Relevant bibliographies by topics / Brain – Computer simulation

Academic literature on the topic 'Brain – Computer simulation'

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Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Brain – Computer simulation"

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Sharkey, Noel. "Computer simulation in brain science." Biological Psychology 29, no. 2 (October 1989): 199–200. http://dx.doi.org/10.1016/0301-0511(89)90039-2.

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Dexter, Franklin, and Bradley J. Hindman. "Computer simulation of brain cooling during cardiopulmonary bypass." Annals of Thoracic Surgery 57, no. 5 (May 1994): 1171–78. http://dx.doi.org/10.1016/0003-4975(94)91350-1.

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Eikmeyer, Hans-Jürgen, and Ulrich Schade. "The Role of Computer Simulation in Neurolinguistics." Nordic Journal of Linguistics 16, no. 2 (December 1993): 153–69. http://dx.doi.org/10.1017/s0332586500002791.

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As a result of present-day technological standards, the technique of computer simulation is constantly gaining influence in cognitive science. Neurolinguistics is a special branch of this field in which cognitive capacities connected with language are related to the structure and functions of the brain. It is argued that computer simulation is a useful technique for evaluating neurolinguistic models. This is demonstrated with respect to neural network models of the process of language production.
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KUROSAWA, Yusuke, Tomoki TAKAHASHI, Kazuo KATO, and Mitsunori KUBO. "A119 Basic analysis of brain injury mechanism by computer simulation." Proceedings of the JSME Conference on Frontiers in Bioengineering 2008.19 (2008): 37–38. http://dx.doi.org/10.1299/jsmebiofro.2008.19.37.

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Jin, Jing, Sahar Shahbazi, John Lloyd, Sidney Fels, Sandrine de Ribaupierre, and Roy Eagleson. "Hybrid simulation of brain–skull growth." SIMULATION 90, no. 1 (December 18, 2013): 3–10. http://dx.doi.org/10.1177/0037549713516691.

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Curta, Catalin, Septimiu Crisan, and Radu V. Ciupa. "Prefrontal Cortex Magnetic Stimulation, a Simulation Analysis." Advanced Engineering Forum 8-9 (June 2013): 631–38. http://dx.doi.org/10.4028/www.scientific.net/aef.8-9.631.

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The presented work aims to elucidate where stimulation occurs in the brain during transcranial magnetic stimulation (TMS), taking into account cortical geometry. A realistic computer model of TMS was developed comprising a stimulation coil and the human cortex. The coil was positioned over the right dorsolateral prefrontal cortex (right DLPFC) and the distribution of the induced electric field was analyzed. A computer simulation was constructed, where the coil is positioned at an angle of 450 relative to the sagittal plane. The results highlight the influence of cortical geometry on the distribution of the electric field in the brain and show that the highest values are not obtained directly under the center of the stimulator.
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AZUMA, Youhei, Kazuhiko ADACHI, Yu HASEGAWA, Atsushi FUJITA, Eiji KOHMURA, and Hiroshi KANKI. "315 Finite Element Human Brain Modeling for Computer-Assisted Neurosurgical Simulation." Proceedings of the Dynamics & Design Conference 2008 (2008): _315–1_—_315–6_. http://dx.doi.org/10.1299/jsmedmc.2008._315-1_.

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FUJITA, Noriyuki, Shigeru AOMURA, and Satoshi FUJIWARA. "20615 Computer simulation of brain injury based on the autopsy data." Proceedings of Conference of Kanto Branch 2005.11 (2005): 173–74. http://dx.doi.org/10.1299/jsmekanto.2005.11.173.

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McClay, W., and A. Haas. "A Real-time Brain Computer Interface for 3-D Flight Simulation." Journal of Vision 7, no. 15 (March 28, 2010): 41. http://dx.doi.org/10.1167/7.15.41.

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Miller, Karol, Kiyoyuki Chinzei, Girma Orssengo, and Piotr Bednarz. "Mechanical properties of brain tissue in-vivo: experiment and computer simulation." Journal of Biomechanics 33, no. 11 (November 2000): 1369–76. http://dx.doi.org/10.1016/s0021-9290(00)00120-2.

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Dissertations / Theses on the topic "Brain – Computer simulation"

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Stetner, Michael E. "Improving decoding in intracortical brain-machine interfaces." Cleveland, Ohio : Case Western Reserve University, 2009. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1254235417.

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Mundy, Andrew. "Real time Spaun on SpiNNaker : functional brain simulation on a massively-parallel computer architecture." Thesis, University of Manchester, 2017. https://www.research.manchester.ac.uk/portal/en/theses/real-time-spaun-on-spinnaker--functional-brain-simulation-on-a-massivelyparallel-computer-architecture(fcf5388c-4893-4b10-a6b4-577ffee2d562).html.

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Model building is a fundamental scientific tool. Increasingly there is interest in building neurally-implemented models of cognitive processes with the intention of modelling brains. However, simulation of such models can be prohibitively expensive in both the time and energy required. For example, Spaun - "the world's first functional brain model", comprising 2.5 million neurons - required 2.5 hours of computation for every second of simulation on a large compute cluster. SpiNNaker is a massively parallel, low power architecture specifically designed for the simulation of large neural models in biological real time. Ideally, SpiNNaker could be used to facilitate rapid simulation of models such as Spaun. However the Neural Engineering Framework (NEF), with which Spaun is built, maps poorly to the architecture - to the extent that models such as Spaun would consume vast portions of SpiNNaker machines and still not run as fast as biology. This thesis investigates whether real time simulation of Spaun on SpiNNaker is at all possible. Three techniques which facilitate such a simulation are presented. The first reduces the memory, compute and network loads consumed by the NEF. Consequently, it is demonstrated that only a twentieth of the cores are required to simulate a core component of the Spaun network than would otherwise have been needed. The second technique uses a small number of additional cores to significantly reduce the network traffic required to simulated this core component. As a result simulation in real time is shown to be feasible. The final technique is a novel logic minimisation algorithm which reduces the size of the routing tables which are used to direct information around the SpiNNaker machine. This last technique is necessary to allow the routing of models of the scale and complexity of Spaun. Together these provide the ability to simulate the Spaun model in biological real time - representing a speed-up of 9000 times over previously reported results - with room for much larger models on full-scale SpiNNaker machines.
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Quek, Melissa. "The role of simulation in developing and designing applications for 2-class motor imagery brain-computer interfaces." Thesis, University of Glasgow, 2013. http://theses.gla.ac.uk/4503/.

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A Brain-Computer Interface (BCI) can be used by people with severe physical disabilities such as Locked-in Syndrome (LiS) as a channel of input to a computer. The time-consuming nature of setting up and using a BCI, together with individual variation in performance and limited access to end users makes it difficult to employ techniques such as rapid prototyping and user centred design (UCD) in the design and development of applications. This thesis proposes a design process which incorporates the use of simulation tools and techniques to improve the speed and quality of designing BCI applications for the target user group. Two different forms of simulation can be distinguished: offline simulation aims to make predictions about a user’s performance in a given application interface given measures of their baseline control characteristics, while online simulation abstracts properties of inter- action with a BCI system which can be shown to, or used by, a stakeholder in real time. Simulators that abstract properties of BCI control at different levels are useful for different purposes. Demonstrating the use of offline simulation, Chapter 3 investigates the use of finite state machines (FSMs) to predict the time to complete tasks given a particular menu hierarchy, and compares offline predictions of task performance with real data in a spelling task. Chapter 5 aims to explore the possibility of abstracting a user’s control characteristics from a typical calibration task to predict performance in a novel control paradigm. Online simulation encompasses a range of techniques from low-fidelity prototypes built using paper and cardboard, to computer simulation models that aim to emulate the feel of control of using a BCI without actually needing to put on the BCI cap. Chapter 4 details the develop- ment and evaluation of a high fidelity BCI simulator that models the control characteristics of a BCI based on the motor-imagery (MI) paradigm. The simulation tools and techniques can be used at different stages of the application design process to reduce the level of involvement of end users while at the same time striving to employ UCD principles. It is argued that prioritising the level of involvement of end users at different stages in the design process is an important strategy for design: end user input is paramount particularly at the initial user requirements stage where the goals that are important for the end user of the application can be ascertained. The interface and specific interaction techniques can then be iteratively developed through both real and simulated BCI with people who have no or less severe physical disabilities than the target end user group, and evaluations can be carried out with end users at the final stages of the process. Chapter 6 provides a case study of using the simulation tools and techniques in the development of a music player application. Although the tools discussed in the thesis specifically concern a 2-class Motor Imagery BCI which uses the electroencephalogram (EEG) to extract brain signals, the simulation principles can be expected to apply to a range of BCI systems.
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Grieve, Stuart Michael. "Development of fast magnetic resonance imaging methods for investigation of the brain." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365824.

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Stetner, Michael E. "Improving decoding in intracortical brain-machine interfaces." Case Western Reserve University School of Graduate Studies / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1254235417.

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Hutt, Axel. "The study of neural oscillations by traversing scales in the brain." Habilitation à diriger des recherches, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00603975.

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Hashemi, Fatemeh Sadat. "Sampling Controlled Stochastic Recursions: Applications to Simulation Optimization and Stochastic Root Finding." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76740.

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We consider unconstrained Simulation Optimization (SO) problems, that is, optimization problems where the underlying objective function is unknown but can be estimated at any chosen point by repeatedly executing a Monte Carlo (stochastic) simulation. SO, introduced more than six decades ago through the seminal work of Robbins and Monro (and later by Kiefer and Wolfowitz), has recently generated much attention. Such interest is primarily because of SOs flexibility, allowing the implicit specification of functions within the optimization problem, thereby providing the ability to embed virtually any level of complexity. The result of such versatility has been evident in SOs ready adoption in fields as varied as finance, logistics, healthcare, and telecommunication systems. While SO has become popular over the years, Robbins and Monros original stochastic approximation algorithm and its numerous modern incarnations have seen only mixed success in solving SO problems. The primary reason for this is stochastic approximations explicit reliance on a sequence of algorithmic parameters to guarantee convergence. The theory for choosing such parameters is now well-established, but most such theory focuses on asymptotic performance. Automatically choosing parameters to ensure good finite-time performance has remained vexingly elusive, as evidenced by continuing efforts six decades after the introduction of stochastic approximation! The other popular paradigm to solve SO is what has been called sample-average approximation. Sample-average approximation, more a philosophy than an algorithm to solve SO, attempts to leverage advances in modern nonlinear programming by first constructing a deterministic approximation of the SO problem using a fixed sample size, and then applying an appropriate nonlinear programming method. Sample-average approximation is reasonable as a solution paradigm but again suffers from finite-time inefficiency because of the simplistic manner in which sample sizes are prescribed. It turns out that in many SO contexts, the effort expended to execute the Monte Carlo oracle is the single most computationally expensive operation. Sample-average approximation essentially ignores this issue since, irrespective of where in the search space an incumbent solution resides, prescriptions for sample sizes within sample-average approximation remain the same. Like stochastic approximation, notwithstanding beautiful asymptotic theory, sample-average approximation suffers from the lack of automatic implementations that guarantee good finite-time performance. In this dissertation, we ask: can advances in algorithmic nonlinear programming theory be combined with intelligent sampling to create solution paradigms for SO that perform well in finite-time while exhibiting asymptotically optimal convergence rates? We propose and study a general solution paradigm called Sampling Controlled Stochastic Recursion (SCSR). Two simple ideas are central to SCSR: (i) use any recursion, particularly one that you would use (e.g., Newton and quasi- Newton, fixed-point, trust-region, and derivative-free recursions) if the functions involved in the problem were known through a deterministic oracle; and (ii) estimate objects appearing within the recursions (e.g., function derivatives) using Monte Carlo sampling to the extent required. The idea in (i) exploits advances in algorithmic nonlinear programming. The idea in (ii), with the objective of ensuring good finite-time performance and optimal asymptotic rates, minimizes Monte Carlo sampling by attempting to balance the estimated proximity of an incumbent solution with the sampling error stemming from Monte Carlo. This dissertation studies the theoretical and practical underpinnings of SCSR, leading to implementable algorithms to solve SO. We first analyze SCSR in a general context, identifying various sufficient conditions that ensure convergence of SCSRs iterates to a solution. We then analyze the nature of such convergence. For instance, we demonstrate that in SCSRs which guarantee optimal convergence rates, the speed of the underlying (deterministic) recursion and the extent of Monte Carlo sampling are intimately linked, with faster recursions permitting a wider range of Monte Carlo effort. With the objective of translating such asymptotic results into usable algorithms, we formulate a family of SCSRs called Adaptive SCSR (A-SCSR) that adaptively determines how much to sample as a recursion evolves through the search space. A-SCSRs are dynamic algorithms that identify sample sizes to balance estimated squared bias and variance of an incumbent solution. This makes the sample size (at every iteration of A-SCSR) a stopping time, thereby substantially complicating the analysis of the behavior of A-SCSRs iterates. That A-SCSR works well in practice is not surprising" the use of an appropriate recursion and the careful sample size choice ensures this. Remarkably, however, we show that A-SCSRs are convergent to a solution and exhibit asymptotically optimal convergence rates under conditions that are no less general than what has been established for stochastic approximation algorithms. We end with the application of a certain A-SCSR to a parameter estimation problem arising in the context of brain-computer interfaces (BCI). Specifically, we formulate and reduce the problem of probabilistically deciphering the electroencephalograph (EEG) signals recorded from the brain of a paralyzed patient attempting to perform one of a specified set of tasks. Monte Carlo simulation in this context takes a more general view, as the act of drawing an observation from a large dataset accumulated from the recorded EEG signals. We apply A-SCSR to nine such datasets, showing that in most cases A-SCSR achieves correct prediction rates that are between 5 and 15 percent better than competing algorithms. More importantly, due to the incorporated adaptive sampling strategies, A-SCSR tends to exhibit dramatically better efficiency rates for comparable prediction accuracies.
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Hetherington, Phil A. (Phillip Alan). "Hippocampal function and spatial information processing : computational and neural analyses." Thesis, McGill University, 1995. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=28778.

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The hippocampus is necessary for normal memory in rodents, birds, monkeys, and people. Damage to the hippocampus can result in the inability to learn new facts, defined by the relationship among stimuli. In rodents, spatial learning involves learning about the relationships among stimuli, and exemplifies the kind of learning the requires the hippocampus. Therefore, understanding the neural mechanisms underlying spatial learning may elucidate basic memory processes. Many hippocampal neurons fire when behaving rats, cats, or monkeys are in circumscribed regions (place fields) of an environment. The neurons, called place cells, fire in relation to distal stimuli, but can persist in signaling location when the stimuli are removed or lights are turned off (memory fields). In this thesis, computational models of spatial information processing simulated many of the defining properties of hippocampal place cells, including memory fields. Furthermore, the models suggested a neurally plausible mechanism of goal directed spatial navigation which involved the encoding of distances in the connections between place cells. To navigate using memory fields, the models required an excitatory, distributed, and plastic association system among place cells. Such properties are well characterized in area CA3 of the hippocampus. In this thesis, a new electrophysiological study provides evidence that a second system in the dentate gyrus has similar properties. Thus, two circuits in the hippocampus meet the requirements of the models. Some predictions of the models were then tested in a single-unit recording experiment in behaving rats. Place fields were more likely to occur in information rich areas of the environment, and removal of single cues altered place fields in a way consistent with the distance encoding mechanism suggested by the models. It was concluded that a distance encoding theory of rat spatial navigation has much descriptive and predictive utility, but most of its predic
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Skare, Stefan. "Optimisation strategies in diffusion tensor MR imaging /." Stockholm, 2002. http://diss.kib.ki.se/2002/91-7349-175-6.

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Nease, Stephen Howard. "Contributions to neuromorphic and reconfigurable circuits and systems." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/44923.

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This thesis presents a body of work in the field of reconfigurable and neuromorphic circuits and systems. Three main projects were undertaken. The first was using a Field-Programmable Analog Array (FPAA) to model the cable behavior of dendrites using analog circuits. The second was to design, lay out, and test part of a new FPAA, the RASP 2.9v. The final project was to use floating-gate programming to remove offsets in a neuromorphic FPAA, the RASP Neuron 1D.
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Books on the topic "Brain – Computer simulation"

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Horstmann, Wolfram. Explaining brains by simulation. Bielefeld, Germany: s.n., 2003.

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1933-, Cotterill Rodney, ed. Models of brain function. Cambridge: Cambridge University Press, 1989.

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Steyn-Ross, Alistair, and Moira Steyn-Ross. Modeling phase transitions in the brain. New York: Springer, 2010.

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Kim, Plunkett, and Rolls Edmund T, eds. Introduction to connectionist modelling of cognitive processes. Oxford: Oxford University Press, 1998.

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An introduction to natural computation. Cambridge, Mass: MIT Press, 1997.

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J, Sejnowski Terrence, ed. The computational brain. Cambridge, Mass: MIT Press, 1992.

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Andrew, Coward L., ed. A system architecture approach to the brain: From neurons to consciousness. Hauppauge, NY: Nova Science, 2005.

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David, Hutchison. Creating Brain-Like Intelligence: From Basic Principles to Complex Intelligent Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Kasabov, Nikola. Evolving connectionist systems: Methods and applications in bioinformatics, brain study and intelligent machines. London: Springer, 2003.

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Trehub, Arnold. The cognitive brain. Cambridge, Mass: MIT Press, 1991.

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Book chapters on the topic "Brain – Computer simulation"

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Bilger, Alexandre, Jérémie Dequidt, Christian Duriez, and Stéphane Cotin. "Biomechanical Simulation of Electrode Migration for Deep Brain Stimulation." In Lecture Notes in Computer Science, 339–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23623-5_43.

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Li, Shaobin, and Chenxi Shao. "Classification of Single Trial EEG Based on Cloud Model for Brain-Computer Interfaces." In Life System Modeling and Simulation, 335–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74771-0_38.

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Yu, Yue, George Bourantas, Tina Kapur, Sarah Frisken, Ron Kikinis, Arya Nabavi, Alexandra Golby, Adam Wittek, and Karol Miller. "Computer Simulation of the Resection Induced Brain Shift; Preliminary Results." In Computational Biomechanics for Medicine, 17–29. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70123-9_2.

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Soza, Grzegorz, Roberto Grosso, Christopher Nimsky, Guenther Greiner, and Peter Hastreiter. "Estimating Mechanical Brain Tissue Properties with Simulation and Registration." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2004, 276–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30136-3_35.

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Huang, Xinru, Xianwei Xue, and Zhongyun Yuan. "A Simulation Platform for the Brain-Computer Interface (BCI) Based Smart Wheelchair." In Lecture Notes in Computer Science, 257–66. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57884-8_23.

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Kaus, M. R., A. Nabavi, C. T. Mamisch, W. H. Wells, F. A. Jolesz, R. Kikinis, and S. K. Warfield. "Simulation of Corticospinal Tract Displacement in Patients with Brain Tumors." In Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000, 9–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-540-40899-4_2.

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Xue, Zhong, Dinggang Shen, Bilge Karacali, and Christos Davatzikos. "Statistical Representation and Simulation of High-Dimensional Deformations: Application to Synthesizing Brain Deformations." In Lecture Notes in Computer Science, 500–508. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11566489_62.

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Silva, Mariana Da, Carole H. Sudre, Kara Garcia, Cher Bass, M. Jorge Cardoso, and Emma C. Robinson. "Distinguishing Healthy Ageing from Dementia: A Biomechanical Simulation of Brain Atrophy Using Deep Networks." In Lecture Notes in Computer Science, 13–22. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87586-2_2.

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Vargas Cardona, Hernán Darío, Álvaro A. Orozco, and Mauricio A. Álvarez. "Analysis of the Geometry and Electric Properties of Brain Tissue in Simulation Models for Deep Brain Stimulation." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, 493–501. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-52277-7_60.

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Ferreira, Cleiton Pons, and Carina Soledad González González. "State of the Art of Business Simulation Games Modeling Supported by Brain-Computer Interfaces." In Communications in Computer and Information Science, 243–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66919-5_25.

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Conference papers on the topic "Brain – Computer simulation"

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Zhang, Yuxuang, and Qianqian Fang. "BlenderPhotonics – an integrated computer-aided design, meshing and Monte Carlo simulation environment for biophotonics." In Optics and the Brain. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/brain.2022.bm2c.4.

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Svejda, Jaromir, Roman Zak, Roman Senkerik, and Roman Jasek. "Using Brain - Computer Interface For Control Robot Movement." In 29th Conference on Modelling and Simulation. ECMS, 2015. http://dx.doi.org/10.7148/2015-0475.

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"Special session on “brain-targeted and brain-inspired computing”." In 2014 International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV). IEEE, 2014. http://dx.doi.org/10.1109/samos.2014.6893233.

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Cheng, Shirley N. "Computer simulation of magnetic resonance images using fractal-grown brain slices." In Midwest - DL tentative, edited by Rudolph P. Guzik, Hans E. Eppinger, Richard E. Gillespie, Mary K. Dubiel, and James E. Pearson. SPIE, 1991. http://dx.doi.org/10.1117/12.47734.

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Caldeira, L., S. Lalitha, M. Lenz, R. Deepu, W. Klijn, C. Lerche, N. J. Shah, and U. Pietrzyk. "Full dynamic brain simulation using GATE in a high-performance computer." In 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC). IEEE, 2017. http://dx.doi.org/10.1109/nssmic.2017.8532732.

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Nam, Seungkyu, Kyung Hwan Kim, and Dae-Shik Kim. "Motor trajectory decoding based on fMRI-based BCI — A simulation study." In 2013 International Winter Workshop on Brain-Computer Interface (BCI). IEEE, 2013. http://dx.doi.org/10.1109/iww-bci.2013.6506641.

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Zhang, Nannan, Yadong Liu, and Zongtan Zhou. "A SSVEP-BCI with random moving stimuli in simulation environment." In 2017 5th International Winter Conference on Brain-Computer Interface (BCI). IEEE, 2017. http://dx.doi.org/10.1109/iww-bci.2017.7858170.

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Lanhua Zhang, Mei Wang, Yujuan Li, and Shaowei Xue. "Deterministic modeling and simulation in brain functional complex nework." In 2011 International Conference on Computer Science and Network Technology (ICCSNT). IEEE, 2011. http://dx.doi.org/10.1109/iccsnt.2011.6182112.

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Bi, Luzheng, Ke Jie, Jinling Lian, and Bingqing Wu. "A SSVEP brain–computer interface under the hybrid stimuli of P300 and SSVEP." In International Conference on Simulation and Modeling Methodologies, Technologies and Applications. Southampton, UK: WIT Press, 2014. http://dx.doi.org/10.2495/smta141252.

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Batmunkh, Munkhbaatar, Lkhagvaa Bayarchimeg, Aleksandr N. Bugay, and Oidov Lkhagva. "Computer simulation of radiation damage mechanisms in the structure of brain cells." In PROCEEDINGS OF THE 24TH INTERNATIONAL SCIENTIFIC CONFERENCE OF YOUNG SCIENTISTS AND SPECIALISTS (AYSS-2020). AIP Publishing, 2021. http://dx.doi.org/10.1063/5.0063370.

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Reports on the topic "Brain – Computer simulation"

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Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.
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