Search for: neurons
0.007 seconds
Total 87 records

    Computational modeling of neuronal current MRI signals with Rat somatosensory cortical neurons

    , Article Interdisciplinary Sciences: Computational Life Sciences ; Volume 8, Issue 3 , 2016 , Pages 253-262 ; 19132751 (ISSN) BagheriMofidi, S. M ; Pouladian, M ; Jameie, S. B ; Abbaspour Tehrani Fard, A ; Sharif University of Technology
    International Association of Scientists in the International Association of Scientists in the  2016
    Magnetic field generated by active neurons has recently been considered to determine location of neuronal activity directly with magnetic resonance imaging (MRI), but controversial results have been reported about detection of such small magnetic fields. In this study, multiple neuronal morphologies of rat tissue were modeled to investigate better estimation of MRI signal change produced by neuronal magnetic field (NMF). Ten pyramidal neurons from layer II to VI of rat somatosensory area with realistic morphology, biophysics, and neuronal density were modeled to simulate NMF of neuronal tissue, from which effects of NMF on MRI signals were obtained. Neuronal current MRI signals, which... 

    A survey on talamocortical activity of ADHD patients based on mean-field bursting model

    , Article 10th IEEE International Workshop on Biomedical Engineering, BioEng 2011, Kos Island, 5 October 2011 through 7 October 2011 ; 2011 ; 9781457705526 (ISBN) Arasteh, A ; Janghorbani, A ; Vahdat, B. V ; University of Patras; University of Ioannina; National Technical University of Athens; University of Thessaly; Univ. Ioannina, Unit Med. Technol. Intelligent Inf. Syst ; Sharif University of Technology
    Modeling is one of assessing tools for better understanding of human body organs and study of diseases. One of the brain diseases is ADHD, which has been studied before, mostly by means of EEG signals. In this paper, the mean-field model, which is a model of neuron-population spiking, and the Power Spectrum of the resulting spikes have been studied by changing parameters of model. The results show that there is a meaningful relationship between firing activity of ADHD patients neuron population and the parameters of mean-field model and Power Spectrum of spikes. In addition, the effects of stimulant medications for ADHD patients on firing activity and power spectrum of firing activity of... 

    Cellular Memristive Dynamical Systems (CMDS)

    , Article International Journal of Bifurcation and Chaos ; Vol. 24, issue. 5 , May , 2014 Bavandpour, M ; Soleimani, H ; Bagheri-Shouraki, S ; Ahmadi, A ; Abbott, D ; Chua, L. O ; Sharif University of Technology
    This study presents a cellular-based mapping for a special class of dynamical systems for embedding neuron models, by exploiting an efficient memristor crossbar-based circuit for its implementation. The resultant reconfigurable memristive dynamical circuit exhibits various bifurcation phenomena, and responses that are characteristic of dynamical systems. High programmability of the circuit enables it to be applied to real-time applications, learning systems, and analytically indescribable dynamical systems. Moreover, its efficient implementation platform makes it an appropriate choice for on-chip applications and prostheses. We apply this method to the Izhikevich, and FitzHugh-Nagumo neuron... 

    Biologically inspired spiking neurons: Piecewise linear models and digital implementation

    , Article IEEE Transactions on Circuits and Systems I: Regular Papers ; Volume 59, Issue 12 , 2012 , Pages 2991-3004 ; 15498328 (ISSN) Soleimani, H ; Ahmadi, A ; Bavandpour, M ; Sharif University of Technology
    There has been a strong push recently to examine biological scale simulations of neuromorphic algorithms to achieve stronger inference capabilities. This paper presents a set of piecewise linear spiking neuron models, which can reproduce different behaviors, similar to the biological neuron, both for a single neuron as well as a network of neurons. The proposed models are investigated, in terms of digital implementation feasibility and costs, targeting large scale hardware implementation. Hardware synthesis and physical implementations on FPGA show that the proposed models can produce precise neural behaviors with higher performance and considerably lower implementation costs compared with... 

    Dynamics of Delayed Neuronal Systems

    , Ph.D. Dissertation Sharif University of Technology Farajzadeh Tehrani, Niloofar (Author) ; Razvan, Mohammad Reza (Supervisor)
    This thesis presents an investigation of the dynamics of two coupled non-identical FitzHugh–Nagumo neurons. It is known that signal transmission in coupled neurons is not instantaneous in general, and time delay is inevitable in signal transmission for real neurons. Therefore we consider the system of two coupled neurons with delayed synaptic connection. We consider coupling strength and time delay as bifurcation parameters, and try to classify all possible dynamics which is fairly rich and we will study the excitability of the neurons. By bifurcation study of the system the coupling strength and delay-dependent stability regions are illustrated in the parameter plane, to describe typical... 

    Collective behavior of interacting locally synchronized oscillations in neuronal networks

    , Article Communications in Nonlinear Science and Numerical Simulation ; Volume 17, Issue 10 , 2012 , Pages 3922-3933 ; 10075704 (ISSN) Jalili, M ; Sharif University of Technology
    Elsevier  2012
    Local circuits in the cortex and hippocampus are endowed with resonant, oscillatory firing properties which underlie oscillations in various frequency ranges (e.g. gamma range) frequently observed in the local field potentials, and in electroencephalography. Synchronized oscillations are thought to play important roles in information binding in the brain. This paper addresses the collective behavior of interacting locally synchronized oscillations in realistic neural networks. A network of five neurons is proposed in order to produce locally synchronized oscillations. The neuron models are Hindmarsh-Rose type with electrical and/or chemical couplings. We construct large-scale models using... 

    Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

    , Article Australasian Physical and Engineering Sciences in Medicine ; Volume 39, Issue 3 , 2016 , Pages 717-726 ; 01589938 (ISSN) BagheriMofidi, S. M ; Pouladian, M ; Jameie, S. B ; Abbaspour Tehrani Fard, A ; Sharif University of Technology
    Springer Netherlands  2016
    Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000... 

    Evaluation of differentiation quality of several differentiation inducers of bone marrow-derived mesenchymal stem cells to nerve cells by as-sessing expression of beta-tubulin 3 marker: A systematic review

    , Article Current Stem Cell Research and Therapy ; Volume 16, Issue 8 , 2021 , Pages 994-1004 ; 1574888X (ISSN) Karami Fath, M ; Zahedi, F ; Hashemi, Z. S ; Khalili, S ; Sharif University of Technology
    Bentham Science Publishers  2021
    Neurological diseases have different etiological causes. Contemporary, developing an ef-fective treatment for these diseases is an ongoing challenge. Cell therapy is recognized as one of the promising solutions for the treatment of these diseases. Amongst various types of stem cells, bone marrow-derived mesenchymal stem cells (BM-MSC) are known to be the most widely used stem cells. These cells are endowed with appealing properties such as the ability to differentiate into other cell types, including the muscle, liver, glial, and nerve cells. In this review study, we have systematically evaluated the ability of a variety of chemical compounds used in the last ten years to differentiate... 

    Design and Fabrication of 2D Multi-Electrode Array (MEA)Devices to Evaluate Functionality of Neural Cell Network and Cardiac Cells

    , M.Sc. Thesis Sharif University of Technology Ahmadvand, Tala (Author) ; Fardmanesh, Mehdi (Supervisor)
    Microelectrode array technology is a broad platform for studying and characterization of the electrophysiological properties of excitable tissues derived from both brain and heart in vitro. Microelectrode arrays can either record or stimulate cells by accessing multiple sites of neural networks and cell tissues and detect signals from all sources around each electrode simultaneously. By using recorded signals, one can detect cell potential changes and its minor fluctuations. Unlike intracellular recording techniques, the noninvasive interface of microelectrode arrays with cells provides durable studying of neural networks and cell tissues. Using related microelectronics fabrication... 

    Geometrical Structure of Neuron Morphology

    , Ph.D. Dissertation Sharif University of Technology Farhoodi, Roozbeh (Author) ; Fotouhi, Morteza (Supervisor)
    The tree structure of neuron morphologies has excited neuroscientists since their discovery in the 19-th century. Many theories assign computational meaning to morphologies, but it is still hard to generate realistic looking morphologies. There are a few growth models for generating neuron morphologies that correctly reproduce some features (e.g. branching angles) of morphologies, but they tend to fall short on other features. Here we present an approach that builds a generative model by extracting a set of human-chosen features from a database of neurons by using the naïve Bayes approach. Then by starting from a neuron with a soma we use statistical sampling techniques to generate... 

    The use of graphene in the self-organized differentiation of human neural stem cells into neurons under pulsed laser stimulation

    , Article Journal of Materials Chemistry B ; Vol. 2, Issue. 34 , 2014 , Pages 5602-5611 ; ISSN: 20507518 Akhavan, O ; Ghaderi, E ; Sharif University of Technology
    An effective and self-organized differentiation of human neural stem cells (hNSCs) into neurons was developed by the pulsed laser stimulation of the cells on graphene films (prepared by drop-casting a GO suspension onto quartz substrates). The effects of graphene oxide (GO) and hydrazine-reduced graphene oxide (rGO) sheets on the proliferation of hNSCs were examined. The higher proliferation of the cells on the GO was assigned to its better hydrophilicity. On the other hand, the rGO sheets, which have significantly better electrical conductivity than GO, exhibited more differentiation of the cells into neurons. The pulsed laser stimulation not only resulted in an accelerated differentiation... 

    Prediction of wax disappearance temperature using artificial neural networks

    , Article Journal of Petroleum Science and Engineering ; Volume 108 , 2013 , Pages 74-81 ; 09204105 (ISSN) Moradi, G ; Mohadesi, M ; Moradi, M. R ; Sharif University of Technology
    In this study, the artificial neural network (ANN) was used for the prediction of WDT. The inputs to network are molar mass and pressure, and the output is WDT at each input. A two-layer network with different hidden neurons and different learning algorithms such as LM, SCG, GDA and BR were examined. The network with 16 hidden neurons and Levenberg-Marquardt (LM) train function showed the best results in comparison with the other networks. Also, the predicted results of this network were compared with the thermodynamic models and better accordance with experimental data for ANN was concluded  

    Hammerstein-Wiener model: A new approach to the estimation of formal neural information

    , Article Basic and Clinical Neuroscience ; Volume 3, Issue 4 , 2012 , Pages 45-51 ; 2008126X (ISSN) Abbasi Asl, R ; Khorsandi, R ; Vosooghi Vahdat, B ; Sharif University of Technology
    A new approach is introduced to estimate the formal information of neurons. Formal Information, mainly discusses about the aspects of the response that is related to the stimulus. Estimation is based on introducing a mathematical nonlinear model with Hammerstein-Wiener system estimator. This method of system identification consists of three blocks to completely describe the nonlinearity of input and output and linear behaviour of the model. The introduced model is trained by 166 spikes of neurons and other 166 spikes are used to test and validate the model. The simulation results show the R-Value of 92.6 % between estimated and reference information rate. This shows improvement of 1.41 % in... 

    Graphene scaffolds in progressive nanotechnology/stem cell-based tissue engineering of the nervous system

    , Article Journal of Materials Chemistry B ; Volume 4, Issue 19 , 2016 , Pages 3169-3190 ; 20507518 (ISSN) Akhavan, O ; Sharif University of Technology
    Royal Society of Chemistry  2016
    Although graphene/stem cell-based tissue engineering has recently emerged and has promisingly and progressively been utilized for developing one of the most effective regenerative nanomedicines, it suffers from low differentiation efficiency, low hybridization after transplantation and lack of appropriate scaffolds required in implantations without any degrading in functionality of the cells. In fact, recent studies have demonstrated that the unique properties of graphene can successfully resolve all of these challenges. Among various stem cells, neural stem cells (NSCs) and their neural differentiation on graphene have attracted a lot of interest, because graphene-based neuronal tissue... 

    Emergence of bursting in two coupled neurons of different types of excitability

    , Article Chaos, Solitons and Fractals ; Volume 132 , 2020 Razvan, M. R ; Yasaman, S ; Sharif University of Technology
    Elsevier Ltd  2020
    In this manuscript, a spiking neuron of type I excitability and a silent neuron of type II excitability are coupled through a gap junction with unequal coupling strengths, and none of the coupled neurons can burst intrinsically. By applying the theory of dynamical systems (e.g. bifurcation theory), we investigate how the coupling strength affects the dynamics of the neurons, when one of the coupling strengths is fixed and the other varies. We report four different regimes of oscillations as the coupling strength increases. (1) Spike–Spike Phase–Locking, where both neurons are in tonic spiking mode but with different frequencies; (2) Spike–Burst mode, where the type II neuron bursts while the... 

    Nonlinear behavior of memristive devices during tuning process and its impact on STDP learning rule in memristive neural networks

    , Article Neural Computing and Applications ; Vol. 26, issue. 1 , 2014 , p. 67-75 Merrikh Bayat, F ; Shouraki, S. B ; Sharif University of Technology
    It is now widely accepted that memristive devices are promising candidates for the emulation of the behavior of biological synapses in neuromorphic systems. This is mainly because of the fact that like the strength of synapse, memristance of the memristive device can be tuned actively for example by the application of voltage or current. In addition, it is also possible to fabricate high density of memristive devices through the nano-crossbar structures. In this paper, we will show that there are some problems associated with memristive devices, which are playing the role of biological synapses. For example, we show that the variation rate of the memristance depends completely on the initial... 

    A complementary method for preventing hidden neurons' saturation in feed forward neural networks training

    , Article Iranian Journal of Electrical and Computer Engineering ; Volume 9, Issue 2 , SUMMER-FALL , 2010 , Pages 127-133 ; 16820053 (ISSN) Moallem, P ; Ayoughi, S. A ; Sharif University of Technology
    In feed forward neural networks, hidden layer neurons' saturation conditions, which are the cause of flat spots on the error surface, is one of the main disadvantages of any conventional gradient descent learning algorithm. In this paper, we propose a novel complementary scheme for the learning based on a suitable combination of anti saturated hidden neurons learning process and accelerating methods like the momentum term and the parallel tangent technique. In our proposed method, a normalized saturation criterion (NSC) of hidden neurons, which is introduced in this paper, is monitored during learning process. When the NSC is higher than a specified threshold, it means that the algorithm... 

    Computational Modeling of Axonal Microtubule and Study the Effect of Cytoplasm on It under the Tension

    , M.Sc. Thesis Sharif University of Technology Manuchehrfar, Farid (Author) ; Shamloo, Amir (Supervisor)
    Axon is an important part of the neuronal cells and axonal microtubules are bundles in axons.In axons, microtubules are coated with microtubule-associated protein tau, a natively unfolded filamentous protein in the central nervous system. These proteins are responsible for cross-linking axonal microtubule bundles. Through complimentary dimerization with other tau proteins, bridges are formed between nearby microtubules creating bundles. Formation of bundles of microtubules causes their transverse reinforcement and has been shown to enhance their ability to bear compressive loads. Though microtubules are conventionally regarded as bearing compressive loads, in certain circumstances such as in... 

    Modeling of Nerve System by Differential Equations Theory

    , M.Sc. Thesis Sharif University of Technology Keramati Tavallayi, Mohammad Mahdi (Author) ; Fotuhi, Morteza (Supervisor)
    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 Vafaiee, Mohadeseh (Author) ; Vossoughi, Manouchehr (Supervisor) ; Sasanpour, Pezhman (Supervisor) ; Mohammadpour, Raheleh (Supervisor)
    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...