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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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Which Policy is Used by the CNS to control human Standing Posture: Intermittent or Impulsive control?

Which Policy is Used by the CNS to Control Human Standing Po...

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Iranian Conference of Biomedical Engineering (ICBME)

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

The aim of this research is to model the function of the Central Nervous System (CNS) when controlling human standing posture which paves the way for designing external skeletal structures for rehabilitation or in an industrial direction, for building human-like biped robots. We used a simple inverted pendulum with one degree of freedom to model body skeletal dynamics. Three different feedback control policies were used to describe the function of the CNS, and the stability of the whole closed-loop model under each policy was studied. The policies we considered for the CNS as a controller are: 1) intermittent control, 2) impulsive control, and 3) hybrid control, which is a combination of intermittent and impulsive. The stability of the closed-loop model under each policy was evaluated numerically using Lyapunov stability criteria. Our simulation results indicate that while the intermittent policy results in a stable system, impulsive control is more efficient in terms of energy consumption. Consequently, a hybrid control strategy will result in a system that compromises between stability and energy efficiency.

关键词： Service robots computational modeling Simulation Stability criteria control systems Energy efficiency Numerical models

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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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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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human brain connectivity: Clinical applications for clinical neurophysiology

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CLINICAL NEUROPHYSIOLOGY 2020年第7期131卷 1621-1651页

作者： Hallett, Mark de Haan, Willem Deco, Gustavo Dengler, Reinhard Di Iorio, Riccardo Gallea, Cecile Gerloff, Christian Grefkes, Christian Helmich, Rick C. Kringelbach, Morten L. Miraglia, Francesca Rektor, Ivan Strycek, Ondrej Vecchio, Fabrizio Volz, Lukas J. Wu, Tao Rossini, Paolo M. NINDS Human Motor Control Sect NIH Bldg 36Rm 4D04 Bethesda MD 20892 USA Vrije Univ Amsterdam Amsterdam UMC Dept Neurol Amsterdam Neurosci De Boelelaan 1117 Amsterdam Netherlands Univ Pompeu Fabra Dept Informat & Commun Technol Computat Neurosci Grp Ctr Brain & Cognit Roc Boronat 138 Barcelona 08018 Spain Inst Catalana Recerca & Estudis Avancats ICREA Passeig Lluis Companys 23 Barcelona 08010 Spain Max Planck Inst Human Cognit & Brain Sci Dept Neuropsychol D-04103 Leipzig Germany Monash Univ Sch Psychol Sci Clayton Vic 3800 Australia Hannover Med Sch Dept Neurol & Clin Neurophysiol Hannover Germany IRCCS Polyclin A Gemelli Fdn Neurol Unit Rome Italy Sorbonne Univ Paris Brain Inst MOVIT Sect Movement Invest & Therapeut CNRSINSERMUMR 7225 1127 Paris France Univ Med Ctr Hamburg Eppendorf UKE Dept Neurol Expt Electrophysiol & Neuroimaging Lab xENi Martinistr 52 D-20246 Hamburg Germany Cologne Univ Hosp Res Ctr Julich Inst Neuroscience & Med Cognit Neurol INM 3 Dept Neurol Julich Germany Radboud Univ Nijmegen Med Ctr Donders Inst Brain Cognit & Behav Ctr Cognit Neuroimaging Nijmegen Netherlands Univ Oxford Dept Psychiat Oxford England Aarhus Univ Dept Clin Med Ctr Mus Brain Aarhus Denmark Univ Minho Sch Med Life & Hlth Sci Res Inst ICVS P-4710057 Braga Portugal Inst Etud Avancees Paris Paris France IRCCS San Raffaele Pisana Brain Connect Lab Dept Neurosci & Neurorehabil Rome Italy Cent European Inst Technol CEITEC Brno Czech Republic St Annes Univ Hosp Epilepsy Ctr Brno Czech Republic St Annes Univ Hosp Dept Neurol Brno Czech Republic Masaryk Univ Fac Med Brno Czech Republic Capital Med Univ Xuanwu Hosp Natl Clin Res Ctr Geriatr Disorders Div Parkinsons Dis Imaging Res Beijing Peoples R China

This manuscript is the second part of a two-part description of the current status of understanding of the network function of the brain in health and disease. We start with the concept that brain function can be understood only by understanding its networks, how and why information flows in the brain. The first manuscript dealt with methods for network analysis, and the current manuscript focuses on the use of these methods to understand a wide variety of neurological and psychiatric disorders. Disorders considered are neurodegenerative disorders, such as Alzheimer disease and amyotrophic lateral sclerosis, stroke, movement disorders, including essential tremor, Parkinson disease, dystonia and apraxia, epilepsy, psychiatric disorders such as schizophrenia, and phantom limb pain. This state-of-the-art review makes clear the value of networks and brain models for understanding symptoms and signs of disease and can serve as a foundation for further work. Published by Elsevier B.V. on behalf of International Federation of Clinical Neurophysiology.

关键词： Networks, coherence Graph theory Neurodegeneration Dementia Amyotrophic lateral sclerosis Stroke Parkinson disease Dystonia Essential tremor Apraxia Epilepsy Phantom limb Psychiatric disorders EEG MRI

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