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Extending concepts of mapping of human brain to artificial intelligence and neural networks
, Article Scientia Iranica ; Volume 28, Issue 3 D , 2021 , Pages 1529-1534 ; 10263098 (ISSN) ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
This paper introduces the concept of mapping of Artificially Intelligent (AI) computational systems. The concept of homunculus from human neurophysiology is extended to AI systems. It is assumed that an AI system behaves similarly to a mini-column or ganglion in the natural animal brain that comprises a layer of afferent (input) neurons, a number of interconnecting processing cells, and a layer of efferent (output) neurons or organs. The objective of the present study was to identify the correlation between the stimulus to each afferent neuron and the corresponding response from each efferent organ when the intelligent system is subjected to certain stimuli. To clarify the general concept, a...
Modeling the Parkinson's tremor and its treatments
, Article Journal of Theoretical Biology ; Volume 236, Issue 3 , 2005 , Pages 311-322 ; 00225193 (ISSN) ; Sarbaz, Y ; Gharibzadeh, S ; Sharif University of Technology
2005
Abstract
In this paper, we discuss modeling issues of the Parkinson's tremor. Through the work we have employed physiological structure as well as functioning of the parts in brain that are involved in the disease. To obtain more practical similarity, random behaviors of the connection paths are also considered. Medication or treatment of the disease both by drug prescription and electrical signal stimulation are modeled based on the same model introduced for the disease itself. Two new medication strategies are proposed based on the model to reduce the side effects caused by the present drug prescription. © 2005 Elsevier Ltd. All rights reserved
A novel pipeline architecture of replacing ink drop spread
, Article Proceedings - 2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010, 15 December 2010 through 17 December 2010, Kitakyushu ; 2010 , Pages 127-133 ; 9781424473762 (ISBN) ; Bagheri Shouraki, S ; Tabandeh, M ; Mousavi, H. R ; Sharif University of Technology
2010
Abstract
Human Brain is one of the most wonderful and complex systems which is designed for ever; A huge complex network composed of neurons as tiny biological and chemical processors which are distributed and work together as a super parallel system to do control and vital activities of human body. Brain learning simulation and hardware implementation is one of the most interesting research areas in order to make artificial brain. One of the researches in this area is Active Learning Method in brief ALM. ALM is an adaptive recursive fuzzy learning algorithm based on brain functionality and specification which models a complex Multi Input Multi Output System as a fuzzy combination of Single Input...
Analyzing Directed Functional Brain Networks Based On Electroencephalogram Data
, M.Sc. Thesis Sharif University of Technology ; Rabiei, Hamid Reza (Supervisor)
Abstract
Over the past few years, various studies have demonstrated that the complex networks can be used to model the structure and functions of human brain. Some of these studies indi- cated that diseases such as Alzheimer, Epilepsy, and Schizophrenia can cause changes in this network. The main idea behind the methods proposed to analyze human brain’s behav- ior, is to identify regions of the brain with specific tasks. Recent studies show that multiple regions of human brain are involved in complex activities, so it’s important to detect their interactions. Using functional high resolution multichannel neurophysiological signals, like electroencephalographic (EEG) and magnetoencephalographic...
Multilayer Network Approach to Brain Connectivity Analysis in Cognitive Disorder
, M.Sc. Thesis Sharif University of Technology ; Rabiee, Hamid Reza (Supervisor) ; Manzori, Mohammad Taghi (Supervisor)
Abstract
Brain is the most complex part of the human body. This three pound organ acting as seed of intelligence, database of memories, interpreter of the senses, and managing our movement. Network neuroscience plays an important role in revealing hidden aspects of brain functions. Recently, multilayer network models have been proposed to achieve a deeper analysis on the brain networks. Multilayer network is a framework that can represent multiple relations between nodes. In a single layer brain network, different shared information methods can be used to find connection between Regions of Interests (ROIs), but in a multilayer approach, ROIs can have multiple connections in different domains such as...
Alzheimer's Disease Diagnosis Using Brain Source Localiztion, Based on Realistic Head Model
, M.Sc. Thesis Sharif University of Technology ; Vosoughi Vahdat, Bijan (Supervisor) ; Jalili, Mahdi (Co-Advisor)
Abstract
Dementia is one of the most common disorders among the elderly population. Statistical analyses show that among several subtypes of dementia, Alzheimer’s disease (AD) is the most frequent cause of dementia and this number is projected to increase. AD not only results in impairment of learning and memory but also in the moderate stages of illness, motor functions are profoundly disturbed, and ultimately will affect the patient's lifetime. The proposed drug treatments for this disease only reduce its progress probability. Therefore, early diagnosis for effective treatment of Alzheimer's disease is one of the critical issues in the field of dementia. This project is an effort to extract...
Primary Visual Pathway Simulation in Mouse Using NetPyNE
, M.Sc. Thesis Sharif University of Technology ; Peyvandi, Hossein (Supervisor)
Abstract
Simulation of biophysical neural networks enables the interpretation and integration of fast-growing and different experimental datasets. The widely used NEURON simulator allows molecular-to-network simulation. However, it is still a very hard challenge to create large-scale models and operate parallel simulations applying NEURON. SUNY Downstate developed the NetPyNE means networks using Python and NEURON, which was funded by the New York State Department of Health and some other institutions. NetPyNE is a Python-based tool that enables the development of data-driven multi-scale network models in NEURON through both programmatic and graphical interfaces. It is a powerful tool for parallel...
Subspace Identification and Brain Connectivity Estimation of Electroencephalogram Signals Using Graph Signal Processing
, Ph.D. Dissertation Sharif University of Technology ; Hajipour Sardouie, Sepideh (Supervisor) ; Shamsollahi, Mohammad Bagher (Supervisor)
Abstract
EEG brain signals have gained particular attention among researchers in the field of brain signal processing due to their easy and cheap recording, high temporal resolution, and non-invasiveness. On the other hand, defects such as high vulnerability to various types of noise and artifacts have caused the main challenge before processing them to improve the signal-to-noise ratio and the interpretability of brain connectivity obtained from them. In order to solve these challenges, two important problems of "separation of desired and undesired signal subspace" and "functional and effective connectivity analysis" have been raised, respectively. In solving both problems, EEG signals are usually...
Attitude control of a quadrotor using brain emotional learning based intelligent controller
, Article 13th Iranian Conference on Fuzzy Systems, IFSC 2013 ; 2013 ; Shahri, A. M ; Shouraki, S. B ; Sharif University of Technology
IEEE Computer Society
2013
Abstract
For the first time in this paper, Brain Emotional Learning Based Intelligent Controller (BELBIC) is applied to attitude control of a Quadrotor. BELBIC controller is designed based on the computational model of emotional learning process in mammalian brain limbic system. Proposed control algorithm is employed because of the learning ability and independency to system model and also satisfactory performances dealing with disturbances and changing in system parameters. Quadrotor is an Unmanned Aerial Vehicle (UAV) which has the capability of Vertical Take-Off and Landing (VTOL). Simulation results of controlling Quadrotor with BELBIC are addressed. Also pitch angle disturbance is applied due to...
A new Markovian approach towards neural spike sorting
, Article ICICS 2011 - 8th International Conference on Information, Communications and Signal Processing, 13 December 2011 through 16 December 2011 ; Dec , 2011 , Page(s): 1 - 5 ; 9781457700309 (ISBN) ; Shamsollahi, M. B ; Vigneron, V ; Sharif University of Technology
Abstract
Brain is the most complicated organ of body. It controls the activity of all other organs. Understanding its function and its language could give us a direct communication pathway for connecting with injured motor organ and it could be the core of functional repairing. Neurons are the vertices of a vast network that generates the brain signals. Neuronal recordings capture brain activity signatures. The processing of these signals can help to translate brain's language. Usually it follows three main stages: spike detection and extraction, spike sorting, and intention extraction from the encoded signal. In this work, we introduce an original idea based on Hidden Markov Models (HMM) which helps...
Mechanical characterization of brain tissue in compression
, Article ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, 21 August 2016 through 24 August 2016 ; Volume 3 , 2016 ; 9780791850138 (ISBN) ; Ahmadian, M. T ; Hoviat Talab, M ; Computers and Information in Engineering Division; Design Engineering Division ; Sharif University of Technology
American Society of Mechanical Engineers (ASME)
Abstract
The biomechanical behavior of brain tissue is needed for predicting the traumatic brain injury (TBI). Each year over 1.5 million people sustain a TBI in the United States. The appropriate coefficients for modeling the injury prediction can be evaluated using experimental data. In the present paper, using an experimental setup on bovine brain tissue, unconfined compression tests at quasi-static strain rates of ϵ 0.0004s-1, 0.008s-1 and 0.4s-1 combined with a stress relaxation test under unconfined uniaxial compression with ϵ 0.67s-1 ramp rate are performed. The fitted viscohyperelastic parameters were utilized by using obtained stressstrain curves. The finite element analysis (FEA) is...
Dynamic analysis of magnetic nanoparticles crossing cell membrane
, Article Journal of Magnetism and Magnetic Materials ; Volume 429 , 2017 , Pages 372-378 ; 03048853 (ISSN) ; Shamloo, A ; Ghafar Zadeh, E ; Alasty, A ; Sharif University of Technology
Elsevier B.V
2017
Abstract
Nowadays, nanoparticles (NPs) are used in a variety of biomedical applications including brain disease diagnostics and subsequent treatments. Among the various types of NPs, magnetic nanoparticles (MNPs) have been implemented by many research groups for an array of life science applications. In this paper, we studied MNPs controlled delivery into the endothelial cells using a magnetic field. Dynamics equations of MNPs were defined in the continuous domain using control theory methods and were applied to crossing the cell membrane. This study, dedicated to clinical and biomedical research applications, offers a guideline for the generation of a magnetic field required for the delivery of...
Detection of change to SSVEPs using analysis of phase space topological : a novel approach
, Article Neurophysiology ; Volume 51, Issue 3 , 2019 , Pages 180-190 ; 00902977 (ISSN) ; Maghooli, K ; Pisheh, N. F ; Mohammadi, M ; Soroush, P. Z ; Tahvilian, P ; Sharif University of Technology
Springer New York LLC
2019
Abstract
A novel method based on EEG nonlinear analysis and analysis of steady-state visual evoked potentials (SSVEPs) has been processed. The EEG phase space is reconstructed, and some new geometrical features are extracted. Statistical analysis is carried out based on ANOVA, and most significant features are selected and then fed into a multi-class support vector machine (MSVM). Both offline and online phases are considered to fully address SSVEP detection. In the offline mode, the whole design evaluation, feature selection, and classifier training are performed. In the online scenario, the proposed method is evaluated and the detection rate is reported for both phases. Subject-dependent and...
A transfer learning algorithm based on linear regression for between-subject classification of EEG data
, Article 25th International Computer Conference, Computer Society of Iran, CSICC 2020, 1 January 2020 through 2 January 2020 ; 2020 ; Sardouie, S. H ; Foroughmand Aarabi, M. H ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2020
Abstract
Classification is the most important part of brain-computer interface (BCI) systems. Because the neural activities of different individuals are not identical, using the ordinary methods of subject-dependent classification, does not lead to high accuracy in betweensubject classification problems. As a result, in this study, we propose a novel method for classification that performs well in between-subject classification. In the proposed method, at first, the subject-dependent classifiers obtained from the train subjects are applied to the test trials to obtain a set of scores and labels for the trials. Using these scores and the real labels of the labeled test trials, linear regression is...
Anisotropic finite element modelling of traumatic brain injury: A voxel-based approach
, Article Scientia Iranica ; Volume 28, Issue 3 B , 2021 , Pages 1271-1283 ; 10263098 (ISSN) ; Farahmand, F ; Ahmadian, M. T ; Masjoodi, S ; Sharif University of Technology
Sharif University of Technology
2021
Abstract
A computationally efficient 3D human head finite element model was constructed. The model includes the mesoscale geometrical details of the brain including the distinction between white and grey matter, sulci and gyri, the ventricular system, foramen magnum, and cerebrospinal fluid. The heterogeneity and anisotropy from diffusion tensor imaging data were incorporated by applying a one-to-one voxel-based correspondence between diffusion voxels and finite elements. The voxel resolution of the model was optimized to obtain a trade-off between reduced computational cost and higher geometrical details. Three sets of constitutive material properties were extracted from the literature to validate...
Introducing a new definition towards clinical detection of microvascular changes using diffusion and perfusion MRI
, Article Scientia Iranica ; Volume 12, Issue 1 , 2005 , Pages 109-115 ; 10263098 (ISSN) ; Jiang, Q ; Chopp, M ; Jahed, M ; Sharif University of Technology
Sharif University of Technology
2005
Abstract
Based on MRI diffusion and perfusion, a new criterion for detection and the healing progress of damaged tissue is suggested. The study is based on the ratio of capillary radii in symmetrical damaged and normal tissue neighboring spaces. The Apparent Diffusion Coefficient (ADC) and Cerebral Blood Flow (CBF) were measured in the brain tissues of six male Wistar rats utilizing suggested MRI measurement techniques. The ADC values of damaged and normal regions were (392 ± 34.1) × 10-6 mm2s-1 and (659 ± 40.7) × 10-6 mm2s-1, respectively. The CBF values of damaged and normal regions were 14.5 ± 10.13 ml/min/ 100 g and 125 ± 41.03 ml/min/100 g, respectively. The geometrical parameters of the...
Spatiotemporal signatures of surprise captured by magnetoencephalography
, Article Frontiers in Systems Neuroscience ; Volume 16 , 2022 ; 16625137 (ISSN) ; Kiani, M. M ; Aghajan, H ; Sharif University of Technology
Frontiers Media S.A
2022
Abstract
Surprise and social influence are linked through several neuropsychological mechanisms. By garnering attention, causing arousal, and motivating engagement, surprise provides a context for effective or durable social influence. Attention to a surprising event motivates the formation of an explanation or updating of models, while high arousal experiences due to surprise promote memory formation. They both encourage engagement with the surprising event through efforts aimed at understanding the situation. By affecting the behavior of the individual or a social group via setting an attractive engagement context, surprise plays an important role in shaping personal and social change. Surprise is...
Resting-State electroencephalogram (EEG) coherence over frontal regions in paranormal beliefs
, Article Basic and Clinical Neuroscience ; Volume 13, Issue 4 , 2022 , Pages 573-584 ; 2008126X (ISSN) ; Hatami, J ; Khosrowabadi, R ; Sohrabi, A ; Sharif University of Technology
Iran University of Medical Sciences
2022
Abstract
Introduction: Paranormal beliefs are defined as the belief in extrasensory perception, precognition, witchcraft, and telekinesis, magical thinking, psychokinesis, superstitions. Previous studies corroborate that executive brain functions underpin paranormal beliefs. To test this hypotheses, neurophysiological studies of brain activity are required. Methods: A sample of 20 students (10 girls, Mean±SD age: 22.50±4.07 years) were included in the current study. The absolute power of resting-state electroencephalogram (EEG) was analyzed in intra-hemispheric and inter-hemispheric coherence with eyes open. The paranormal beliefs were determined based on the total score of the revised paranormal...
Effective connectivity inference in the whole-brain network by using rDCM method for investigating the distinction between emotional states in fMRI data
, Article Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization ; 2022 ; 21681163 (ISSN) ; Ghahari, S ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
Taylor and Francis Ltd
2022
Abstract
In recent years, the regression dynamic causal modelling (rDCM) method was introduced as a new version of dynamic causal modelling (DCM) to derive effective connectivity in whole-brain networks for functional magnetic resonance imaging (fMRI) data. In this research, we used data obtained while applying the stimulation of audio movie comprised different emotional states. We applied this method to two networks consisting of ten auditory and forty-four regions, respectively. This method was used to study effective connections between emotional states and represent the distinction between emotions. Finally, significant effective connections were found in emotional processing and auditory...
Wavelet-Based biphase analysis of brain rhythms in automated wake-sleep classification
, Article International Journal of Neural Systems ; 2022 ; 01290657 (ISSN) ; Makkiabadi, B ; Shamsollahi, M. B ; Reisi, P ; Kermani, S ; Sharif University of Technology
World Scientific
2022
Abstract
Many studies in the field of sleep have focused on connectivity and coherence. Still, the nonstationary nature of electroencephalography (EEG) makes many of the previous methods unsuitable for automatic sleep detection. Time-frequency representations and high-order spectra are applied to nonstationary signal analysis and nonlinearity investigation, respectively. Therefore, combining wavelet and bispectrum, wavelet-based bi-phase (Wbiph) was proposed and used as a novel feature for sleep-wake classification. The results of the statistical analysis with emphasis on the importance of the gamma rhythm in sleep detection show that the Wbiph is more potent than coherence in the wake-sleep...