Sharif Digital Repository

Excitability is one of the most important characteristics of a neuron. In 1948, Hodgkin identified three different types of excitability of neurons. Excitability an all of its types can be observed in Hodgkin-Huxley model of neuronal dynamics (H-H model) as a four-dimensional system of differential equations and in at least two dimensional reductions of H-H type models. By applying the theory of dynamical systems (e.g. bifurcation theory), one can give a mathematical definition of excitability.Excitability of the neuron is equivalent to that the neuronal model is near a bifurcation through which the state of the system approaches to a stable limit cycle. In this thesis, a two-dimensional... 

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.... 

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... 

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... 

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  

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  

Centuries passed and the human knew himself as the protagonist who searches around nature and discovers the phenomena. But after the birth of ``neuroscience", his wisdom and the process of reasoning were also added to the list of uncovered subjects. Since its arrival, many scientists started investigating ``reasoning", "sleep", ``memory disorders" etc. with a such framework. One of the main branches of this stream is the ``Synchronization" problem when the neurons get synced in the matter of spiking likelihood. ``Synchronization" means a lot to the community, because it is said that it is one major symptom of Epilepsy. With that said, we need to get to the root of this effect. It seems... 

Based on the large number of interacting cells and their abundant connections, human brain is a complex system able to produce interesting collective behaviors. Studying these collective behaviors needs special tools that potentially could be found in the context of the statistical physics of critical phenomena, as these tools are specifically developed for understanding the large-scale properties of physical systems. Starting with the introduction of the self-organized criticality in the late 80s, a number of physicists have tried to utilize this concept for explaining some aspects of the brain properties, such as memory and learnig. The observation of the neuronal avalanches in the early... 

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... 

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... 

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... 

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... 

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... 

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... 

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... 

Scientists have discovered by researching the human brain that full knowledge of the functions of the human brain is beyond what they have ever imagined. Much has been learned about the brain and structure of the human nervous system today, but complete structural implementation with the complexity of the human brain is not possible with today's information and technology.One of the biggest struggles while working with artificial neural networks is being able to come up with models which closely match biological observations. Biological neural networks seem to capable of creating and pruning dendritic spines, leading to synapses being changed, which may result in higher learning capability.... 

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... 

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... 

In neural systems of living organism, moreover than differences in anatomic structure of cells, there is also differences in physiological functions of analogous cells.Specification and categorization of neurons based on physiological functions is one of objectives of neuroscience. Study of cognitive behaviors and systematic study of neural system, modeling and practical applications in neural prosthesis design are some of applications of categorizing neural cells. Neural signals can be studied by Spike rate of a single neuron activity or Local Field Potential (LFP) of a finite number of neurons. In previous studies neurons of first visual cortex are divided into two groups of simple and... 

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...