Journal articles: '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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Kryger, Michael, Brock Wester, Eric A. Pohlmeyer, Matthew Rich, Brendan John, James Beaty, Michael McLoughlin, Michael Boninger, and Elizabeth C. Tyler-Kabara. "Flight simulation using a Brain-Computer Interface: A pilot, pilot study." Experimental Neurology 287 (January 2017): 473–78. http://dx.doi.org/10.1016/j.expneurol.2016.05.013.

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Taha, Ibrahem, and Gregory Cook. "Brain sources estimation based on EEG and computer simulation technology (CST)." Biomedical Signal Processing and Control 46 (September 2018): 145–56. http://dx.doi.org/10.1016/j.bspc.2018.03.011.

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Schneider, K., and R. F. Zernicke. "Brain injury risk during soccer heading: Experimental results and computer simulation." Journal of Biomechanics 20, no. 8 (January 1987): 817–18. http://dx.doi.org/10.1016/0021-9290(87)90107-2.

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Demichelis, Remy. "Can We Learn Anything from Brain Simulation?" Glimpse 22, no. 1 (2021): 7–12. http://dx.doi.org/10.5840/glimpse20212212.

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If you figure out how machines learn, then you will figure out how the brain works, and what the brain’s functions are. Such an idea is widespread among philosophers and computer scientists who agree with a functionalist reductionist point of view of consciousness. This theory leads to hold that the more accurate the simulation of cognitive behavior is, the more the math behind it must be true – when true means what really happens in our brain. In this article, we aim to show that, on one hand, brain simulation is nothing more than just another simulation, and it offers very little help to understand – nor to produce – the vivid experiences (qualia) of cognitive functions. On the other hand, we would like to emphasize that when it succeeds at predicting a mechanism with less ambiguity and more accuracy than without a simulation nor direct observation, it really develops the knowledge of our brain. As long as brain simulation follows scientific principles, it should be regarded as valuable, even though the knowledge it brings to science must not be confused for the real phenomenon. Brain simulation, like all simulation, cannot fill any reality or epistemic gap. It is a consolation prize.

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Li, Peng. "Research on Visual Art Design Method Based on Virtual Reality." International Journal of Gaming and Computer-Mediated Simulations 13, no. 2 (April 2021): 16–25. http://dx.doi.org/10.4018/ijgcms.2021040102.

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In today's society, computer technology has been deeply rooted in the hearts of people. Computers are wonderful tools for creative thinking. It is an extension of our visual function and the function of the visual cortex of the brain. Through this extension, we can see more scenes that we could not see before. As a computer simulation system that creates and feels virtual worlds, 3D digital virtual reality technology uses a computer as a media simulation or a real or imaginary scene. It is a system simulation of interactive 3D dynamic vision and entity behavior based on diversified information fusion. As the creator of visual arts, we must try to observe the world at a deeper level and establish a model that resonates with the viewer. At every level, our technology will convey the way we view the world more deeply. We will be more amazed at the richness of the real world. This chapter explores a visual art design method based on virtual reality.

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Sanders, Laura. "Mind & brain: Model brain mimics human quirks: Computer simulation turns decisions into plans for action." Science News 183, no. 1 (December 28, 2012): 13. http://dx.doi.org/10.1002/scin.5591830114.

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Ledesma, Sergio, Jose Ruiz Pinales, Ma Guadalupe Garcia, and Francisco Elizalde. "Simple Human Emotions Modeling Oriented to Human Learning: A Brain Computer Simulation." Ubiquitous Learning: An International Journal 3, no. 2 (2011): 125–38. http://dx.doi.org/10.18848/1835-9795/cgp/v03i02/40267.

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Clark, John W., Johann Rafelski, and Jeffrey V. Winston. "Brain without mind: Computer simulation of neural networks with modifiable neuronal interactions." Physics Reports 123, no. 4 (July 1985): 215–73. http://dx.doi.org/10.1016/0370-1573(85)90038-9.

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Fujita, Setsuya. "Morphogenesis of the brain as studied by 3-D computer graphics simulation." Journal of Microscopy 157, no. 3 (March 1990): 259–69. http://dx.doi.org/10.1111/j.1365-2818.1990.tb02965.x.

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Awasthi, Abhilash, Umesh Gautam, Suryanarayanan Bhaskar, and Sitikantha Roy. "Biomechanical modelling and computer aided simulation of deep brain retraction in neurosurgery." Computer Methods and Programs in Biomedicine 197 (December 2020): 105688. http://dx.doi.org/10.1016/j.cmpb.2020.105688.

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Sun, Yong, Li Zhao, Neng Xu, and Rong Ou. "Research of Visual Stimulation Method and Design of Visual Stimulator Based on Brain-Computer Interface." Applied Mechanics and Materials 734 (February 2015): 375–82. http://dx.doi.org/10.4028/www.scientific.net/amm.734.375.

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Brain Computer Interface (BCI) is a new ways of communicating with outside for the loss of some or all of the muscles controlling function of the patients. And the BCI is to set up a new information communication and control channel though the computer or other electronic device between the human brain and the external environment that does not depend on the peripheral nerve and muscle tissue. Firstly, this paper studies the methods of visual stimulation based on Brain Computer Interface that classified by stimulating form can be divided into flash simulation and figures simulation and classified by stimulating frequency can be divide into transient visual stimulation and steady-state visual stimulation. Then, using FPGA and the VGA interface designed of the visual stimulator that can be used to acquisition of steady-state visual evoked potential. Finally, adopting EEG signal processing platform verify this simulator. After numbers of verification, this simulator obtains a good desired result which achieved over 80% accuracy rate.

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Komosinski, Maciej. "Universes and simulations: Civilizational development in nested embedding." Foundations of Computing and Decision Sciences 43, no. 3 (September 1, 2018): 181–205. http://dx.doi.org/10.1515/fcds-2018-0010.

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Abstract The rapid development of technology has allowed computer simulations to become routinely used in an increasing number of fields of science. These simulations become more and more realistic, and their energetic efficiency grows due to progress in computer hardware and software. As humans merge with machines via implants, brain-computer interfaces and increased activity involving information instead of material objects, philosophical concepts and theoretical considerations on the nature of reality are beginning to concern practical, working models and testable virtual environments. This article discusses how simulation is understood and employed in computer science today, how software, hardware and the physical universe unify, how simulated realities are embedded one in another, how complicated it can get in application, practical scenarios, and the possible consequences of these situations. A number of basic properties of universes and simulations in such multiply nested structures are reviewed, and the relationship of these properties with a level of civilizational development is explored.

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Cremonesi, Francesco, Georg Hager, Gerhard Wellein, and Felix Schürmann. "Analytic performance modeling and analysis of detailed neuron simulations." International Journal of High Performance Computing Applications 34, no. 4 (April 3, 2020): 428–49. http://dx.doi.org/10.1177/1094342020912528.

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Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state-of-the-art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering, which is based on “white-box,” that is, first-principles performance models, to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive codesign efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually codesign of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

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Tohka, Jussi, Pierre Bellec, Christophe Grova, and Anthonin Reilhac. "Simulation and Validation in Brain Image Analysis." Computational Intelligence and Neuroscience 2016 (2016): 1–2. http://dx.doi.org/10.1155/2016/1041058.

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Rossler, Otto E., Yaël Kolb, and Andrei Ujica. "Going Back to Einstein's 1907 in the Modern Computer Age." Journal of Advance Research in Computer Science & Engineering (ISSN: 2456-3552) 6, no. 3 (March 31, 2019): 01–04. http://dx.doi.org/10.53555/nncse.v6i3.383.

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Einstein’s equivalence principle of 1907 amounts to a mental analog computer that is ready for simulation in the modern computer age. Several new fundamental implications do already come to the fore in the process of merely preparing to do so. They are as revolutionary as one is used to from Einstein’s brain children.

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Toiviainen, Petri, Mari Tervaniemi, Jukka Louhivuori, Marieke Saher, Minna Huotilainen, and Risto Näätänen. "Timbre Similarity: Convergence of Neural, Behavioral, and Computational Approaches." Music Perception 16, no. 2 (1998): 223–41. http://dx.doi.org/10.2307/40285788.

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The present study compared the degree of similarity of timbre representations as observed with brain recordings, behavioral studies, and computer simulations. To this end, the electrical brain activity of subjects was recorded while they were repetitively presented with five sounds differing in timbre. Subjects read simultaneously so that their attention was not focused on the sounds. The brain activity was quantified in terms of a change-specific mismatch negativity component. Thereafter, the subjects were asked to judge the similarity of all pairs along a five-step scale. A computer simulation was made by first training a Kohonen self-organizing map with a large set of instrumental sounds. The map was then tested with the experimental stimuli, and the distance between the most active artificial neurons was measured. The results of these methods were highly similar, suggesting that timbre representations reflected in behavioral measures correspond to neural activity, both as measured directly and as simulated in self-organizing neural network models.

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Miller, K., A. Wittek, G. Joldes, A. Horton, T. Dutta-Roy, J. Berger, and L. Morriss. "Modelling brain deformations for computer-integrated neurosurgery." International Journal for Numerical Methods in Biomedical Engineering 26, no. 1 (January 2010): 117–38. http://dx.doi.org/10.1002/cnm.1260.

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Basiakova, K. V., and E. P. Titovets. "Computer simulation of aquaporin4-dependent water transfer across the hematoencephalic barrier." Proceedings of the National Academy of Sciences of Belarus, Biological Series 64, no. 2 (May 18, 2019): 190–97. http://dx.doi.org/10.29235/1029-8940-2019-64-2-190-197.

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A computational simulation of water transfer across the blood-brain barrier (BBB) has been carried out. In the developed model, AQP4 plays a kinetically limiting role in water transfer across the BBB. The effects of the AQP4 specific density changes and its polarized distribution have been studied in respect to the volumetric water transfer. It has been demonstrated that AQP4 density and polarization within the glial membranes enveloping the capillary can affect the volumetric flow and the sign of the water flux. The results might be used for elucidation of the pathogenic mechanism of cerebral edema and in development of the ways of pharmacological correction of the cerebral water metabolism disorders.

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Wittek, Adam, George Bourantas, Benjamin F. Zwick, Grand Joldes, Lionel Esteban, and Karol Miller. "Mathematical modeling and computer simulation of needle insertion into soft tissue." PLOS ONE 15, no. 12 (December 22, 2020): e0242704. http://dx.doi.org/10.1371/journal.pone.0242704.

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In this study we present a kinematic approach for modeling needle insertion into soft tissues. The kinematic approach allows the presentation of the problem as Dirichlet-type (i.e. driven by enforced motion of boundaries) and therefore weakly sensitive to unknown properties of the tissues and needle-tissue interaction. The parameters used in the kinematic approach are straightforward to determine from images. Our method uses Meshless Total Lagrangian Explicit Dynamics (MTLED) method to compute soft tissue deformations. The proposed scheme was validated against experiments of needle insertion into silicone gel samples. We also present a simulation of needle insertion into the brain demonstrating the method’s insensitivity to assumed mechanical properties of tissue.

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Armstrong, J. Douglas, and Jano I. van Hemert. "Towards a virtual fly brain." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1896 (June 13, 2009): 2387–97. http://dx.doi.org/10.1098/rsta.2008.0308.

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Models of the brain that simulate sensory input, behavioural output and information processing in a biologically plausible manner pose significant challenges to both computer science and biology. Here we investigated strategies that could be used to create a model of the insect brain, specifically that of Drosophila melanogaster that is very widely used in laboratory research. The scale of the problem is an order of magnitude above the most complex of the current simulation projects, and it is further constrained by the relative sparsity of available electrophysiological recordings from the fly nervous system. However, fly brain research at the anatomical and behavioural levels offers some interesting opportunities that could be exploited to create a functional simulation. We propose to exploit these strengths of Drosophila central nervous system research to focus on a functional model that maps biologically plausible network architecture onto phenotypic data from neuronal inhibition and stimulation studies, leaving aside biophysical modelling of individual neuronal activity for future models until more data are available.

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Qasim, Mohammed, and Omar Y. Ismael. "Shared Control of a Robot Arm Using BCI and Computer Vision." Journal Européen des Systèmes Automatisés 55, no. 1 (February 28, 2022): 139–46. http://dx.doi.org/10.18280/jesa.550115.

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Brain-Computer Interface (BCI) is a device that can transform human thoughts into control commands. However, BCI aggravates the common problems of robot teleoperation due to its low-dimensional and noisy control commands, particularly when utilized to control high-DOF robots. Thus, a shared control strategy can enhance the BCI performance and reduce the workload for humans. This paper presents a shared control scheme that assists disabled people to control a robotic arm through a non-invasive Brain-Computer Interface (BCI) for reach and grasp activities. A novel algorithm is presented which generates a trajectory (position and orientation) for the end-effector to reach and grasp an object based on a specially designed color-coded tag placed on the object. A single camera is used for tag detection. The simulation is performed using the CoppeliaSim robot simulator in conjunction with MATLAB to implement the tag detection algorithm and Python script to receive the commands from the BCI. The human-in-the-loop simulation results prove the effectiveness of the proposed algorithm to reach and grasp objects.

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Brunner, Clemens, Brendan Z. Allison, Dean J. Krusienski, Vera Kaiser, Gernot R. Müller-Putz, Gert Pfurtscheller, and Christa Neuper. "Improved signal processing approaches in an offline simulation of a hybrid brain–computer interface." Journal of Neuroscience Methods 188, no. 1 (April 2010): 165–73. http://dx.doi.org/10.1016/j.jneumeth.2010.02.002.

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Vimy, M. J., A. J. Luft, and F. L. Lorscheider. "Estimation of Mercury Body Burden from Dental Amalgam: Computer Simulation of a Metabolic Compartmental Model." Journal of Dental Research 65, no. 12 (December 1986): 1415–19. http://dx.doi.org/10.1177/00220345860650120701.

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Estimated release rates of Hg vapor from dental amalgams permitted calculation of the potential Hg body burden by employing a four-compartment model for inorganic and elemental Hg distribution. A computer program, compatible with most personal computers, simulated the cumulative and incremental distribution in each compartment and total body accumulation between 1 and 10,000 days for different daily Hg dosages. For a given Hg dose of 30 μ g/day, metabolic compartments R1-R3 were close to equilibrium at 5, 100, and 300 days, respectively; whereas by 10,000 days, R4 closely approximated total body burden and had not yet attained equilibrium. Projected values obtained with the computer model were consistent with results obtained by another method using a standard tissue burden equation, which employed experimentally determined tissue half-lives for blood and CNS. The model predicted that continuous exposure to elemental Hg vapor, at 30 μ g/day for 10 years, would result in a total Hg body burden of 5.9 mg, of which 4.8 mg could be contained in R4. Assuming that the Hg in R4 displayed uniform distribution throughout the body, then the brain concentration was estimated to be 68 nglg wet weight. In contrast, if Hg in R4 reflected long-term preferential accumulation in brain and other neural tissue, then concentrations as high as 4.0 μ g/g could be attained. However, predictions of Hg concentrations in blood and urine were well within established ranges, and were unlikely to be of utility in assessing effects of chronic low-dose Hg exposure. It is concluded that the CNS could accumulate a substantial amount of Hg over extended time, based on low-dose elemental Hg vapor exposure via inhalation from dental amalgams.

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Van Asseldonk, J. T. H. "Criteria for conduction block based on computer simulation studies of nerve conduction with human data obtained in the forearm segment of the median nerve." Brain 129, no. 9 (September 1, 2006): 2447–60. http://dx.doi.org/10.1093/brain/awl197.

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Horwitz, Barry. "Functional Interactions in the Brain: Use of Correlations between Regional Metabolic Rates." Journal of Cerebral Blood Flow & Metabolism 11, no. 1_suppl (March 1991): A114—A120. http://dx.doi.org/10.1038/jcbfm.1991.46.

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Correlation coefficients between pairs of regional metabolic rates have been used to study patterns of functional associations among brain regions in humans and animals. An overview is provided concerning the additional information about brain functioning this type of analysis yields. A computer simulation model is presented for the purpose of giving a partial validation for correlational analysis. The model generates a set of simulated metabolic data upon which correlational analysis is performed. Because the underlying pattern of functional couplings in the model is known, these simulations demonstrate that the correlation coefficient between normalized metabolic rates is proportional to the strength of the functional coupling constant and that correlational analysis yields information on regional involvement in neural systems not evident in the pattern of absolute metabolic values.

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Soto, David, Usman Ayub Sheikh, Ning Mei, and Roberto Santana. "Decoding and encoding models reveal the role of mental simulation in the brain representation of meaning." Royal Society Open Science 7, no. 5 (May 2020): 192043. http://dx.doi.org/10.1098/rsos.192043.

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How the brain representation of conceptual knowledge varies as a function of processing goals, strategies and task-factors remains a key unresolved question in cognitive neuroscience. In the present functional magnetic resonance imaging study, participants were presented with visual words during functional magnetic resonance imaging (fMRI). During shallow processing, participants had to read the items. During deep processing, they had to mentally simulate the features associated with the words. Multivariate classification, informational connectivity and encoding models were used to reveal how the depth of processing determines the brain representation of word meaning. Decoding accuracy in putative substrates of the semantic network was enhanced when the depth processing was high, and the brain representations were more generalizable in semantic space relative to shallow processing contexts. This pattern was observed even in association areas in inferior frontal and parietal cortex. Deep information processing during mental simulation also increased the informational connectivity within key substrates of the semantic network. To further examine the properties of the words encoded in brain activity, we compared computer vision models—associated with the image referents of the words—and word embedding. Computer vision models explained more variance of the brain responses across multiple areas of the semantic network. These results indicate that the brain representation of word meaning is highly malleable by the depth of processing imposed by the task, relies on access to visual representations and is highly distributed, including prefrontal areas previously implicated in semantic control.

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ZHOU, SHUANGZHEN, XIONG ZHANG, and HONGLEI MA. "NUMERICAL SIMULATION OF HUMAN HEAD IMPACT USING THE MATERIAL POINT METHOD." International Journal of Computational Methods 10, no. 04 (April 23, 2013): 1350014. http://dx.doi.org/10.1142/s021987621350014x.

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In this paper, a three-dimensional material point human head model is constructed from the computed tomography (CT) scanned images of an adult male volunteer, and used to study the dynamic response of human head under the impact of a three-dimensional cylindrical lead projectile with a speed of 6.4 m/s. The model consists of skull bone, brain tissue and membrane of human head, which is close to the real one. The skull and membrane are modeled by an elastic constitutive model, and the brain tissue is modeled by an anisotropic viscoelastic constitutive model. These constitutive models have been implemented in our three-dimensional explicit material point method code, MPM3D, and is verified by comparing its numerical results for a ball impact problem with those obtained by LS-DYNA. The simulation results help illustrate the response of skull bone, membrane and brain tissues subjected to impact, which contributes to the understanding of the biomechanics and mechanisms of head injury.

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Liu, Xin, Yi Zeng, Tielin Zhang, and Bo Xu. "Parallel Brain Simulator: A Multi-scale and Parallel Brain-Inspired Neural Network Modeling and Simulation Platform." Cognitive Computation 8, no. 5 (April 23, 2016): 967–81. http://dx.doi.org/10.1007/s12559-016-9411-y.

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Al Ajrawi, Shams, Ramesh Rao, and Mahasweta Sarkar. "Efficient MAC Protocols for Brain Computer Interface Applications." Computers, Materials & Continua 69, no. 1 (2021): 589–605. http://dx.doi.org/10.32604/cmc.2021.016930.

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Wang, Xiashuang, Guanghong Gong, and Ni Li. "Multimodal fusion of EEG and fMRI for epilepsy detection." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 02 (March 20, 2018): 1850010. http://dx.doi.org/10.1142/s1793962318500101.

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Technology of brain–computer interface (BCI) provides a new way of communication and control without language or physical action. Brain signal tracking and positioning is the basis of BCI research, while brain modeling affects the treatment analysis of (EEG) and functional magnetic resonance imaging (fMRI) directly. This paper proposes human ellipsoid brain modeling method. Then, we use non-parametric spectral estimation method of time–frequency analysis to deal with simulation and real EEG of epilepsy patients, which utilizes both the high spatial and the high time resolution to improve the doctor’s diagnostic efficiency.

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Qiu, Shi, Junjun Li, Mengdi Cong, Chun Wu, Yan Qin, and Ting Liang. "Detection of Solitary Pulmonary Nodules Based on Brain-Computer Interface." Computational and Mathematical Methods in Medicine 2020 (June 15, 2020): 1–10. http://dx.doi.org/10.1155/2020/4930972.

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Solitary pulmonary nodules are the main manifestation of pulmonary lesions. Doctors often make diagnosis by observing the lung CT images. In order to further study the brain response structure and construct a brain-computer interface, we propose an isolated pulmonary nodule detection model based on a brain-computer interface. First, a single channel time-frequency feature extraction model is constructed based on the analysis of EEG data. Second, a multilayer fusion model is proposed to establish the brain-computer interface by connecting the brain electrical signal with a computer. Finally, according to image presentation, a three-frame image presentation method with different window widths and window positions is proposed to effectively detect the solitary pulmonary nodules.

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Tonar, Zbyněk, Petra Kochová, Robert Cimrman, Kirsti Witter, Jiří Janáček, and Vladimír Rohan. "Microstructure Oriented Modelling of Hierarchically Perfused Porous Media for Cerebral Blood Flow Evaluation." Key Engineering Materials 465 (January 2011): 286–89. http://dx.doi.org/10.4028/www.scientific.net/kem.465.286.

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We used immunochemistry, light microscopy and stereological methods for quantitative description of the microvascular network in 13 tissue samples of the human brain. While the tortuosity of microvessels was comparable in all brain parts under study, the length density of microvessels was higher in subcortical grey matter (652.5±162.0 mm-2) and in the cortex (570.9±71.8 mm-2) than in the white matter (152.7±42.0 mm-2). The numerical density of microvessels was higher in subcortical grey matter (3782.0±1602.0 mm-3) and cerebral cortex (3160.0±638.4 mm-3) than in white matter (627.7±318.5 mm-3). We developed simulation software gensei which generates series of images representing three-dimensional models of microvessels with known length density, volume fraction, and surface density. The simulations are statistically similar to real microvessel networks and can be used for computer modelling of brain perfusion.

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Hakala, Jaakko, Joni Kilpijarvi, Mariella Sarestoniemi, Matti Hamalainen, Sami Myllymaki, and Teemu Myllyla. "Microwave Sensing of Brain Water – a Simulation and Experimental Study Using Human Brain Models." IEEE Access 8 (2020): 111303–15. http://dx.doi.org/10.1109/access.2020.3001867.

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Karantasis, Konstantinos I., Eleftherios D. Polychronopoulos, Konstantinos T. Panourgias, and John A. Ekaterinaris. "Accelerating the simulation of brain tumor proliferation with many-core GPUs." Journal of Computational Science 3, no. 5 (September 2012): 306–13. http://dx.doi.org/10.1016/j.jocs.2011.06.005.

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Yu, Tianyou, Yuanqing Li, Jinyi Long, and Feng Li. "A Hybrid Brain-Computer Interface-Based Mail Client." Computational and Mathematical Methods in Medicine 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/750934.

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Brain-computer interface-based communication plays an important role in brain-computer interface (BCI) applications; electronic mail is one of the most common communication tools. In this study, we propose a hybrid BCI-based mail client that implements electronic mail communication by means of real-time classification of multimodal features extracted from scalp electroencephalography (EEG). With this BCI mail client, users can receive, read, write, and attach files to their mail. Using a BCI mouse that utilizes hybrid brain signals, that is, motor imagery and P300 potential, the user can select and activate the function keys and links on the mail client graphical user interface (GUI). An adaptive P300 speller is employed for text input. The system has been tested with 6 subjects, and the experimental results validate the efficacy of the proposed method.

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Fraile, Alberto, Emmanouil Panagiotakis, Nicholas Christakis, and Luis Acedo. "Cellular Automata and Artificial Brain Dynamics." Mathematical and Computational Applications 23, no. 4 (November 16, 2018): 75. http://dx.doi.org/10.3390/mca23040075.

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Brain dynamics, neuron activity, information transfer in brains, etc., are a vast field where a large number of questions remain unsolved. Nowadays, computer simulation is playing a key role in the study of such an immense variety of problems. In this work, we explored the possibility of studying brain dynamics using cellular automata, more precisely the famous Game of Life (GoL). The model has some important features (i.e., pseudo-criticality, 1/f noise, universal computing), which represent good reasons for its use in brain dynamics modelling. We have also considered that the model maintains sufficient flexibility. For instance, the timestep is arbitrary, as are the spatial dimensions. As first steps in our study, we used the GoL to simulate the evolution of several neurons (i.e., a statistically significant set, typically a million neurons) and their interactions with the surrounding ones, as well as signal transfer in some simple scenarios. The way that signals (or life) propagate across the grid was described, along with a discussion on how this model could be compared with brain dynamics. Further work and variations of the model were also examined.

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Drake, James M., Michael Joy, Andrew Goldenberg, and David Kreindler. "Computer- and Robot-assisted Resection of Thalamic Astrocytomas in Children." Neurosurgery 29, no. 1 (July 1, 1991): 27–33. http://dx.doi.org/10.1227/00006123-199107000-00005.

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Abstract Six children ranging in age from 2 to 10 years who harbored deep benign astrocytomas were operated upon using a computer- and robot-assisted system. A radical excision was achieved in all cases with no significant morbidity nor any mortality. The system consists of an interactive, three-dimensional display of computed tomographic image contours and digitized cerebral angiograms taken using the Brown-Roberts-Wells stereotactic frame. The surgical retractor is held and manipulated using a PUMA 200 robot. The position and orientation of the surgical retractor is displayed on the three-dimensional display. Preoperative planning and simulation are important features of this system. Movement of the brain after removal of the tumor and cerebrospinal fluid is substantial, so the tumor removal is based on visually defined margins. Enhanced computer graphics and robotic devices are important adjuncts to neurosurgical procedures and will find increasing use in the future. (Neurosurgery 29:27-31, 1991)

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MOCHIZUKI, Yasuhiro, Seiya TERASHIMA, Masatoshi SUZUKI, Tetsuya NISHIMOTO, and Robert ANDERSON. "3A04 The Analysis of Brain Injury Based on the β-APP Protein and Computer Simulation." Proceedings of the Bioengineering Conference Annual Meeting of BED/JSME 2013.25 (2013): 469–70. http://dx.doi.org/10.1299/jsmebio.2013.25.469.

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Kocher, Martin, Harald Treuer, Jürgen Voges, Moritz Hoevels, Volker Sturm, and Rolf-Peter Müller. "Computer simulation of cytotoxic and vascular effects of radiosurgery in solid and necrotic brain metastases." Radiotherapy and Oncology 54, no. 2 (February 2000): 149–56. http://dx.doi.org/10.1016/s0167-8140(99)00168-1.

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Hammad, Sofyan H., Ernest N. Kamavuako, Dario Farina, and Winnie Jensen. "Simulation of a Real-Time Brain Computer Interface for Detecting a Self-Paced Hitting Task." Neuromodulation: Technology at the Neural Interface 19, no. 8 (August 11, 2016): 804–11. http://dx.doi.org/10.1111/ner.12478.

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Journal articles on the topic 'Brain – Computer simulation'

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