Sharif Digital Repository

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... 

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... 

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... 

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... 

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... 

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... 

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... 

Neurological disorders and nerve injuries, such as spinal cord injury, stroke, and multiple sclerosis can result in the loss of muscle function. Electrical stimulation of the neuronal cells is the currently available clinical treatment in this regard. As an effective energy harvester, the triboelectric nanogenerators (TENG) can be used for self-powered neural/muscle stimulations because the output of the TENG provides stimulation pulses for nerves. In the present study, using a computational modelling approach, the effect of surface micropatterns on the electric field distribution, induced voltage and capacitance of the TENG structures have been investigated. By incorporating the effect of... 

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... 

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  

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... 

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... 

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... 

It has been observed experimentally that the neural tissues generate highly variable and scale-free distributed outbursts of activity both in vivo and in vitro. Understanding whether these heterogeneous patterns of activity come from operation of the brain at the edge of a phase transition is an interesting possibility. Therefore, constructing a simple model that exhibits such behavior is of great interest. Additionally, the presence of both critical behavior and oscillatory patterns in brain dynamics is a very interesting phenomenon: Oscillatory patterns define a temporal scale, while criticality imposes scale-free characteristics. In this paper, we consider a model for a neuronal... 

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... 

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...