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1
Liao, James Yu-Chang. "Evaluating Multi-Modal Brain-Computer Interfaces for Controlling Arm Movements Using a Simulator of Human Reaching." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1404138858.
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Guillon, Didier. "Noos : Neural Object Oriented Simulator : un simulateur orienté objet d'un neurone biologique." Université Joseph Fourier (Grenoble), 1997. http://www.theses.fr/1997GRE19002.
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Xu, Shuxiang, University of Western Sydney, and of Informatics Science and Technology Faculty. "Neuron-adaptive neural network models and applications." THESIS_FIST_XXX_Xu_S.xml, 1999. http://handle.uws.edu.au:8081/1959.7/275.
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Artificial Neural Networks have been widely probed by worldwide researchers to cope with the problems such as function approximation and data simulation. This thesis deals with Feed-forward Neural Networks (FNN's) with a new neuron activation function called Neuron-adaptive Activation Function (NAF), and Feed-forward Higher Order Neural Networks (HONN's) with this new neuron activation function. We have designed a new neural network model, the Neuron-Adaptive Neural Network (NANN), and mathematically proved that one NANN can approximate any piecewise continuous function to any desired accuracy. In the neural network literature only Zhang proved the universal approximation ability of FNN Group to any piecewise continuous function. Next, we have developed the approximation properties of Neuron Adaptive Higher Order Neural Networks (NAHONN's), a combination of HONN's and NAF, to any continuous function, functional and operator. Finally, we have created a software program called MASFinance which runs on the Solaris system for the approximation of continuous or discontinuous functions, and for the simulation of any continuous or discontinuous data (especially financial data). Our work distinguishes itself from previous work in the following ways: we use a new neuron-adaptive activation function, while the neuron activation functions in most existing work are all fixed and can't be tuned to adapt to different approximation problems; we only use on NANN to approximate any piecewise continuous function, while a neural network group must be utilised in previous research; we combine HONN's with NAF and investigate its approximation properties to any continuous function, functional, and operator; we present a new software program, MASFinance, for function approximation and data simulation. Experiments running MASFinance indicate that the proposed NANN's present several advantages over traditional neuron-fixed networks (such as greatly reduced network size, faster learning, and lessened simulation errors), and that the suggested NANN's can effectively approximate piecewise continuous functions better than neural networks groups. Experiments also indicate that NANN's are especially suitable for data simulationDoctor of Philosophy (PhD)
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Preyer, Amanda Jervis. "Coupling and synchrony in neuronal networks electrophysiological experiments /." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/24799.
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Thesis (Ph.D.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2008.Committee Chair: Butera, Robert; Committee Member: Canavier, Carmen; Committee Member: DeWeerth, Stephen; Committee Member: Hasler, Paul; Committee Member: Lanterman, Aaron; Committee Member: Prinz, Astrid.
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Kulakov, Anton. "Multiprocessing neural network simulator." Thesis, University of Southampton, 2013. https://eprints.soton.ac.uk/348420/.
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Over the last few years tremendous progress has been made in neuroscience by employing simulation tools for investigating neural network behaviour. Many simulators have been created during last few decades, and their number and set of features continually grows due to persistent interest from groups of researchers and engineers. A simulation software that is able to simulate a large-scale neural network has been developed and presented in this work. Based on a highly abstract integrate-and-fire neuron model a clock-driven sequential simulator has been developed in C++. The created program is able to associate the input patterns with the output patterns. The novel biologically plausible learning mechanism uses Long Term Potentiation and Long Term Depression to change the strength of the connections between the neurons based on a global binary feedback. Later, the sequentially executed model has been extended to a multi-processor system, which executes the described learning algorithm using the event-driven technique on a parallel distributed framework, simulating a neural network asynchronously. This allows the simulation to manage larger scale neural networks being immune to processor failure and communication problems. The multi-processor neural network simulator has been created, the main benefit of which is the possibility to simulate large scale neural networks using high-parallel distributed computing. For that reason the design of the simulator has been implemented considering an efficient weight-adjusting algorithm and an efficient way for asynchronous local communication between processors.
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Chen, Prakoon. "The Neural Shell : a neural networks simulator." Connect to resource, 1989. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1228839518.
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Boatin, William. "Characterization of neuron models." Thesis, Available online, Georgia Institute of Technology, 2005, 2005. http://etd.gatech.edu/theses/available/etd-04182005-181732/.
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Thesis (M. S.)--Electrical and Computer Engineering, Georgia Institute of Technology, 2006.Dr. Robert H. Lee, Committee Member ; Dr. Kurt Wiesenfeld, Committee Member ; Dr Robert J. Butera, Committee Member.
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Liss, Anders. "Optimizing stochastic simulation of a neuron with parallelization." Thesis, Uppsala universitet, Avdelningen för beräkningsvetenskap, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324444.
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In order to optimize the solving of stochastic simulations of neuron channels, an attempt to parallelize the solver has been made. The result of the implementation was unsuccessful. However, the implementation is not impossible and is still a field of research with big potential for improving performance of stochastic simulations.
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Tang, Ping. "Simulation du traitement effectué par certaines cellules étoilées du noyau cochléaire antéroventral et analyse de leur comportement en terme de modulation d'amplitude /." Thèse, Chicoutimi : Université du Québec à Chicoutimi, 1995. http://theses.uqac.ca.
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Colbrunn, Robb William. "A Robotic Neuro-Musculoskeletal Simulator for Spine Research." Cleveland State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=csu1367977446.
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Sorensen, Michael Elliott. "Functional Consequences of Model Complexity in Hybrid Neural-Microelectronic Systems." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6908.
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Hybrid neural-microelectronic systems, systems composed of biological neural networks
and neuronal models, have great potential for the treatment of neural injury and
disease. The utility of such systems will be ultimately determined by the ability of the engineered
component to correctly replicate the function of biological neural networks. These
models can take the form of mechanistic models, which reproduce neural function by describing
the physiologic mechanisms that produce neural activity, and empirical models,
which reproduce neural function through more simplified mathematical expressions.
We present our research into the role of model complexity in creating robust and flexible
behaviors in hybrid systems. Beginning with a complex mechanistic model of a leech
heartbeat interneuron, we create a series of three systematically reduced models that incorporate
both mechanistic and empirical components. We then evaluate the robustness
of these models to parameter variation, and assess the flexibility of the models activities.
The modeling studies are validated by incorporating both mechanistic and semi-empirical
models in hybrid systems with a living leech heartbeat interneuron. Our results indicate
that model complexity serves to increase both the robustness of the system and the ability
of the system to produce flexible outputs.
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Littlewort, G. C. "Neural network analysis and simulation." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292677.
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Clay, Robert Christopher. "Computer models to simulate ion flow in neurons." Thesis, University of Nottingham, 2017. http://eprints.nottingham.ac.uk/42951/.
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In this thesis the Drift Diffusion enhanced Hodgkin Huxley model is developed. This model uses the Drift Diffusion equations to model the bulk solutions both within a neuron and in the surrounding extracellular media. The Hodgkin Huxley ion channel behaviour is incorporated into the membrane regions through the use of an altered diffusion coefficient. Firstly the model is applied to the case of intracellular and extracellular media separated by a single membrane. Secondly the model is applied to a cell within a restricted extracellular space. This takes a slice through a cell and is therefore termed a double membrane model, since there are two membrane layers. Finally the model is used to determine whether there is any charge and field buildup on a gold surface located 100 nm from the cell. The results from this could then be used in future to model Surface Plasmon Resonance experiments which may form the basis of novel neuronal activity detectors.
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Painkras, Eustace. "A chip multiprocessor for a large-scale neural simulator." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/a-chip-multiprocessor-for-a-largescale-neural-simulator(d3637073-2669-4a81-985a-2da9eec46480).html.
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A Chip Multiprocessor for a Large-scale Neural SimulatorEustace PainkrasA thesis submitted to The University of Manchesterfor the degree of Doctor of Philosophy, 17 December 2012The modelling and simulation of large-scale spiking neural networks in biologicalreal-time places very high demands on computational processing capabilities andcommunications infrastructure. These demands are difficult to satisfy even with powerfulgeneral-purpose high-performance computers. Taking advantage of the remarkableprogress in semiconductor technologies it is now possible to design and buildan application-driven platform to support large-scale spiking neural network simulations.This research investigates the design and implementation of a power-efficientchip multiprocessor (CMP) which constitutes the basic building block of a spikingneural network modelling and simulation platform. The neural modelling requirementsof many processing elements, high-fanout communications and local memoryare addressed in the design and implementation of the low-level modules in the designhierarchy as well as in the CMP. By focusing on a power-efficient design, the energyconsumption and related cost of SpiNNaker, the massively-parallel computation engine,are kept low compared with other state-of-the-art hardware neural simulators.The SpiNNaker CMP is composed of many simple power-efficient processors withsmall local memories, asynchronous networks-on-chip and numerous bespoke modulesspecifically designed to serve the demands of neural computation with a globallyasynchronous, locally synchronous (GALS) architecture.The SpiNNaker CMP, realised as part of this research, fulfills the demands of neuralsimulation in a power-efficient and scalable manner, with added fault-tolerancefeatures. The CMPs have, to date, been incorporated into three versions of SpiNNakersystem PCBs with up to 48 chips onboard. All chips on the PCBs are performing successfully, during both functional testing and their targeted role of neural simulation.
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Thakore, Vaibhav. "Nonlinear dynamic modeling, simulation and characterization of the mesoscale neuron-electrode interface." Doctoral diss., University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/5529.
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Extracellular neuroelectronic interfacing has important applications in the fields of neural prosthetics, biological computation and whole-cell biosensing for drug screening and toxin detection. While the field of neuroelectronic interfacing holds great promise, the recording of high-fidelity signals from extracellular devices has long suffered from the problem of low signal-to-noise ratios and changes in signal shapes due to the presence of highly dispersive dielectric medium in the neuron-microelectrode cleft. This has made it difficult to correlate the extracellularly recorded signals with the intracellular signals recorded using conventional patch-clamp electrophysiology. For bringing about an improvement in the signal-to-noise ratio of the signals recorded on the extracellular microelectrodes and to explore strategies for engineering the neuron-electrode interface there exists a need to model, simulate and characterize the cell-sensor interface to better understand the mechanism of signal transduction across the interface. Efforts to date for modeling the neuron-electrode interface have primarily focused on the use of point or area contact linear equivalent circuit models for a description of the interface with an assumption of passive linearity for the dynamics of the interfacial medium in the cell-electrode cleft. In this dissertation, results are presented from a nonlinear dynamic characterization of the neuroelectronic junction based on Volterra-Wiener modeling which showed that the process of signal transduction at the interface may have nonlinear contributions from the interfacial medium. An optimization based study of linear equivalent circuit models for representing signals recorded at the neuron-electrode interface subsequently proved conclusively that the process of signal transduction across the interface is indeed nonlinear. Following this a theoretical framework for the extraction of the complex nonlinear material parameters of the interfacial medium like the dielectric permittivity, conductivity and diffusivity tensors based on dynamic nonlinear Volterra-Wiener modeling was developed. Within this framework, the use of Gaussian bandlimited white noise for nonlinear impedance spectroscopy was shown to offer considerable advantages over the use of sinusoidal inputs for nonlinear harmonic analysis currently employed in impedance characterization of nonlinear electrochemical systems. Signal transduction at the neuron-microelectrode interface is mediated by the interfacial medium confined to a thin cleft with thickness on the scale of 20-110 nm giving rise to Knudsen numbers (ratio of mean free path to characteristic system length) in the range of 0.015 and 0.003 for ionic electrodiffusion. At these Knudsen numbers, the continuum assumptions made in the use of Poisson-Nernst-Planck system of equations for modeling ionic electrodiffusion are not valid. Therefore, a lattice Boltzmann method (LBM) based multiphysics solver suitable for modeling ionic electrodiffusion at the mesoscale neuron-microelectrode interface was developed. Additionally, a molecular speed dependent relaxation time was proposed for use in the lattice Boltzmann equation. Such a relaxation time holds promise for enhancing the numerical stability of lattice Boltzmann algorithms as it helped recover a physically correct description of microscopic phenomena related to particle collisions governed by their local density on the lattice. Next, using this multiphysics solver simulations were carried out for the charge relaxation dynamics of an electrolytic nanocapacitor with the intention of ultimately employing it for a simulation of the capacitive coupling between the neuron and the planar microelectrode on a microelectrode array (MEA). Simulations of the charge relaxation dynamics for a step potential applied at t = 0 to the capacitor electrodes were carried out for varying conditions of electric double layer (EDL) overlap, solvent viscosity, electrode spacing and ratio of cation to anion diffusivity. For a large EDL overlap, an anomalous plasma-like collective behavior of oscillating ions at a frequency much lower than the plasma frequency of the electrolyte was observed and as such it appears to be purely an effect of nanoscale confinement. Results from these simulations are then discussed in the context of the dynamics of the interfacial medium in the neuron-microelectrode cleft. In conclusion, a synergistic approach to engineering the neuron-microelectrode interface is outlined through a use of the nonlinear dynamic modeling, simulation and characterization tools developed as part of this dissertation research.Ph.D.
Doctorate
Physics
Sciences
Physics
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Osborne, Mark David. "Simulation of neutron radiation effects in silicon avalanche photodiodes." Thesis, Brunel University, 2000. http://bura.brunel.ac.uk/handle/2438/7398.
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A new one-dimensional device simulation package developed for the simulation of neutron radiatiol! effects in silicon avalanche photodiodes is described. The software uses a finite difference technique to solve the time-independent semiconductor equations across a user specified structure. Impact ionisation and illumination are included, allowing accurate simulation with minimal assumptions about the device under investigation. The effect of neutron radiation damage is incorporated via the introduction of deep acceptor levels subject to Shockley-Read-Hall statistics. Two models are presented. A reverse reach through model, based on the EG&G C30626E reverse reach through avalanche photo diode originally proposed for use in the CMS electromagnetic calorimeter, and a reach through model, based on widely available commerical devices. A short experimental study on two commercial silicon avalanche photodiodes, a C30719F reverse reach through APD and a C30916E reach through APD, is presented for comparison with the simulation data. To allow full comparison with the simulated predictions, the commercial devices were irradiated at the Rutherford Appleton Laboratory's ISIS facility. The simulated data shows good qualitative agreement with the measurements performed on the commercial devices, quantitative predictions would require exact information about the doping profile. The characteristic behaviour of the devices is predicted over a wide range of conditions both before and after neutron irradiation. The effect of ionised deep acceptors in the bulk of the devices is investigated. The simulation package provides a useful tool for the analysis of semiconductor devices, particularly in areas where a non-ionising radiation damage is prevelent e.g. high energy physics, and provides a good basis for further development.
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Tolo, Silvia. "Monte Carlo simulation of the WENDI-2 neutron dosimeter." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4035/.
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Il presente lavoro di tesi, sviluppato nell’arco di sei mesi presso l’Institut Supérieur Industriel de Bruxelles (ISIB) in collaborazione con Ion Beam Application Group (IBA, Louvain la Neuve), ha come principale soggetto lo studio della risposta del rem meter WENDI-2 commercializzato da Thermo Scientific. Lo studio si è basato principalmente sull’uso del codice Monte Carlo MCNPX 2.5.0, simulando la risposta del detector sia in caso di campi di radiazione neutronica monoenergetici sia in corrispondenza di spettri neutronici continui. La prima fase è stata dedicata alla modellizzazione MCNPX del rem counter, consentendo così la valutazione della sua funzione risposta. Questa è stata ricostruita interpolando 93 punti, ciascuno calcolato in corrispondenza di un singolo valore di energia di una sorgente puntiforme, compreso tra 1 meV e 5 GeV. In tal caso è stata rilevata un’ottima corrispondenza tra i risultati ottenuti e quelli riportati nella letteratura scientifica esistente. In una seconda fase, al fine di ottenere informazioni sulla risposta di WENDI II in corrispondenza di campi complessi di radiazione, simulazioni MCNPX sono state realizzate riproducendo un ambiente di lavoro esistente presso la sede IBA di Louvain la Neuve: la risposta del detector è stata valutata in corrispondenza di 9 diverse posizioni all’interno di un bunker contenente un ciclotrone PET (18 MeV H-), implicando la rilevazione di campi di radiazione neutronica continui ed estesi dalle energie termiche fino a 18 MeV. I risultati ottenuti sono stati infine comparati con i valori di dose ambiente equivalente calcolata nelle stesse condizioni di irraggiamento.
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Echanique, Christopher. "Characterization of an advanced neuron model." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/547.
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This thesis focuses on an adaptive quadratic spiking model of a motoneuron that is both versatile in its ability to represent a range of experimentally observed neuronal firing patterns as well as computationally efficient for large network simulation. The objective of research is to fit membrane voltage data to the model using a parameter estimation approach involving simulated annealing. By manipulating the system dynamics of the model, a realizable model with linear parameterization (LP) can be obtained to simplify the estimation process. With a persistently excited current input applied to the model, simulated annealing is used to efficiently determine the best model parameters that minimize the square error function between the membrane voltage reference data and data generated by the LP model. Results obtained through simulation of this approach show feasibility to predict a range of different neuron firing patterns.B.S.P.E.
Bachelors
Engineering and Computer Science
Computer Engineering
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Angel, Nathan A. "EQUIVALENT CIRCUIT IMPLEMENTATION OF DEMYELINATED HUMAN NEURON IN SPICE." DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/611.
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This work focuses on modeling a demyelinated Hodgkin and Huxley (HH) neuron with Simulated Program with Integrated Circuit Emphasis (SPICE) platform. Demyelinating disorders affect over 350,000 people in the U.S and understanding the demyelination process at the cellular level is necessary to find safe ways to treat the diseases [9]. Utilizing a previous SPICE model of an electrically small cell neuron developed by Szlavik [32], an extended core conductor myelinated neuron was produced in this work. The myelinated neuron developed has seven active Nodes of Ranvier (nodes) separated by a myelin sheath. The myelin sheath can be successfully modeled with a resistive and capacitive network known as internodes. Both the Nodes of Ranvier and internode equivalent circuits were implemented in P-SPICE sub-circuit library files. Properties of the neuron can be changed in the library files to simulate neurons of different electrical or geometric properties. Using the P-SPICE code developed in this work, a myelinated neuron’s action potential was simulated and the action potential at each node was recorded. The action potential at each node was uniform in amplitude and pulse width. The conduction velocity of the action potential was calculated to be 57.15 m/s.
Demyelination can be modeled by decreasing the capacitance and increasing the resistance of the myelin [34]. Two demyelinated neuron models were simulated in this work. The first model had one internode segment demyelinated, and the second model was of three consecutive internode segments. The resulting conduction velocity was calculated for both simulations. For one and three internode segment demyelinated the conduction velocity was slowed to 44.15 m/s, and 27.15 m/s respectively. This model successfully showed that an HH neuron implemented in SPICE could show the effects of demyelination on conduction velocity
The goal of this work is to develop a demyelinated neuron so that treatments for Multiple Sclerosis (MS) and other demyelinated neurons could be simulated to test various treatments’ effectiveness. A current treatment for MS is ion channel blockers. Future work would be to use this model to test current ion channel blocker therapy and to validate if such therapies alleviate conduction slowing.
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Van, Den Bosch Magali Marie. "Simulation of ion exchange processes using neuro-fuzzy reasoning." Thesis, Cape Peninsula University of Technology, 2009. http://hdl.handle.net/20.500.11838/2161.
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Thesis (MTech (Chemical Engineering))--Cape Peninsula University of Technology, 2009.Neuro-fuzzy computing techniques have been approached and evaluated in areas of process control; researchers have recently begun to evaluate its potential in pattern recognition. Multi-component ion exchange is a non-linear process, which is difficult to model and simulate as there are many factors influencing the chemical process which are not well understood. In the past, empirical isotherm equations were used but there were definite shortcomings resulting in unreliable simulations. In this work, the use of artificial intelligence has therefore been researched to test the effectiveness in simulating ion exchange processes. The branch of artificial intelligence used was the adaptive neuro fuzzy inference system. The objective of this research was to develop a neuro-fuzzy software package to simulate ion exchange processes. The first step towards building this system was to collect data from laboratory scale ion exchange experiments. Different combinations of inputs (e.g. solution concentration, resin loading, impeller speed), were tested to determine whether it was necessary to monitor all available parameters. The software was developed in MSEXCEL where tools like SOLVER could be utilised whilst the code was written in Visual Basic. In order to compare the neuro-fuzzy simulations to previously used empirical methods, the Fritz and Schluender isotherm was used to model and simulate the same data. The results have shown that both methods were adequate but the neuro-fuzzyapproach was the more appropriate method. After completion of this study, it could be concluded that a neuro-fuzzy system does not always have the ability to describe ion exchange processes adequately.
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Grünler, Daniel, and Saman Rassam. "The effects of connection density on neuronal synchrony in a simulated neuron network." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-280348.
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As one of our most complex and least understood organs, the brain constitutes a major area of research, and our understanding of the inner workings of the brain is still at an early stage. Research into neural activity can provide better understanding of the brain by looking at patterns of activity within the brain. One form of such pattern is neuronal synchrony which has been shown to have significance in cognitive performance. We first gathered spike time data by simulating a neuronal network with varying degrees of connection density. We then analyzed the spike data using the ISI-distance measure in order to quantify the level of neuronal synchrony in the network at the different degrees of connection density. As the final step, we calculated the Pearson product to get a measure of the correlation between connection density and neuronal synchrony. The results indicated that connection density is strongly negatively correlated to neuronal synchrony, however due to limiting factors the result can not be generalized beyond the specific circumstances of this experiment.Hjärnan är ett av våra mest komplexa organ. Vår förståelse för hjärnan är fortfarande i ett tidigt skede och hjärnrelaterad forskning har länge utgjort ett stort forskningsområde. Ett sätt att få en ökad förståelse för hur hjärnan fungerar är att undersöka neuronaktiviteten genom att kolla på aktiviteten och aktivitetsmönstret. Ett aktivitetsmönster är neuronsynkroni som har visats ha en betydande roll i vår kognitiva förmåga. Vi började med att generera spikdata genom att simulera ett neuronnätverk med varierande grad av densitet. Vi analyserade sedan spikdatan med ISI-avståndsmetoden för att kvantifiera nivån av neuronsynkroni i nätverket vid de olika graderna densitet. I ett sista steg beräknade vi Pearsons korrelationskoefficient för att få ett mått på korrelationen mellan densitet och neuronsynkroni. Resultatet visade på att densiteten i nätverket och graden av neuronsynkroni var starkt negativt korrelerade, men på grund av begränsande faktorer kan resultatet inte generaliseras utöver experimentets specifika omständigheter.
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Montgomery, Rick. "Fractal Electrodes for Interfacing Neurons to Retinal Implants." Thesis, University of Oregon, 2015. http://hdl.handle.net/1794/18714.
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With life expectancy on the rise, age-related ailments are a significant strain on the welfare of individuals and the economy. Progress is being made towards combating the leading cause of unavoidable blindness, age-related macular degeneration (AMD). AMD affects ten million Americans and costs the world economy $343 billion annually. Retinal implants promise to restore sight by replacing the eye's damaged photoreceptors with electronic photodiodes. Clinical trials succeed at restoring some vision, but are limited by the stimulating electrodes. We study the electrode-neuron interface with a focus on the geometrical dependence of the electrode.
The functionality of neurons is intimately connected to their branching and curving shape, described by fractal geometry. We examine the morphology of neurons using fractal analysis. The results inform our electrode designs, which are fabricated using top-down lithographic and bottom-up self-assembly techniques.
A novel technique for fabricating a fractal electrode is presented. Heating and cooling a film of poly(methyl methacrylate) on a SiO2 substrate causes fractal structures to form on the surface. The geometry of the structures is temperature dependent, producing crystalline branches at lower temperatures and diffusion-limited aggregates at higher temperatures. Subsequent deposition of antimony nanoclusters shows preferred diffusion to the fractal surface features.
The dependence of a photodiode's performance on its top contact geometry is explored using modified nodal analysis. The results reinforce the need to balance a low mean semiconductor-metal separation distance with an adequate contact width for low resistance, all while maximizing light input. Future designs will benefit from the spatial voltage maps produced by the simulation.
The electric field emanating from an electrode is also dependent on the geometry of the electrode. The Faraday cage effect is exploited to achieve similar electric field responses to traditional electrode shapes.
A preliminary study of neural adhesion to SU-8 fractal electrodes is promising. The neuron grows along the electrode even at 90° turns.
The role the fractal geometry plays in neuron and electrode functionality is shown to be significant. Continued study of, and experimentation with, new electrode designs is sure to produce exciting possibilities in the future.
This dissertation includes previously unpublished co-authored material.
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Collins, David R. "Computer simulation and neutron scattering studies of layer silicate materials." Thesis, Keele University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292741.
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Folkert, Michael R. (Michael Ryan) 1975. "Monte Carlo simulation of neutron shielding for proton therapy facilities." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/50492.
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Thesis (S.B. and S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1998.Includes bibliographical references (leaves 60-63).
A study was performed to develop a Monte Carlo method of modeling neutron shielding of proton therapy facilities in a complex, realistic environment. The bulk neutron shielding of the Northeast Proton Therapy Center (Massachusetts General Hospital, Boston, MA) was used as the basis of the design work. A geometrical model of the facility was simulated using the LAHET Code System, a set of Monte Carlo codes developed at Los Alamos National Laboratory. Additional software tools for reading and analyzing the simulation data that the model provides have been developed and tested. In order to verify the computer simulations, neutron detection and data acquisition systems have been assembled, modified, and thoroughly tested in order to monitor the neutron dose equivalent during proton beam operation at several locations on a continuous basis. Preliminary tests show that the geometry and physics models proposed in this work are valid.
by Michael R. Folkert.
S.B.and S.M.
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Moustafa, Salli. "Massively Parallel Cartesian Discrete Ordinates Method for Neutron Transport Simulation." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0408/document.
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La simulation haute-fidélité des coeurs de réacteurs nucléaires nécessite une évaluation précise du flux neutronique dans le coeur du réacteur. Ce flux est modélisé par l’équation de Boltzmann ou équation du transport neutronique. Dans cette thèse, on s’intéresse à la résolution de cette équation par la méthode des ordonnées discrètes (SN) sur des géométries cartésiennes. Cette méthode fait intervenir un schéma d’itérations à source, incluant un algorithme de balayage sur le domaine spatial qui regroupe l’essentiel des calculs effectués. Compte tenu du très grand volume de calcul requis par la résolution de l’équation de Boltzmann, de nombreux travaux antérieurs ont été consacrés à l’utilisation du calcul parallèle pour la résolution de cette équation. Jusqu’ici, ces algorithmes de résolution parallèles de l’équation du transport neutronique ont été conçus en considérant la machine cible comme une collection de processeurs mono-coeurs indépendants, et ne tirent donc pas explicitement profit de la hiérarchie mémoire et du parallélisme multi-niveaux présents sur les super-calculateurs modernes. Ainsi, la première contribution de cette thèse concerne l’étude et la mise en oeuvre de l’algorithme de balayage sur les super-calculateurs massivement parallèles modernes. Notre approche combine à la fois la vectorisation par des techniques de la programmation générique en C++, et la programmation hybride par l’utilisation d’un support d’exécution à base de tâches: PaRSEC. Nous avons démontré l’intérêt de cette approche grâce à des modèles de performances théoriques, permettant également de prédire le partitionnement optimal. Par ailleurs, dans le cas de la simulation des milieux très diffusifs tels que le coeur d’un REP, la convergence du schéma d’itérations à source est très lente. Afin d’accélérer sa convergence, nous avons implémenté un nouvel algorithme (PDSA), adapté à notre implémentation hybride. La combinaison de ces techniques nous a permis de concevoir une version massivement parallèle du solveur SN Domino. Les performances de la partie Sweep du solveur atteignent 33.9% de la performance crête théorique d’un super-calculateur à 768 cores. De plus, un calcul critique d’un réacteur de type REP 900MW à 26 groupes d’énergie mettant en jeu 1012 DDLs a été résolu en 46 minutes sur 1536 coeursHigh-fidelity nuclear reactor core simulations require a precise knowledge of the neutron flux inside the reactor core. This flux is modeled by the linear Boltzmann equation also called neutron transport equation. In this thesis, we focus on solving this equation using the discrete ordinates method (SN) on Cartesian mesh. This method involves a source iteration scheme including a sweep over the spatial mesh and gathering the vast majority of computations in the SN method. Due to the large amount of computations performed in the resolution of the Boltzmann equation, numerous research works were focused on the optimization of the time to solution by developing parallel algorithms for solving the transport equation. However, these algorithms were designed by considering a super-computer as a collection of independent cores, and therefore do not explicitly take into account the memory hierarchy and multi-level parallelism available inside modern super-computers. Therefore, we first proposed a strategy for designing an efficient parallel implementation of the sweep operation on modern architectures by combining the use of the SIMD paradigm thanks to C++ generic programming techniques and an emerging task-based runtime system: PaRSEC. We demonstrated the need for such an approach using theoretical performance models predicting optimal partitionings. Then we studied the challenge of converging the source iterations scheme in highly diffusive media such as the PWR cores. We have implemented and studied the convergence of a new acceleration scheme (PDSA) that naturally suits our Hybrid parallel implementation. The combination of all these techniques have enabled us to develop a massively parallel version of the SN Domino solver. It is capable of tackling the challenges posed by the neutron transport simulations and compares favorably with state-of-the-art solvers such as Denovo. The performance of the PaRSEC implementation of the sweep operation reaches 6.1 Tflop/s on 768 cores corresponding to 33.9% of the theoretical peak performance of this set of computational resources. For a typical 26-group PWR calculations involving 1.02×1012 DoFs, the time to solution required by the Domino solver is 46 min using 1536 cores
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Peng, Qunming. "Brainstem a neocortical simulator interface for robotic studies /." abstract and full text PDF (free order & download UNR users only), 2006. http://0-gateway.proquest.com.innopac.library.unr.edu/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:1438917.
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Sugden, Frank Daniel. "A NOVEL DUAL MODELING METHOD FOR CHARACTERIZING HUMAN NERVE FIBER ACTIVATION." DigitalCommons@CalPoly, 2014. https://digitalcommons.calpoly.edu/theses/1318.
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Presented in this work is the investigation and successful illustration of a coupled model of the human nerve fiber. SPICE netlist code was utilized to describe the electrical properties of the human nervous membrane in tandem with COMSOL Multiphysics, a finite element analysis software tool. The initial research concentrated on the utilization of the Hodgkin-Huxley electrical circuit representation of the nerve fiber membrane. Further development of the project identified the need for a linear circuit model that more closely resembled the McNeal linearization model augmented by the work of Szlavik which better facilitated the coupling of both SPICE and COMSOL programs. Related literature was investigated and applied to validate the model. This combination of analysis tools allowed for the presentation of a consistent model and revealed that a coupled model produced not only a qualitatively comparable, but also a quantitatively comparable result to studies presented in the literature. All potential profiles produced during the simulation were compared against the literature in order to meet the purpose of presenting an advanced computational model of human neural recruitment and excitation. It was demonstrated through this process that the correct usage of neuron models within a two dimensional conductive space did allow for the approximate modeling of human neural electrical characteristics.
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Wu, Jian. "A router for massively-parallel neural simulation." Thesis, University of Manchester, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.515088.
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i. The author of this thesis (including any appendices and/or schedules to this thesis) owns any copyright in it (the "Copyright") and s/he has given The University of Manchester the right to use such Copyright for any administrative, promotional, educational and/or teaching purposes. ii. Copies of this thesis, either in full or in extracts, may be made only in accordance with the regulations of the John Rylands University Library of Manchester. Details of these regulations may be obtained from the Librarian. This page must form part of any such copies made. iii. The ownership of any patents, designs, trade marks and any and all other intellectual property rights except for the Copyright (the "Intellectual Property Rights") and any reproductions of copyright works, for example graphs and tables ("Reproductions"), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property Rights and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property Rights and/or Reproductions. iv. Further information on the conditions under which disclosure, publication and exploitation of this thesis, the Copyright and any Intellectual Property Rights and/or Reproductions described in it may take place is available from the Head of School of School of Computer Science (or the Vice-President).
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Vindlacheruvu, Prasad. "Simulation of a neural node co-processor." Thesis, Nottingham Trent University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.309566.
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Garrihy, G. "Neural network simulation of dynamic speech perception." Thesis, University of Essex, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317930.
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Clavijo, Maria F. "Using neural networks for goal driven simulation." FIU Digital Commons, 2004. http://digitalcommons.fiu.edu/etd/2383.
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An integration framework for Neural Networks (NN) and Goal Driven Simulation (GDS) has been designed. It offers no constraints regarding number of variables (n>3) and it does not have domain restrictions. The effectiveness of the framework was tested by observing the computational time required for obtaining responses and for training, and by assessing its accuracy for different scenarios. This framework has achieved the automation objective set by GDS under a shorter time frame, as it reduces the time from more than 42 hours to less than 14. A trained NN generates responses to queries almost instantaneously. However, it requires time re-building and re-training new NNs when changes are made to the system represented by the model. If these changes are rare, the payoff is worthy as this approach gives users more flexibility.
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Corasaniti, Maria. "Monte Carlo simulation of a neutron veto for the XENONnT experiment." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13974/.
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XENON1T, located at the Laboratori Nazionali del Gran Sasso, is currently the largest experiment for direct dark matter search. It consists of a dual phase TPC filled with 2 tonnes of xenon, and has completed the first science run in January 2017, obtaining the most stringent exclusion limits on the spin-independent WIMP- nucleon interaction cross section for WIMP masses above 10 GeV/c2, with a minimum of 7.7·10−47 cm2 for 35-GeV/c2 WIMPs at 90% confidence level. Currently the experiment is still in data acquisition and aims at a sensitivity of 1.6 · 10−47 cm2 for WIMP masses of 50 GeV/c2 in 2 t·y exposure.
A next generation detector, called XENONnT, is already foreseen by the collaboration. It will have a larger TPC with an increased xenon target (∼ 6 t) which will improve the WIMP sensitivity by another order of magnitude. For this purpose, it also requires a very low background level. The expected neutron background for the new designed time projection chamber is ∼5 events in the 4 t fiducial volume, in the nominal 20 ton·year exposure. In this work we present a Monte Carlo simulation study of a Gd-loaded liquid scintillator neutron veto for the XENONnT experiment, with the goal of tagging the background events from radiogenic neutrons. Results indicate that, for a scintillating mixture with 0.1% of gadolinium by weight, and a light collection efficiency of ∼7%, we obtain a neutron rejection factor higher than 80%. This allows to reduce the neutron background by a factor ∼5, in order to be in full agreement with the background goal of the XENONnT experiment: <1 background event in the total exposure.
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Schramm, Georg Alexander. "Analysis and simulation of photon scattering and neutron capture gamma spectra." Helmholtz-Zentrum Dresden-Rossendorf, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:d120-qucosa-146889.
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Within this thesis two twin experiments consisting of neutron capture and photon scattering on the neighbour isotopes 77Se / 78Se and 195Pt / 196Pt have been analysed to gain qualitative and quantitative information about the photon strength function and level density in the respective compound nuclei. For the analysis and simulation of both experimental types a new Monte Carlo simulation using a fast and efficient, extreme statistical treatment of radiative nuclear deexcitations, was developed. Furthermore the influence of fluctuations of transition widths on photon scattering were investigated and quantified. It could be shown that those lead to an enhancement of elastic scattering processes. The data analysis of both twin experiments reveals non-Lorentzian extra E1 photon strength below the neutron separation energy.
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Baldassarre, Gianluca. "Planning with neural networks and reinforcement learning." Thesis, University of Essex, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252285.
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Bazargan-Harandi, Hamid. "Neural network based simulation of sea-state sequences." Thesis, Brunel University, 2006. http://bura.brunel.ac.uk/handle/2438/379.
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The present PhD study, in its first part, uses artificial neural networks (ANNs), an optimization technique called simulated annealing, and statistics to simulate the significant wave height (Hs) and mean zero-up-crossing period ( ) of 3-hourly sea-states of a location in the North East Pacific using a proposed distribution called hepta-parameter spline distribution for the conditional distribution of Hs or given some inputs. Two different seven- network sets of ANNs for the simulation and prediction of Hs and were trained using 20-year observed Hs’s and ’s. The preceding Hs’s and ’s were the most important inputs given to the networks, but the starting day of the simulated period was also necessary. However, the code replaced the day with the corresponding time and the season. The networks were trained by a simulated annealing algorithm and the outputs of the two sets of networks were used for calculating the parameters of the probability density function (pdf) of the proposed hepta-parameter distribution. After the calculation of the seven parameters of the pdf from the network outputs, the Hs and of the future sea-state is predicted by generating random numbers from the corresponding pdf. In another part of the thesis, vertical piles have been studied with the goal of identifying the range of sea-states suitable for the safe pile driving operation. Pile configuration including the non-linear foundation and the gap between the pile and the pile sleeve shims were modeled using the finite elements analysis facilities within ABAQUS. Dynamic analyses of the system for a sea-state characterized by Hs and and modeled as a combination of several wave components were performed. A table of safe and unsafe sea-states was generated by repeating the analysis for various sea-states. If the prediction for a particular sea-state is repeated N times of which n times prove to be safe, then it could be said that the predicted sea-state is safe with the probability of 100(n/N)%. The last part of the thesis deals with the Hs return values. The return value is a widely used measure of wave extremes having an important role in determining the design wave used in the design of maritime structures. In this part, Hs return value was calculated demonstrating another application of the above simulation of future 3-hourly Hs’s. The maxima method for calculating return values was applied in such a way that avoids the conventional need for unrealistic assumptions. The significant wave height return value has also been calculated using the convolution concept from a model presented by Anderson et al. (2001).
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May, Norman L. "Fault simulation of a wafer-scale neural network." Full text open access at:, 1988. http://content.ohsu.edu/u?/etd,159.
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Jordan, Jakob Verfasser], Markus [Akademischer Betreuer] Diesmann, and Bastian [Akademischer Betreuer] [Leibe. "Probabilistic neural computation and neural simulation technology / Jakob Jordan ; Markus Diesmann, Bastian Leibe." Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1210862727/34.
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Jordan, Jakob [Verfasser], Markus Akademischer Betreuer] Diesmann, and Bastian [Akademischer Betreuer] [Leibe. "Probabilistic neural computation and neural simulation technology / Jakob Jordan ; Markus Diesmann, Bastian Leibe." Aachen : Universitätsbibliothek der RWTH Aachen, 2018. http://d-nb.info/1210862727/34.
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Fischer, Shain Ann. "A Three-Dimensional Anatomically Accurate Finite Element Model for Nerve Fiber Activation Simulation Coupling." DigitalCommons@CalPoly, 2015. https://digitalcommons.calpoly.edu/theses/1365.
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Improved knowledge of human nerve function and recruitment would enable innovation in the Biomedical Engineering field. Better understanding holds the potential for greater integration between devices and the nervous system as well as the ability to develop therapeutic devices to treat conditions affecting the nervous system. This work presents a three-dimensional volume conductor model of the human arm for coupling with code describing nerve membrane characteristics. The model utilizes an inhomogeneous medium composed of bone, muscle, skin, nerve, artery, and vein. Dielectric properties of each tissue were collected from the literature and applied to corresponding material subdomains. Both a fully anatomical version and a simplified version are presented. The computational model for this study was developed in COMSOL and formatted to be coupled with SPICE netlist code. Limitations to this model due to computational power as well as future work are discussed. The final model incorporated both anatomically correct geometries and simplified geometries to enhance computational power. A stationary study was performed implementing a boundary current source through the surface of a conventionally placed electrode. Results from the volume conductor study are presented and validated through previous studies.
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Hô, Nicolas. "Simulation numérique du neurone pyramidal du néocortex en présence d'activité synaptique intense." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape2/PQDD_0021/MQ49099.pdf.
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Refson, Keith. "The phases of butane : an investigation by computer simulation and neutron diffraction." Thesis, University of Edinburgh, 1986. http://hdl.handle.net/1842/14263.
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Pretorius, Christiaan Johannes. "Artificial neural networks as simulators for behavioural evolution in evolutionary robotics." Thesis, Nelson Mandela Metropolitan University, 2010. http://hdl.handle.net/10948/1476.
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Robotic simulators for use in Evolutionary Robotics (ER) have certain challenges associated with the complexity of their construction and the accuracy of predictions made by these simulators. Such robotic simulators are often based on physics models, which have been shown to produce accurate results. However, the construction of physics-based simulators can be complex and time-consuming. Alternative simulation schemes construct robotic simulators from empirically-collected data. Such empirical simulators, however, also have associated challenges, such as that some of these simulators do not generalize well on the data from which they are constructed, as these models employ simple interpolation on said data. As a result of the identified challenges in existing robotic simulators for use in ER, this project investigates the potential use of Artificial Neural Networks, henceforth simply referred to as Neural Networks (NNs), as alternative robotic simulators. In contrast to physics models, NN-based simulators can be constructed without needing an explicit mathematical model of the system being modeled, which can simplify simulator development. Furthermore, the generalization capabilities of NNs suggest that NNs could generalize well on data from which these simulators are constructed. These generalization abilities of NNs, along with NNs’ noise tolerance, suggest that NNs could be well-suited to application in robotics simulation. Investigating whether NNs can be effectively used as robotic simulators in ER is thus the endeavour of this work. Since not much research has been done in employing NNs as robotic simulators, many aspects of the experimental framework on which this dissertation reports needed to be carefully decided upon. Two robot morphologies were selected on which the NN simulators created in this work were based, namely a differentially steered robot and an inverted pendulum robot. Motion tracking and robotic sensor logging were used to acquire data from which the NN simulators were constructed. Furthermore, custom code was written for almost all aspects of the study, namely data acquisition for NN training, the actual NN training process, the evolution of robotic controllers using the created NN simulators, as well as the onboard robotic implementations of evolved controllers. Experimental tests performed in order to determine ideal topologies for each of the NN simulators developed in this study indicated that different NN topologies can lead to large differences in training accuracy. After performing these tests, the training accuracy of the created simulators was analyzed. This analysis showed that the NN simulators generally trained well and could generalize well on data not presented during simulator construction. In order to validate the feasibility of the created NN simulators in the ER process, these simulators were subsequently used to evolve controllers in simulation, similar to controllers developed in related studies. Encouraging results were obtained, with the newly-evolved controllers allowing real-world experimental robots to exhibit obstacle avoidance and light-approaching behaviour with a reasonable degree of success. The created NN simulators furthermore allowed for the successful evolution of a complex inverted pendulum stabilization controller in simulation. It was thus clearly established that NN-based robotic simulators can be successfully employed as alternative simulation schemes in the ER process.
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Jiang, Liqiu. "THE SIMULATION AND APPROXIMATION OF THE FIRST PASSAGE TIME OF THE ORNSTEIN--UHLENBECK PROCESS OF NEURON." NCSU, 2002. http://www.lib.ncsu.edu/theses/available/etd-04232002-224527/.
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Neurons communicate with each other via sequences of action potentials. The purpose of this study is to approximate the interval between action potentials which is also called the First Passage Time (FPT), the first time the membrane voltage passes a threshold. The subthreshold depolarization of a neuron receiving a multitude of random synaptic inputs has often been modelled as the Ornstein--Uhlenbeck (OU) process. This model provides an analytically tractable formalism of neuronal membrane voltage mean and variance in terms of a neuron's membrane time constant and the mean of input voltage. Some authors obtained an approximate mean and variance of the FPT for Stein's model with a constant threshold for firing by using Stein's method. They approximated the mean and variance of FPT by using the first term of the Taylor's series expansion. We expect this procedure works for the OU process, a diffusion process. This study finds that Stein's method works well for the OU process with the small Wiener process parameter. After adding a few other terms of the Taylor's series, the parameter range in which the approximation works well are almost the same as the range in which the first term does. The relationship between the approximation results and the confidence band of the mean and variance of the simulated FPT gives evidence that their parameter range is the same; but, the approximation by two terms of the Taylor's series gives less approximation error. The goodness--of--fit--test shows that the lognormal distribution is close to the distribution of FPT for all the Wiener parameters we used. We compared a lognormal distribution of the FPT, estimated from simulation of the OU process, with the probability density function (pdf) of the FPT, approximated from a transformation of the marginal distribution of membrane voltage at the time at which the mean of membrane voltage passes the threshold. We found that the approximation pdf and the lognormal pdf are almost equally close to the true and unknown pdf when the parameter of the Wiener process is small.
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Ajay, Anurag. "Augmenting physics simulators with neural networks for model learning and control." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/122747.
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Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019Cataloged from PDF version of thesis.
Includes bibliographical references (pages 77-81).
Physics simulators play an important role in robot state estimation, planning and control; however, many real-world control problems involve complex contact dynamics that cannot be characterized analytically. Therefore, most physics simulators employ approximations that lead to a loss in precision. We propose a hybrid dynamics model, combining a deterministic physical simulator with a stochastic neural network for dynamics modeling as it provides us with expressiveness, efficiency, and generalizability simultaneously. To demonstrate this, we compare our hybrid model to both purely analytical models and purely learned models. We then show that our model is able to characterize the complex distribution of object trajectories and compare it with existing methods. We further build in object based representation into the neural network so that our hybrid model can generalize across number of objects. Finally, we use our hybrid model to complete complex control tasks in simulation and on a real robot and show that our model generalizes to novel environments with varying object shapes and materials.
by Anurag Ajay.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Krupina, Olga. "NeuroSim: neural simulation system with a client server architecture." [S.l. : s.n.], 2005. http://www.diss.fu-berlin.de/2005/173/index.html.
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Franke, Cameron. "Autonomous Driving with a Simulation Trained Convolutional Neural Network." Scholarly Commons, 2017. https://scholarlycommons.pacific.edu/uop_etds/2971.
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Autonomous vehicles will help society if they can easily support a broad range of driving environments, conditions, and vehicles.
Achieving this requires reducing the complexity of the algorithmic system, easing the collection of training data, and verifying operation using real-world experiments. Our work addresses these issues by utilizing a reflexive neural network that translates images into steering and throttle commands. This network is trained using simulation data from Grand Theft Auto V~\cite{gtav}, which we augment to reduce the number of simulation hours driven. We then validate our work using a RC car system through numerous tests. Our system successfully drive 98 of 100 laps of a track with multiple road types and difficult turns; it also successfully avoids collisions with another vehicle in 90\% of the trials.
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Yancey, Madison E. "Computational Simulation and Analysis of Neuroplasticity." Wright State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=wright1622582138544632.
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Cheetham, Andrew. "Simulation of a multi-dimensional pattern classifier." Thesis, Nottingham Trent University, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297128.
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Blomqvist, David. "Monte Carlo Simulation of Proton and Neutron Transport Based on the PENELOPE Code." Thesis, KTH, Fysik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-181080.
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George, Stuart. "Cell scale modelling and numerical simulation of transmembrane potential in neurons and glia." Thesis, University of Southampton, 2015. https://eprints.soton.ac.uk/384191/.
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