Bibliografias temáticas / Computational nerve model

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Literatura científica selecionada sobre o tema "Computational nerve model"

Autor: Grafiati

Publicado: 4 de junho de 2021

Última modificação: 1 de fevereiro de 2022

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Artigos de revistas sobre o assunto "Computational nerve model"

Helmers, S. L., J. Begnaud, A. Cowley, H. M. Corwin, J. C. Edwards, D. L. Holder, H. Kostov et al. "Application of a computational model of vagus nerve stimulation". Acta Neurologica Scandinavica 126, n.º 5 (24 de fevereiro de 2012): 336–43. http://dx.doi.org/10.1111/j.1600-0404.2012.01656.x.

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Michalkova, A., e J. Leszczynski. "Interactions of nerve agents with model surfaces: Computational approach". Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 28, n.º 4 (julho de 2010): 1010–17. http://dx.doi.org/10.1116/1.3271148.

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Lubba, Carl H., Yann Le Guen, Sarah Jarvis, Nick S. Jones, Simon C. Cork, Amir Eftekhar e Simon R. Schultz. "PyPNS: Multiscale Simulation of a Peripheral Nerve in Python". Neuroinformatics 17, n.º 1 (15 de junho de 2018): 63–81. http://dx.doi.org/10.1007/s12021-018-9383-z.

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Abstract Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms. To reduce experimentation load and allow for a faster, more detailed analysis of peripheral nerve stimulation and recording, computational models incorporating experimental insights will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealised extracellular space models in one environment. We modelled the extracellular space as a three-dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed in finite element models for different media (homogeneous, nerve in saline, nerve in cuff) and imported into our simulator. Axons, on the other hand, were modelled more abstractly as one-dimensional chains of compartments. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibres, we adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibres along the nerve with a variable tortuosity fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity alters recorded signal shapes and increases stimulation thresholds. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

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Beck, Jeremy M., e Christopher M. Hadad. "Hydrolysis of nerve agents by model nucleophiles: A computational study". Chemico-Biological Interactions 175, n.º 1-3 (setembro de 2008): 200–203. http://dx.doi.org/10.1016/j.cbi.2008.04.026.

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Giannessi, Elisabetta, Maria Rita Stornelli e Pier Nicola Sergi. "A unified approach to model peripheral nerves across different animal species". PeerJ 5 (10 de novembro de 2017): e4005. http://dx.doi.org/10.7717/peerj.4005.

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Peripheral nerves are extremely complex biological structures. The knowledge of their response to stretch is crucial to better understand physiological and pathological states (e.g., due to overstretch). Since their mechanical response is deterministically related to the nature of the external stimuli, theoretical and computational tools were used to investigate their behaviour. In this work, a Yeoh-like polynomial strain energy function was used to reproduce the response of in vitro porcine nerve. Moreover, this approach was applied to different nervous structures coming from different animal species (rabbit, lobster, Aplysia) and tested for different amount of stretch (up to extreme ones). Starting from this theoretical background, in silico models of both porcine nerves and cerebro-abdominal connective of Aplysia were built to reproduce experimental data (R2 > 0.9). Finally, bi-dimensional in silico models were provided to reduce computational time of more than 90% with respect to the performances of fully three-dimensional models.

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Sharma, G. C., e Madhu Jain. "A computational solution of mathematical model for oxygen transport in peripheral nerve". Computers in Biology and Medicine 34, n.º 7 (outubro de 2004): 633–45. http://dx.doi.org/10.1016/s0010-4825(03)00043-x.

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Yang, Changhui, Ruixia Yang, Tingting Xu e Yinxia Li. "Computational model of enterprise cooperative technology innovation risk based on nerve network". Journal of Algorithms & Computational Technology 12, n.º 2 (22 de março de 2018): 177–84. http://dx.doi.org/10.1177/1748301818762527.

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Collaborative innovation has become the principal innovation method because of the puniness of the innovation strength of the enterprises. Due to the objective reality nature of the risk of enterprise collaborative technology innovation, it is necessary to take measures to prevent and indemnify the loss which the risk may bring. Because there is the complex nonlinear function mechanism between risk factors, the cooperative mode and control mechanism of enterprise collaborative innovation can be studied by nonlinear method. First, this paper analyzed the seeking method of enterprise collaborative innovation risk, and then the concept of controlling risk regulation gradient of the cooperating technological innovation under network environment was explained. And a complete controlling risk model of the cooperating technological innovation has been put forward, which is based on the wavelet and nerve network. Finally, the discussion about the conclusion of the research was given.

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Sachs, Murray B., Raimond L. Winslow e Bernd H. A. Sokolowski. "A computational model for rate-level functions from cat auditory-nerve fibers". Hearing Research 41, n.º 1 (agosto de 1989): 61–69. http://dx.doi.org/10.1016/0378-5955(89)90179-2.

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Bacqué-Cazenave, Julien, Bryce Chung, David W. Cofer, Daniel Cattaert e Donald H. Edwards. "The effect of sensory feedback on crayfish posture and locomotion: II. Neuromechanical simulation of closing the loop". Journal of Neurophysiology 113, n.º 6 (15 de março de 2015): 1772–83. http://dx.doi.org/10.1152/jn.00870.2014.

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Neuromechanical simulation was used to determine whether proposed thoracic circuit mechanisms for the control of leg elevation and depression in crayfish could account for the responses of an experimental hybrid neuromechanical preparation when the proprioceptive feedback loop was open and closed. The hybrid neuromechanical preparation consisted of a computational model of the fifth crayfish leg driven in real time by the experimentally recorded activity of the levator and depressor (Lev/Dep) nerves of an in vitro preparation of the crayfish thoracic nerve cord. Up and down movements of the model leg evoked by motor nerve activity released and stretched the model coxobasal chordotonal organ (CBCO); variations in the CBCO length were used to drive identical variations in the length of the live CBCO in the in vitro preparation. CBCO afferent responses provided proprioceptive feedback to affect the thoracic motor output. Experiments performed with this hybrid neuromechanical preparation were simulated with a neuromechanical model in which a computational circuit model represented the relevant thoracic circuitry. Model simulations were able to reproduce the hybrid neuromechanical experimental results to show that proposed circuit mechanisms with sensory feedback could account for resistance reflexes displayed in the quiescent state and for reflex reversal and spontaneous Lev/Dep bursting seen in the active state.

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Ge, Yimeng, Shuan Ye, Kaihua Zhu, Tianruo Guo, Diansan Su, Dingguo Zhang, Yao Chen, Xinyu Chai e Xiaohong Sui. "Mediating different-diameter Aβ nerve fibers using a biomimetic 3D TENS computational model". Journal of Neuroscience Methods 346 (dezembro de 2020): 108891. http://dx.doi.org/10.1016/j.jneumeth.2020.108891.

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Teses / dissertações sobre o assunto "Computational nerve model"

Christopher, Mark Allen. "Computational methods to model disease and genetic effects on optic nerve head structure". Diss., University of Iowa, 2015. https://ir.uiowa.edu/etd/1959.

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Glaucoma is a leading cause of blindness throughout the world and is estimated to affect 80 million by 2020. This disease causes progressive loss of vision and, left untreated, can lead to complete blindness. With treatment, however, disease progression can be slowed dramatically. This makes early detection and intervention crucial in preserving the vision of affected individuals. Onset and progression of glaucoma are associated with structural changes to an anatomical feature known as the optic nerve head (ONH). The ONH is the site of attachment between the retina and the optic nerve that carries all visual information to the brain. As glaucoma progresses, characteristic changes related to cell death and loss of vision can be observed in the three-dimensional structure of the ONH. A common modality used to observe these changes is stereo fundus imaging. This modality captures three-dimensional information via stereo imaging and is commonly used in clinical settings to diagnose and monitor glaucoma. A limitation of using stereo fundus images is the need for review by glaucoma specialists to identify disease related features of ONH structure. Further, even when expert evaluation is possible, the subjective nature of the process can lead due large discrepancies in the evaluations and resultant clinical decisions. The work presented here seeks address these concerns by providing automated, computational tools that can be used to characterize ONH structure. Specifically, this thesis outlines the development of computational methods for inferring three-dimensional information from stereo fundus images and identifying objective, quantitative measurements of ONH structure. The resulting computational tools were applied to image and clinical data collected from a large cohort of individuals to identify hidden relationships between ONH structure, clinical measurements, and glaucoma. These tools were then applied to develop methods for estimating the impact of individual genetic factors on the ONH. Finally, using a longitudinal dataset collected over more than a decade, computational analysis was used to investigate how ONH structure changes over time in response to aging, other disease-related factors, and glaucoma progression.

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Brill, Natalie Amber. "Optimization of High Density Nerve Cuff Stimulation in Upper Extremity Nerves". Case Western Reserve University School of Graduate Studies / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=case1418147191.

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Kieselbach, Rebecca. "A numerically stable model for simulating high frequency conduction block in nerve fiber". Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/41233.

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Previous studies performed on myelinated nerve fibers have shown that a high frequency alternating current stimulus can block impulse conduction. The current threshold at which block occurs increases as the blocking frequency increases. Cable models based on the Hodgkin-Huxley model are consistent with these results. Recent experimental studies on unmyelinated nerve have shown that at higher frequencies, the block threshold decreases. When the block threshold is plotted as a function of frequency the resulting graph is distinctly nonmonotonic. Currently, all published models do not explain this behavior and the physiological mechanisms that create it are unknown. This difference in myelinated vs. unmyelinated block thresholds at high frequencies could have numerous clinical applications, such as chronic pain management. A large body of literature has shown that the specific capacitance of biological tissue decreases at frequencies in the kHz range or higher. Prior research has shown that introducing a frequency-dependent capacitance (FDC) to the Hodgkin-Huxley model will attenuate the block threshold at higher frequencies, but not to the extent that was seen in the experiments. This model was limited by the methods used to solve its higher order partial differential equation. The purpose of this thesis project is to develop a numerically stable method of incorporating the FDC into the model and to examine its effect on block threshold. The final, modified model will also be compared to the original model to ensure that the fundamental characteristics of action potential propagation remain unchanged.

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Peterson, Erik J. "INFRARED NEURAL STIMULATION AND FUNCTIONALRECRUITMENT OF THE PERIPHERAL NERVE". Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1363640552.

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Morse, Robert. "Studies of temporal coding for analogue cochlear implants using animal and computational models : benefits of noise". Thesis, Keele University, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242448.

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Denniss, Jonathan, A. M. McKendrick e A. Turpin. "An Anatomically Customizable Computational Model Relating the Visual Field to the Optic Nerve Head in Individual Eyes". 2012. http://hdl.handle.net/10454/16269.

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No
To present a computational model mapping visual field (VF) locations to optic nerve head (ONH) sectors accounting for individual ocular anatomy, and to describe the effects of anatomical variability on maps produced. A previous model that related retinal locations to ONH sectors was adapted to model eyes with varying axial length, ONH position and ONH dimensions. Maps (n = 11,550) relating VF locations (24-2 pattern, n = 52 non–blind-spot locations) to 1° ONH sectors were generated for a range of clinically plausible anatomical parameters. Infrequently mapped ONH sectors (5%) were discarded for all locations. The influence of anatomical variables on the maps was explored by multiple linear regression. Across all anatomical variants, for individual VF locations (24-2), total number of mapped 1° ONH sectors ranged from 12 to 90. Forty-one locations varied more than 30°. In five nasal-step locations, mapped ONH sectors were bimodally distributed, mapping to vertically opposite ONH sectors depending on vertical ONH position. Mapped ONH sectors were significantly influenced (P < 0.0002) by axial length, ONH position, and ONH dimensions for 39, 52, and 30 VF locations, respectively. On average across all VF locations, vertical ONH position explained the most variance in mapped ONH sector, followed by horizontal ONH position, axial length, and ONH dimensions. Relations between ONH sectors and many VF locations are strongly anatomy-dependent. Our model may be used to produce customized maps from VF locations to the ONH in individual eyes where some simple biometric parameters are known.
ustralian Research Council Linkage Project LP100100250 (with Heidelberg Engineering GmbH, Germany); Australian Research Council Future Fellowship FT0990930 (AMM); Australian Research Council Future Fellowship FT0991326 (AT)

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Denniss, Jonathan, A. Turpin, F. Tanabe, C. Matsumoto e A. M. McKendrick. "Structure–Function Mapping: Variability and Conviction in Tracing Retinal Nerve Fiber Bundles and Comparison to a Computational Model". 2014. http://hdl.handle.net/10454/11088.

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yes
Purpose: We evaluated variability and conviction in tracing paths of retinal nerve fiber bundles (RNFBs) in retinal images, and compared traced paths to a computational model that produces anatomically-customized structure–function maps. Methods: Ten retinal images were overlaid with 24-2 visual field locations. Eight clinicians and 6 naïve observers traced RNFBs from each location to the optic nerve head (ONH), recording their best estimate and certain range of insertion. Three clinicians and 2 naïve observers traced RNFBs in 3 images, 3 times, 7 to 19 days apart. The model predicted 10° ONH sectors relating to each location. Variability and repeatability in best estimates, certain range width, and differences between best estimates and model-predictions were evaluated. Results: Median between-observer variability in best estimates was 27° (interquartile range [IQR] 20°–38°) for clinicians and 33° (IQR 22°–50°) for naïve observers. Median certain range width was 30° (IQR 14°–45°) for clinicians and 75° (IQR 45°–180°) for naïve observers. Median repeatability was 10° (IQR 5°–20°) for clinicians and 15° (IQR 10°–29°) for naïve observers. All measures were worse further from the ONH. Systematic differences between model predictions and best estimates were negligible; median absolute differences were 17° (IQR 9°–30°) for clinicians and 20° (IQR 10°–36°) for naïve observers. Larger departures from the model coincided with greater variability in tracing. Conclusions: Concordance between the model and RNFB tracing was good, and greatest where tracing variability was lowest. When RNFB tracing is used for structure–function mapping, variability should be considered.

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Smit, Jacoba Elizabeth. "Modelled response of the electrically stimulated human auditory nerve fibre". Thesis, 2008. http://upetd.up.ac.za/thesis/available/etd-09182008-144232/.

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"Biomechanical properties of the normal and early glaucomatous optic nerve head: An experimental and computational study using the monkey model". Tulane University, 2002.

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Glaucoma is a disease that affects over 1 million people in the United States, and is one of the three leading causes of blindness nationwide. Loss of vision from glaucoma is believed to be the result of damage to the axons of the retina ganglion cells as they pass through the lamina cribrosa, which spans the opening in the back of the eye wall called the scleral canal. It is known that elevations of intraocular pressure (IOP) are associated with this damage, but the exact mechanisms by which the level of IOP causes these changes are unknown and controversial The objective of this work is to investigate how the load-bearing connective tissues within and around the optic nerve head (ONH) respond to changes in intraocular pressure, and how these responses are altered when the tissues are damaged early in glaucoma. The connective tissues of the ONH provide support for the retinal ganglion cell axons as they pass through the wall of the eye, and are crucial in maintaining axonal health at the ONH. Since the ONH is the principal site of glaucomatous damage, understanding how these connective tissues respond to different loading conditions provide insight into the pathophysiology of the retinal axons in this disease Histologic measurements made in 4 mum serial sagittal sections show that acute increases in IOP can deform the load-bearing connective tissues of the ONH. This is true both when the IOP increase is from 0 to 10 mm Hg, and when the increase is from 10 to 30 or 45 mm Hg. Additionally, these measurements show that in eyes that have been given early experimental glaucoma, the magnitude of the deformations caused by a given IOP increase is larger than in normal eyes, and that these larger deformations have both a plastic and hyperelastic component. Using digitized three-dimensional reconstructions, these profound deformations can be visualized. Finally, using finite element modeling that incorporates these digital reconstructions, a better understanding was gained of how regional stresses and strains are related to these deformations, and how all of these factors are associated with the onset and progression of glaucoma
acase@tulane.edu

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Hsu, Wei-Chei, e 許偉傑. "Modeling Stochastic Auditory Nerves Behavior Based on Computational Neuroscience Model Using Artificial Neural Networks". Thesis, 2006. http://ndltd.ncl.edu.tw/handle/56857822916739155478.

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碩士
義守大學
電機工程學系碩士班
94
Neural response to electrical stimulation can be modeled by Generalized Schwarz Eikhof and Frijns (GSEF) equations. They are deterministic and computational intensive. On the other hand, real neural response to electrical stimulation is stochastic. This makes GSEF model unattractive for realistic neural engineering application. In order to model the stochastic behavior of an electrically stimulated nerve, an artificial neural network (ANN) is used to model the GSEF with stochastic response. Once the ANN is trained, the neural response is readily available without the computation delay similar to those of the GSEF models.

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Livros sobre o assunto "Computational nerve model"

Trappenberg, Thomas P. Fundamentals of computational neuroscience. 2^a ed. Oxford: Oxford University Press, 2010.

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Trappenberg, Thomas P. Fundamentals of computational neuroscience. 2^a ed. Oxford: Oxford University Press, 2010.

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Fundamentals of computational neuroscience. 2^a ed. Oxford: Oxford University Press, 2010.

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Hippocampal microcircuits: A computational modeler's resource book. New York: Springer, 2010.

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Cox, Steven J. (Steven James), 1960- e ScienceDirect (Online service), eds. Mathematics for neuroscientists. Amsterdam: Elsevier Academic Press, 2010.

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Brain dynamics. 2^a ed. New York: Springer, 2008.

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Haken, H. Brain dynamics. 2^a ed. New York: Springer, 2008.

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Brain dynamics: Synchronization and activity patterns in pulse-coupled neural nets with delays and noise. Berlin: Springer, 2002.

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Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can be encoded in the voltage across the membrane, in the intracellular calcium concentration, or in the timing of individual spikes. Key topics covered include the linear cable equation; cable theory as applied to passive dendritic trees and dendritic spines; chemical and electrical synapses and how to treat them from a computational point of view; nonlinear interactions of synaptic input in passive and active dendritic trees; the Hodgkin-Huxley model of action potential generation and propagation; phase space analysis; linking stochastic ionic channels to membrane-dependent currents; calcium and potassium currents and their role in information processing; the role of diffusion, buffering and binding of calcium, and other messenger systems in information processing and storage; short- and long-term models of synaptic plasticity; simplified models of single cells; stochastic aspects of neuronal firing; the nature of the neuronal code; and unconventional models of sub-cellular computation. Biophysics of Computation: Information Processing in Single Neurons serves as an ideal text for advanced undergraduate and graduate courses in cellular biophysics, computational neuroscience, and neural networks, and will appeal to students and professionals in neuroscience, electrical and computer engineering, and physics.

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Trappenberg, Thomas. Fundamentals of Computational Neuroscience. Oxford University Press, USA, 2002.

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Capítulos de livros sobre o assunto "Computational nerve model"

Schiefer, Matthew. "Peripheral Nerve Models". In Encyclopedia of Computational Neuroscience, 1–7. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_213-3.

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Schiefer, Matthew. "Peripheral Nerve Models". In Encyclopedia of Computational Neuroscience, 2302–7. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_213.

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Medina, Leonel E., e Warren M. Grill. "Mammalian Motor Nerve Fibers, Models of". In Encyclopedia of Computational Neuroscience, 1–4. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4614-7320-6_369-2.

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Medina, Leonel E., e Warren M. Grill. "Mammalian Motor Nerve Fibers, Models of". In Encyclopedia of Computational Neuroscience, 1645–48. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_369.

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Meddis, Ray, e Enrique A. Lopez-Poveda. "Auditory Periphery: From Pinna to Auditory Nerve". In Computational Models of the Auditory System, 7–38. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-5934-8_2.

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Wang, Jing, Michael I. Miller e Andrew T. Ogielski. "A Stochastic Model Of Synaptic Transmission and Auditory Nerve Discharge (Part I)". In Computation in Neurons and Neural Systems, 147–52. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_24.

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Wang, Jing, Michael I. Miller e Andrew T. Ogielski. "A Stochastic Model Of Synaptic Transmission and Auditory Nerve Discharge (Part II)". In Computation in Neurons and Neural Systems, 153–58. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4615-2714-5_25.

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"Nerve Fiber Model(s)". In Encyclopedia of Computational Neuroscience, 1849. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4614-6675-8_100379.

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Penrose, Roger, e Martin Gardner. "Real Brains and Model Brains". In The Emperor's New Mind. Oxford University Press, 1989. http://dx.doi.org/10.1093/oso/9780198519737.003.0017.

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Inside our heads is a magnificent structure that controls our actions and somehow evokes an awareness of the world around. Yet, as Alan Turing once put it, it resembles nothing so much as a bowl of cold porridge! It is hard to see how an object of such unpromising appearance can achieve the miracles that we know it to be capable of. Closer examination, however, begins to reveal the brain as having a much more intricate structure and sophisticated organization. The large convoluted (and most porridge-like) portion on top is referred to as the cerebrum. It is divided cleanly down the middle into left and right cerebral hemispheres, and considerably less cleanly front and back into the frontal lobe and three other lobes: the parietal, temporal and occipital. Further down, and at the back lies a rather smaller, somewhat spherical portion of the brain - perhaps resembling two balls of wool - the cerebellum. Deep inside, and somewhat hidden under the cerebrum, lie a number of curious and complicated-looking different structures: the pons and medulla (including the reticular formation, a region that will concern us later) which constitute the brain-stem, the thalamus, hypothalamus, hippocampus, corpus callosum, and many other strange and oddly named constructions. The part that human beings feel that they should be proudest of is the cerebrum - for that is not only the largest part of the human brain, but it is also larger, in its proportion of the brain as a whole, in man than in other animals. (The cerebellum is also larger in man than in most other animals.) The cerebrum and cerebellum have comparatively thin outer surface layers of grey matter and larger inner regions of white matter. These regions of grey matter are referred to as, respectively, the cerebral cortex and the cerebellar cortex. The grey matter is where various kinds of computational task appear to be performed, while the white matter consists of long nerve fibres carrying signals from one part of the brain to another. Various parts of the cerebral cortex are associated with very specific functions.

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"Models and Methods for Investigation of the Human Motor Nerve Fibre". In Computational Neuroscience, 18–32. CRC Press, 2013. http://dx.doi.org/10.1201/b14589-3.

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Trabalhos de conferências sobre o assunto "Computational nerve model"

Fritz, Nicholas, Daniel Gulick e Jennifer M. Blain Christen. "Computational Model of Optogenetic Stimulation in a Peripheral Nerve". In 2018 IEEE Life Sciences Conference (LSC). IEEE, 2018. http://dx.doi.org/10.1109/lsc.2018.8572187.

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Lin, Qihang, Mohit N. Shivdasani, David Tsai, Yao-Chuan Chang, Naveen Jayaprakash, Stavros Zanos, Nigel H. Lovell, Socrates Dokos e Tianruo Guo. "A Computational Model of Functionally-distinct Cervical Vagus Nerve Fibers". In 2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) in conjunction with the 43rd Annual Conference of the Canadian Medical and Biological Engineering Society. IEEE, 2020. http://dx.doi.org/10.1109/embc44109.2020.9175855.

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Baruah, Satyabrat Malla Bujar, Plabita Gogoi e Soumik Roy. "From Cable Equation to Active and Passive Nerve Membrane Model". In 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP). IEEE, 2019. http://dx.doi.org/10.1109/icaccp.2019.8883011.

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Ciotti, Federico, Giacomo Valle, Alessandra Pedrocchi e Stanisa Raspopovic. "A Computational Model of the Pudendal Nerve for the Bioelectronic Treatment of Sexual Dysfunctions". In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2021. http://dx.doi.org/10.1109/ner49283.2021.9441309.

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Deka, Rashmi, e Jiten Ch Dutta. "Parameter Extraction for Neuron Model Simulation of Action Potential in Earthworm Giant Nerve Fiber". In 2015 IEEE International Conference on Computational Intelligence & Communication Technology (CICT). IEEE, 2015. http://dx.doi.org/10.1109/cict.2015.57.

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Williams, Megan, Julie Barkmeier-Kraemer, Urs Utzinger e Jonathan Vande Geest. "Biomechanical and Microstructural Response of Recurrent Laryngeal Nerve in Pigs". In ASME 2013 Summer Bioengineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/sbc2013-14618.

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Tensile loading is a common physiological condition of peripheral nerves but can induce pathologic effects. Significant defects in nerve conduction have been reported for strains as low as ∼6% greater than the in situ strain [1]. In order to better understand the functional deficits resulting from tensile loading of nerve tissue, biomechanical testing is performed. The long-term goal of this research is to develop a constitutive and a computational model of the biomechanical properties of the “packaging,” or connective tissues of the recurrent laryngeal nerve (RLN) to investigate their role in the onset of unilateral vocal fold paralysis (UVP). The vocal folds are important for protection of the airway during swallowing, the regulation of breathing, and for voice production. Although surgery is most often linked to onset of UVP, the cause remains unknown in a large percentage of those with this disorder. Recent research has suggested that individuals with idiopathic UVP may have damage to the RLN at the level of the aortic arch related to a thoracic aneurysm. Our preliminary work has resulted in the conclusion that connective tissues of the RLN exhibit different biomechanical properties in the region of the aortic arch [2]. An aneurysm would impose increased stress and strain on the RLN where it is adjacent to the aorta resulting in impaired nerve function. The primary goal of this study is to identify the relationship between the biomechanical response of RLN tissue and how it response is governed by load dependent underlying extracellular matrix (collagen) organization. We hypothesize that regional differences exist in the microstructure and/or biomechanical response of the RLN and that these differences play a role in the onset of idiopathic UVP.

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Waldman, Lewis, Crystal Cunanan, Sanjay Asrani, Roy Kerckhoffs e Andrew McCulloch. "Computational Mechanics of the Sclera and Optic Nerve Head (ONH): Effects of ONH Size and Pressure Range". In ASME 2008 3rd Frontiers in Biomedical Devices Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/biomed2008-38051.

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Computational modeling was performed to study how loss of compliance of the eye and abnormally high pressures result in changes in stresses and strains that may impact the optic nerve in diseases such as glaucoma. Hemispherical finite element models of the eye were created in which scleral thickness varied from the equatorial region to the optic nerve head (ONH). Nonhomogeneous material properties were used to model the ONH as a continuous region softer than the adjacent sclera. The ONH and an adjacent buffer zone in the sclera were modeled with enough detail that the size of the ONH could be changed to account for variations observed in humans. The model was provided with appropriate dimensions typical of patients and nonlinear material properties with decreased compliance. Models with different ONH sizes were inflated in small steps to 55 mmHg (7.33 kPa), providing deformed configurations at intermediate pressures of 15, 30 and 45 mmHg, respectively. Color-coded maps of stress and strain components were rendered directly on deformed configurations of the eye model; and animations were produced that show both spatial and temporal variations of stresses and strains as internal pressure increases. Three-dimensional stresses and accompanying finite strains were similar for ONH sizes ranging form 1.5 to 2.5 mm in diameter. Stress and strain differences were estimated as pressure was increased from 15 to 25 mmHg, 30 to 40 mmHg, and 45 to 55 mmHg. Substantial changes occurred in stress and strain differences as the pressure range was varied with large changes occurring in the lowest pressure range for strain components and moderate increases in stress differences as pressures increase.

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Tong, Junfei, e Linxia Gu. "The Influence of Primary Blast Wave on the Posterior Part of the Eyeball". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-88113.

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With the increasing application of improvised explosive devices, the ratio of traumatic ocular injury significantly increased in the past decades, which has become the fourth most happened injury to military deployment. The ocular injury treatment is costly and has been less effective, which influences the military service and life experience of the soldiers. With years of research on the traumatic ocular injury through experiment or computational simulations, the primary blast wave related overpressure was found to induce macular damage, globe rupture. While the influence of the primary blast wave on the posterior part of the eyeball was poorly understood, such as the optic nerve. In this work, we developed a three-dimensional computation model, which included lamina cribrosa (LC), optic nerve and cerebrospinal fluid (CSF). The strain evaluated in optic nerve was found to exceed neural tissue’s physiological loading range, which might explain the vision loss after the blast.

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Kavan, Loabat S., Abhijeet Wadkar e Samuel F. Asokanthan. "Computational Study of Onset Dynamics in Neuron-Spiking With Threshold Adaptation". In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86689.

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Magnetic Seizure therapy (MST) is emerging as a treatment for patients suffering from severe depression where an induced current due to an external electromagnetic field is employed. This procedure can only be considered effective when sufficient induced current activates the neurons in the prefrontal cortex. Computer simulation of MST is essential to provide better insight of this procedure and to supplement the clinical trials. To this end, an understanding of transmission of electric impulse through the nerve is considered essential. Stochastic impulse spike sequences are trigged when membrane potential crosses a threshold value. Quantitative numerical predictions employing a mathematical model and induced current defined via Ornstein Uhlenbeck (OU) process predict that both the linear steady-state and rectified models provide adequate threshold adaptation while the rectified model exhibits superior spiking behavior. The present study when combined with suitable numerical simulation of electromagnetic induction is envisaged to aid the MST clinical treatment.

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Tong, Junfei, Deepta Ghate, Sachin Kedar e Linxia Gu. "Image-Based Modeling of Optic Nerve Head Mechanics Following Lumbar Puncture". In 2017 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dmd2017-3531.

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Biomechanics of optic nerve head (ONH) has attracted increasing attention in recent years due to its association with ganglion cell damage and tissue remodeling resulted vision impairments [1, 2]. The ONH is exposed to both intraocular pressure (IOP) and intracranial pressure (ICP), separated by the lamina cribrosa (LC) which is regarded as the primary site of axonal injury in glaucoma[3]. The elevated IOP was widely acknowledged as a major risk factor for glaucoma. However, a large number of glaucoma patients never have an increase in IOP [4]. In studies that have looked at lumbar puncture (LP) data, patients with open-angle glaucoma were found to have lower ICPs than non-glaucomatous controls[5]. It suggests that higher translaminar pressure difference across the LC rather than IOP alone may have an important role in the pathogenesis of ONH damage. There were few computational models had been established to investigate the ICP’s role on ONH, such as Ethier et al. found elevated ICP could induce decreased strain within LC using finite element model[6]. However, less experimental data are available for delineating the role of ICP on the behaviors of LC. In this work, we present one dataset from LP patients and reconstruct its two-dimensional computational model of the ONH based on the patient’s images to delineate the role of ICP on ONH mechanics. The changes of LC depth, BMO width and papillary height were compared between the simulation and clinical dataset. The maximum principal strain of LC was calculated to reinforce its link with mechanosensitive cells in ONH.

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