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Modeling of Nerve System by Differential Equations Theory
, M.Sc. Thesis Sharif University of Technology ; Fotuhi, Morteza (Supervisor)
Abstract
In this thesis, behavior of a single neuron and a collection of neurons, has modeled by using ordinary differential equation techniques. In modeling of a single neuron, cell’s potential and parameters related to ion’s are variables of differential equation. Often some of this variables change very faster than the others. That causes using small perturbation methods in modeling. The Hodgkin-hoxley equations identified as main model and its reduced models used in thesis. In modeling of a network of neurons, there are also synaptic and neuron variables and also they change in different speeds, too. That again leads us to small perturbation theory. Synapses are hyperpolarizing and depolarizing....
Designing Electrophysiological Characterization System of Biological Cells Based on the Use of Nanostructured Electrodes
, Ph.D. Dissertation Sharif University of Technology ; Vossoughi, Manouchehr (Supervisor) ; Sasanpour, Pezhman (Supervisor) ; Mohammadpour, Raheleh (Supervisor)
Abstract
In the last half century, the recording of the electrophysiological activities of the neurons has been one of the most effective methods for neuroscience development. One of the techniques used to record the activity of the nerve cells is the use of multi-electrode arrays (MEAs). Current MEAs still face limitations such as low signal-to-noise ratio (SNR) and low spatial resolution. There is a need to develop arrays that are smaller in size and have less impedance to achieve better spatial resolution and lower noise levels. The main focus of this research is on the designing and fabrication of multi-electrode arrays and improvement of their properties using nanostructures and conductive...
Design and Fabrication of Nanocamposite Scaffold for Neural Tissue Engineering
, M.Sc. Thesis Sharif University of Technology ; Ramazani Saadat Abadi , Ahmad (Supervisor) ; Neamati, ZiaratAli (Supervisor)
Abstract
Nervous system plays an intricate biological process of man body.Damage of nervous system has serious consequences and is hard to recover, as well as the other parts of the body may not work properly. Many strategies have been used to repair spinal cord injuries in which the main objective is to improve the regeneration of axons and functional recovery. The purpose of this research is introducing neural tissue engineering concepts (e.g. scaffolds, stimulation and etc.) and also design and fabrication suitable for neural tissue engineering. For this purpose the combination of biodegradable polymers (chitosan),conductive polymer (poly-aniline) and carbon nanosheets (graphene) was chosen as the...
Modeling of Human Decision Making in Problem Solving Based on Physiological Models of Neuron
, M.Sc. Thesis Sharif University of Technology ; Vosughi Vahdat, Bijan (Supervisor)
Abstract
How the human nervous system works is one of the most important topics in science and in this topic providing a model of it is scientists' main concern. The human brain that has been formed from a large number of nerve cells lets it do complex computations. The structure of cognition, memorizing and processing which are some of human features are being studied in many fields of science named "brain and cognitive science".
In this study, we will point to modeling of one of the human cognitive phenomena (decision making in problem solving). In this modeling, we aim to connect the microscopic and macroscopic levels of the nervous system to each other.
First, we will give an introduction...
In this study, we will point to modeling of one of the human cognitive phenomena (decision making in problem solving). In this modeling, we aim to connect the microscopic and macroscopic levels of the nervous system to each other.
First, we will give an introduction...
Discrete Hardware Neural Networks for Civil Engineering Application
, M.Sc. Thesis Sharif University of Technology ; Joghataie, Abdolreza (Supervisor)
Abstract
In recent years considerable effort has been made to advance the hardware Artificial Neural Networks where in most cases, many neurons are placed on a VLSI chip. This research attempts to build individual neurons and connect them to build a hybrid of analog and digital neural network. In fact, every neuron is an AVR microcontroller which has a number of inputs and outputs. The data transfer between neurons are done by both analog (PWM- ADC) and digital (UART). In the first part, the necessary voltage source and programmer and how to build are discussed. Next A\D convertor, PWM technique, UART and their usage in this project are demonstrated. After that, the neurons are calibrated to improve...
Statistical Mechanics of The Neocortex
, M.Sc. Thesis Sharif University of Technology ; Bahraini, Alireza (Supervisor)
Abstract
In this paper, the field theory tools will be used to study none-equilibrium statistical processes and eventually analyze the dynamic of the neocortex. Assuming the neocortex is Markovian, a model is proposed which contains fluctuations of the neuron activities as well as response to the stimulations. The experimental data shows that the fluctuation and correlation have a vital impact on the dynamics of the cortex, and many of its characteristics can only be justified by it.The paper will study the model that considers correlations and fluctuations to reach a more accurate evaluation of the cortex than the mean field approximation
The Effect of the Threshold Parameter on the Statistics of Neuronal Avalanches in the Rotational Model
, M.Sc. Thesis Sharif University of Technology ; Moghimi Araghi, Saman (Supervisor)
Abstract
There are experiments that conclude the brain is in the critical or near the critical region. These researches extract avalanches from the neuronal activity and then show that avalanche size (or duration) distribution obeys the power-law distribution. Defining avalanches from neuronal activity has some challenges. In some cases deciding the threshold (which determines the beginning and end of an avalanche) seems arbitrary or fine-tuned. In this thesis, we will show how different thresholds for defining avalanche and different time resolutions for defining neuronal activity can change avalanche size (or duration) distribution
Three dimensional modeling of axonal microtubules
, Article 2014 21st Iranian Conference on Biomedical Engineering, ICBME 2014, 26 November 2014 through 28 November 2014 ; November , 2014 , Pages 298-302 ; 9781479974177 (ISBN) ; Shamloo, A ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2014
Abstract
Axon is a filament in neuronal system and axonal microtubules are bundles in axons. In axons, microtubules are coated with microtubule-associated protein tau, a natively unfolded profuse filamentous protein in the central nervous system. These proteins are responsible for the cross-linked structure of the axonal microtubule bundles. Through complimentary dimerization with other tau proteins, bridges are formed to nearby microtubules to create bundles. The transverse reinforcement of microtubules by cross-linking to the cytoskeleton has been shown to enhance their ability to bear compressive loads. Though microtubules are conventionally regarded as bearing compressive loads, in certain...
Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis
, Article Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference ; 2010 , Pages 5573-5576 ; 1557170X (ISSN) ; Torabi, M ; Kim, K ; Sharif University of Technology
2010
Abstract
In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2-weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions...
Salience memories formed by value, novelty and aversiveness jointly shape object responses in the prefrontal cortex and basal ganglia
, Article Nature Communications ; Volume 13, Issue 1 , 2022 ; 20411723 (ISSN) ; Hikosaka, O ; Sharif University of Technology
Nature Research
2022
Abstract
Ecological fitness depends on maintaining object histories to guide future interactions. Recent evidence shows that value memory changes passive visual responses to objects in ventrolateral prefrontal cortex (vlPFC) and substantia nigra reticulata (SNr). However, it is not known whether this effect is limited to reward history and if not how cross-domain representations are organized within the same or different neural populations in this corticobasal circuitry. To address this issue, visual responses of the same neurons across appetitive, aversive and novelty domains were recorded in vlPFC and SNr. Results showed that changes in visual responses across domains happened in the same rather...
Colorimetric assay for exon 7 SMN1/SMN2 single nucleotide polymorphism using gold nanoprobes
, Article BioImpacts ; Volume 3, Issue 4 , 2013 , Pages 185-194 ; 22285652 (ISSN) ; Hormozi Nezhad, M. R ; Abadi, A ; Sanati, M. H ; Kazemi, B ; Sharif University of Technology
2013
Abstract
Introduction: Proximal spinal muscular atrophy (SMA) is one of the most significant neurodegenerative diseases amongst the autosomal-recessive genetic disorders which is caused by the absence of protein survival of motor neuron (SMN). A critical nucleotide difference in SMN2 compared to SMN1 gene leads to an inefficient protein. Hence, homozygous lack of SMN1 provides a progressive disease. Due to the high prevalence, up to now, several molecular diagnostic methods have been used which most of them are lengthy, expensive, and laborious. Methods: In the present study, we exploited a gold nanoprobe-based method for semi-quantitative SMN1 gene dosage analysis compared to SMN2. The assay was...
Application of generalized neuron in electricity price forecasting
, Article 2009 IEEE Bucharest PowerTech: Innovative Ideas Toward the Electrical Grid of the Future, 28 June 2009 through 2 July 2009, Bucharest ; 2009 ; 9781424422357 (ISBN) ; Sahari, A. A ; Sharif University of Technology
Abstract
With recent deregulation in electricity industry, price forecasting has become the basis for this competitive market. The precision of this forecasting is essential in bidding strategies. So far, the artificial neural networks which can find an accurate relation between the historical data and the price have been used for this purpose. One major problem is that, they usually need a large number of training data and neurons either for complex function approximation and data fitting or classification and pattern recognition. As a result, the network topology has a significant impact on the network computational time and ability to learn and also to generate unseen data from training data. To...
Introducing a comprehensive framework to measure spike-LFP coupling
, Article Frontiers in Computational Neuroscience ; Volume 12 , 2018 ; 16625188 (ISSN) ; Jahed, M ; Daliri, M. R ; Sharif University of Technology
Frontiers Media S.A
2018
Abstract
Measuring the coupling of single neuron's spiking activities to the local field potentials (LFPs) is a method to investigate neuronal synchronization. The most important synchronization measures are phase locking value (PLV), spike field coherence (SFC), pairwise phase consistency (PPC), and spike-triggered correlation matrix synchronization (SCMS). Synchronization is generally quantified using the PLV and SFC. PLV and SFC methods are either biased on the spike rates or the number of trials. To resolve these problems the PPC measure has been introduced. However, there are some shortcomings associated with the PPC measure which is unbiased only for very high spike rates. However evaluating...
A new nonlinear sparse component analysis for a biologically plausible model of neurons
, Article Neural Computation ; Volume 31, Issue 9 , 2019 , Pages 1853-1873 ; 08997667 (ISSN) ; Jahed, M ; Ghazizadeh, A ; Sharif University of Technology
MIT Press Journals
2019
Abstract
It is known that brain can create a sparse representation of the environment in both sensory and mnemonic forms (Olshausen & Field, 2004). Such sparse representation can be combined in downstream areas to create rich multisensory responses to support various cognitive and motor functions. Determining the components present in neuronal responses in a given region is key to deciphering its functional role and connection with upstream areas. One approach for parsing out various sources of information in a single neuron is by using linear blind source separation (BSS) techniques. However, applying linear techniques to neuronal spiking activity is likely to be suboptimal due to inherent and...
Identification of the appropriate architecture of multilayer feed-forward neural network for estimation of NPPs parameters using the GA in combination with the LM and the BR learning algorithms
, Article Annals of Nuclear Energy ; Volume 156 , 2021 ; 03064549 (ISSN) ; Sharif University of Technology
Elsevier Ltd
2021
Abstract
In this study, accurate estimation of nuclear power plant (NPP) parameters is done using the new and simple technique. The proposed technique using the genetic algorithm (GA) in combination with the Bayesian regularization (BR) and Levenberg- Marquardt (LM) learning algorithms identifies the appropriate architecture for estimation of the target parameters. In the first step, the input patterns features are selected using the features selection (FS) technique. In the second step, the appropriate number of hidden neurons and hidden layers are investigated to provide a more efficient initial population of the architectures. In the third step, the estimation of the target parameter is done using...
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...
Digital implementation of a biological astrocyte model and its application
, Article IEEE Transactions on Neural Networks and Learning Systems ; Volume 26, Issue 1 , 2014 , Pages 127-139 ; 2162237X (ISSN) ; Bavandpour, M ; Ahmadi, A ; Abbott, D ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2014
Abstract
This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm...
Design an Artificial Neural Structure by Using Mirror Neurons for Implementing the Ink Drop Spread (I.D.S) Operator in Active Learning Method Algorithm
, M.Sc. Thesis Sharif University of Technology ; Bagheri Shouraki, Saeed (Supervisor)
Abstract
In this research, a study on Mirror Neuron and Active learning method was done based on the human capability of using their past knowledge in order to understand new systems faster and with more accuracy. Mirror ALM, A new modeling technique based on ALM was proposed that is capable of merging the IDS planes of an old system in order to improve the output of the modeling for a new system. This new technique was tested on a 3D function, state estimation of an inverted pendulum and finally in control procedure of an inverted pendulum. The results of the tests were compared with the classic ALM method to recognize the advantages and disadvantages of the introduced method. The results showed...
Fabricating Graphene Paper and Determining Its Electrical and Mechanical Properties and Using It for Proliferation and Differentiation of Neural Stem Cells
, M.Sc. Thesis Sharif University of Technology ; Akhavan, Omid (Supervisor)
Abstract
Nowadays, tissue engineering and stem cells-based therapies have outlined a promising prospect in neural networks regeneration. But it usually requires biocompatible and conductive scaffolds for culturing neural stem cells and directing their differentiation toward the neurons. Graphene due to its unique physical and chemical properties has attracted much interest in tissue engineering. For this purpose, in this study biocompatible graphene oxide foams have been used for neural stem cell culturing. For the first time, graphene oxide foam were fabricated by precipitation of chemically exfoliated graphene oxide sheets in an aqueous suspension onto the PET substrate at ~80 oC under UV...
Modeling Neural Systems with a Group of Dissipative Rotators
, M.Sc. Thesis Sharif University of Technology ; Moghimi-Araghi, Saman (Supervisor)
Abstract
Neural systems are the threshold ones. It means if the electrical potential passed through a specific amount then they could spike and cause the activity of other cells. The models that really mimic the action of neurons are usually complicated and are not suitable when you put them on a network to study the collective behavior of the neurons. Simple threshold models have been designed for such purposes. One on most studied ones is the ’integrate and fire’ model, in which cells integrate the inputs until the threshold potential and then spike. usually, a network of these objects are simulated and different properties of such network are investigated.However, this model has some shortcomings...