation of neural networks is called spiking neural networks. Spiking neural networks can not only answer all the problems that can be solved by common neural networks, they can also be computationally more powerful than them. Also, these networks are closer to the biological reality of neurons. According to these reasons, spiking neural networks have received much attention in recent years. In addition to neurons, other elements are also used in spiking neural networks, whose connection with neurons has been proven biologically. These elements are called astrocytes. In this study, we have used astrocyte local feedback in a spiking neural network to improve the accuracy of the network in a digit recognition process. To reach this goal, we use the network proposed by Diehl et al in 2015, then we added astrocyte local feedback to this network. We run the network in both states. The classification accuracy of the network in the absence of astrocytes was 88.48% on Farsi handwritten digits. After adding astrocyte local feedback, it is observed that astrocytes control the activity of neurons and modified the weight of connections based on neuron activity. In this state classification accuracy reached 90.26%. The idea was to create a neural network for learning, where, inspired by the physiological role of astrocytes at the tripartite synapse improves the performance of the neural network and we demonstrate that systems that include neurons and astrocytes perform better than systems incorporating only neurons. © 2023 IEEE.

关键词： Neurons

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

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31st National and 9th International Iranian Conference on Biomedical Engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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Improving Spiking Neural Network Performance Using Astrocyte Feedback for Farsi Digit Recognition

Improving Spiking Neural Network Performance Using Astrocyte...

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Iranian Conference on Electrical Engineering (ICEE)

作者： Malihe Nazari Fariba Bahrami Mohammad Javad Yazdanpanah Human Motor Control and Computational Neuroscience Lab and Advanced Control Systems Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Advanced Control Systems Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and computational Models

Temporal Dynamics in instrumental Learning: Integrating Entr...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Zakieh Hassanzadeh Fariba Bahrami Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran School of of Electrical and Computer Engineering University College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9798331529710

ISBN: (纸本)9798331529727

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, $\mathbf{~ p i H}$ - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments.

关键词： Learning systems computational modeling Instruments Decision making Biomedical measurement Brain modeling Entropy Complexity theory Time factors Load modeling

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Antiepileptic capacity of c-tDCS: A computational modeling study

Antiepileptic capacity of c-tDCS: A computational modeling s...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Hossein Soroushi Fariba Bahrami Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

Transcranial Direct-Current Stimulation (tDCS), as a safe and non-invasive neuromodulator, has been recently the center of attention for the treatment and management of neurological disorders. However, the intervening mechanisms of tDCS are not completely understood, and in consequence, it was not possible or advisable to utilize it as an effective treatment. In this work, by integrating a neural mass model of the thalamocortical system, which can generate and induce different types of seizures, and the physiological aspects of the interaction between tDCS and the brain, such as permissible current and how it must be involved in different neural populations, we have proposed a basic model. Then, by connecting several units of the basic model and applying a learning rule to calculate properly the connectivity weights between units, a multi-zone model is established. Our simulation results explained the ever-increasing connectivity between pre-and post-stimulation, an accepted feature of tDCS. Then, we described computationally (based on bifurcation analysis) how this change in connectivity can lead to a reduction in epileptic susceptibility.

关键词： Neurological diseases computational modeling Simulation Sociology Limit-cycles Bifurcation Brain modeling

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A Simple computational Model of Light-sensitive Cardiac tissue for Simulation of the optogenetic Defibrillation 26

A Simple Computational Model of Light-sensitive Cardiac tiss...

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26th National and 4th International Iranian Conference on Biomedical Engineering, ICBME 2019

作者： Zafarghandi, Elham Zakeri Bahrami, Fariba School of Electrical and Computer Engineering College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Laboratory Tehran Iran

ISBN: (纸本)9781728156637

Atrial and ventricular fibrillations are two types of severe heart arrhythmias. As have claimed in Karathanos et al. (2014 and 2016), and Boyle et al. (2015) optogenetic treatments are considered as a convincing substitute for cardiac therapies. However, investigating these techniques under different constraints and the condition of in vivo testing is not always straight-forward. computational modeling can be used as the first step before any experimental study. The objective of this study is to modify and simplify an already developed cardiac cell model of Ten Tusscher in order to simulating an optogenetic cardiac tissue in silico. In the original model Action Potential (AP) generation in each cardiac cell is described by a dynamic model of order 17. We estimated the parameters of a second-order Aliev-Panfilov model such that it can mimic the behavior of both light-sensitive and normal cardiac cells. Then we showed that the electrophysiological behavior of the new model of the tissue (consisted of 128∗128 cells) is similar to the original complex model. The criteria for comparing the behavior of the two models is their ability to generate the spiral waves representing cardiac tissue fibrillation and also the characteristics of these spiral waves. Afterwards, the model is used to predict the propagation of the spiral waves on the tissue, and to describe the function of the optogenetic cells used to defibrillate a stable-induced spiral wave. © 2019 IEEE.

关键词： Tissue

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Recommendation for Cardiac Magnetic Resonance Imaging-Based Phenotypic Study:Imaging Part

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Phenomics 2021年第4期1卷 151-170页

作者： Chengyan Wang Yan Li Jun Lv Jianhua Jin Xumei Hu Xutong Kuang Weibo Chen He Wang Human Phenome Institute Fudan University825 Zhangheng RoadPudong New DistrictShanghai 201203China Department of Radiology Ruijin HospitalShanghai Jiao Tong University School of MedicineShanghaiChina School of Computer and Control Engineering Yantai UniversityYantaiChina School of Data Science Fudan UniversityShanghaiChina Philips Healthcare.Co. ShanghaiChina Institute of Science and Technology for Brain-Inspired Intelligence Fudan University220 Handan RoadYangpu DistrictShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence(Fudan University) Ministry of EducationShanghaiChina

Cardiac magnetic resonance(CMR)imaging provides important biomarkers for the early diagnosis of many cardiovascular diseases and has been reported to reveal phenome-wide associations of cardiac/aortic structure and functionality in popula-tion ***,due to the complexity of operation and variations among manufactural vendors,magnetic field strengths,coils,sequences,scan parameters,and image analysis approaches,CMR is rarely used in large cohort *** guidelines mainly focused on the diagnosis of cardiovascular diseases,which did not aim to basic *** purpose of this study was to propose a recommendation for CMR based phenotype measurements for cohort *** classify the imaging sequences of CMR into three categories according to the importance and universality of correspond-ing measurable *** acquisition time and repeatability of the phenotypic measurement were also taken into consideration during the *** other guidelines,this recommendation focused on quantitative measurement of large amount of phenotypes from CMR.

关键词： Cardiac imaging Magnetic resonance imaging Phenotype Standardization

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A Simple computational Model of Light-sensitive Cardiac tissue for Simulation of the optogenetic Defibrillation

A Simple Computational Model of Light-sensitive Cardiac tiss...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Elham Zakeri Zafarghandi Fariba Bahrami Human Motor Control and Computational Neuroscience Laboratory School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

Atrial and ventricular fibrillations are two types of severe heart arrhythmias. As have claimed in Karathanos et al. (2014 and 2016), and Boyle et al. (2015) optogenetic treatments are considered as a convincing substitute for cardiac therapies. However, investigating these techniques under different constraints and the condition of in vivo testing is not always straight-forward. computational modeling can be used as the first step before any experimental study. The objective of this study is to modify and simplify an already developed cardiac cell model of Ten Tusscher in order to simulating an optogenetic cardiac tissue in silico. In the original model Action Potential (AP) generation in each cardiac cell is described by a dynamic model of order 17. We estimated the parameters of a second-order Aliev-Panfilov model such that it can mimic the behavior of both light-sensitive and normal cardiac cells. Then we showed that the electrophysiological behavior of the new model of the tissue (consisted of 128*128 cells) is similar to the original complex model. The criteria for comparing the behavior of the two models is their ability to generate the spiral waves representing cardiac tissue fibrillation and also the characteristics of these spiral waves. Afterwards, the model is used to predict the propagation of the spiral waves on the tissue, and to describe the function of the optogenetic cells used to defibrillate a stable-induced spiral wave.

关键词： Mathematical model computational modeling Cardiac tissue Biological system modeling Spirals Heart Biomedical engineering

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Effects of irreversible olivary system lesion on the gain adaptation of optokinetic response eye movement: a model based study

Effects of irreversible olivary system lesion on the gain ad...

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Iranian Conference of Biomedical Engineering (ICBME)

作者： Saeed Solouki Fariba Bahrami Mahyar Janahmadi CIPCE Human Motor Control and Computational Neuroscience Laboratory School of ECE College of Engineering University of Tehran Tehran Iran Neuroscience Research Center and Department of Physiology School of Medicine Shahid Beheshti University of Medical Sciences Tehran Iran

ISBN: (纸本)9781538679531;9781538679524

The optokinetic reflex (OKR) is a behavioral oculomotor response which serves to stabilize moving images on the retina. As the cerebellum is intimately involved in the adaptive control of such compensatory eye movement, the OKR has been turned to a favorable test bed for modeling and assessing cerebellar learning function. Learning process in the cerebellum consists of two distinct phases: 1) short-term learning, which is acquired by single session of behavioral training and disappears within 24 hours, and 2) long-term learning which is induced by repeating sessions and persists for days. On the other hand, emerging evidences from experimental studies indicates high level of coordination between olivary system activity and cerebellar learning. However, it is still unclear which phase of learning will be affected by olivary system disruption. In this paper, we proposed a simple model for learning adaptation and memory formation of the cerebellum. The model is capable to reproduce the OKR gain adaptation of eye movement in both long and short term phases. The simulation results were found to strongly agree with previously reported experimental data from wild type mice. As a second step, we explore the effects of irreversible olivary system lesion on the gain adaptability of OKR by cutting off the connection of climbing fiber, which originates from the inferior olive (IO) neuron. Thereafter, comparing to the normal case, the gain of OKR undergoes a significant decline in both short and long phases of learning. This suggests that the olivary system plays a critical role in both short-and long-term adaption of OKR.

关键词： Training Adaptation models Synapses Brain modeling Cerebellum Lesions Mice

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Program evaluation of GLA:D® Australia: Physiotherapist training outcomes and effectiveness of implementation for people with knee osteoarthritis

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Osteoarthritis and Cartilage Open 2021年第3期3卷 100175-100175页

作者： Barton, Christian J. Kemp, Joanne L. Roos, Ewa M. Skou, Soren T. Dundules, Karen Pazzinatto, Marcella F. Francis, Matthew Lannin, Natasha A. Wallis, Jason A. Crossley, Kay M. Department of Physiotherapy Podiatry and Prosthetics and Orthotics School of Allied Health Human Services and Sport La Trobe University Australia La Trobe Sport and Exercise Medicine Research Centre School of Allied Health Human Services and Sport La Trobe University Bundoora Australia Department of Surgery St Vincent's Hospital University of Melbourne Australia Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark The Research Unit PROgrez Department of Physiotherapy and Occupational Therapy Næstved-Slagelse-Ringsted Hospitals Slagelse Denmark Laboratory of Biomechanics and Motor Control School of Science and Technology Sao Paulo State University (UNESP) Presidente Prudente Brazil Department of Neuroscience Central Clinical School Monash University Melbourne Australia Alfred Health Melbourne Australia Monash Department of Clinical Epidemiology Cabrini Institute and Department of Epidemiology and Preventive Medicine School of Public Health and Preventive Medicine Monash University Australia

Objective: Evaluate the implementation of Good Life with osteoArthritis from Denmark (GLA:D®) for knee osteoarthritis in Australia using the RE-AIM QuEST (Reach, Effectiveness, Adoption, Implementation, Maintenance Qualitative Evaluation for Systematic Translation) framework. Design: Physiotherapists completed surveys before and after GLA:D® training (2017–2020) to assess practices, and barriers and enablers to implementation. Patients completed online baseline, 3-month (post-treatment) and 12-month patient reported outcomes. Effective implementation was defined as within-participant moderate effect size (ES, ≥0.50) for average pain (100 mm VAS) and Knee Injury and Osteoarthritis Outcome Score quality of life scores (KOOS-QoL), and small effect size (≥0.20) for health-related quality of life (EQ-5D-5L). Results: Reach: 1064 physiotherapists and 1945 patients from all states and territories participated. Key barriers included out-of-pocket cost to patients, and program suitability for culturally and linguistically diverse communities. Effectiveness: Following training, more physiotherapists reported discussing treatment goals and weight management, and prescribing supervised, neuromuscular exercise. Patient outcomes at 3- and 12-months (n = 1044 [54%] and 927 [48%]) reflected effective implementation, including reduced pain (ES, 95%CI = 0.72, 0.62–0.84;and 0.65, 0.54–0.77) and improved KOOS-QoL (0.79, 0.69–0.90;and 0.93, 0.81–1.04) and EQ-5D-5L (0.43, 0.31–0.54;and 0.46, 0.35–0.58) scores. Adoption: 297 sites (264 private, 33 public) implemented GLA:D®. Implementation: Most patients completed at least one education (90%), and 10 exercise-therapy (78%) sessions. Adequate staffing to support program delivery was a key enabler. Maintenance: 99% of sites (293/297) continued offering the program in July 2020. Conclusion: Training changed practice and was associated with effective widespread implementation of GLA:D® in Australia. © 2021 The Authors

关键词： Education Exercise Implementation Osteoarthritis Physiotherapy Program evaluation

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