Relevant bibliographies by topics / Neural conduction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Neural conduction'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2024

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Journal articles on the topic "Neural conduction"

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Klimkin, A. V., V. B. Voitenkov, and N. V. Skripchenko. "NEURAL CONDUCTION AT TOURNIQUET TEST." Marine Medicine 3, no. 1 (January 1, 2017): 25–33. http://dx.doi.org/10.22328/2413-5747-2017-3-1-25-33.

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Parris, W. C. V., K. E. Misulis, J. R. Kambam, J. J. Franks, and W. D. Dettbarn. "VERAPAMIL POTENTIATES BUPIVACAINE-INDUCED NEURAL CONDUCTION BLOCKADE." Anesthesiology 67, no. 3 (September 1, 1987): A250. http://dx.doi.org/10.1097/00000542-198709001-00250.

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Mackel, R., and E. Brink. "Conduction of neural impulses in diabetic neuropathy." Clinical Neurophysiology 114, no. 2 (February 2003): 248–55. http://dx.doi.org/10.1016/s1388-2457(02)00320-6.

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Gorza, Luisa, Stefano Schiaffino, and Maurizio Vitadello. "Heart conduction system: a neural crest derivative?" Brain Research 457, no. 2 (August 1988): 360–66. http://dx.doi.org/10.1016/0006-8993(88)90707-x.

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Keneskyzy, K., and S. B. Yeskermes. "Метод машинного обучения для обратных задач теплопроводности." INTERNATIONAL JOURNAL OF INFORMATION AND COMMUNICATION TECHNOLOGIES 2, no. 1(5) (March 26, 2021): 59–64. http://dx.doi.org/10.54309/ijict.2021.05.1.008.

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Investigated in this work is the potential of carrying out inverse problems with linear and non-linear behavior using machine learning methods and the neural network method. With the advent of ma-chine learning algorithms it is now possible to model inverse problems faster and more accurately. In order to demonstrate the use of machine learning and neural networks in solving inverse problems, we propose a fusion between computational mechanics and machine learning. The forward problems are solved first to create a database. This database is then used to train the machine learning and neural network algorithms. The trained algorithm is then used to determine the boundary conditions of a problem from assumed meas-urements. The proposed method is tested for the linear/non-linear heat conduction problems in which the boundary conditions are determined by providing three, four, and five temperature measurements. This re-search demonstrates that the proposed fusion of computational mechanics and machine learning is an effec-tive way of tackling complex inverse problems.
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Fukuoka, Yuko, Hiromichi Komori, Shigenori Kawabata, Harunobu Ohkubo, Kenichi Shinomiya, and Omi Terasaki. "Imaging of neural conduction block by neuromagnetic recording." Clinical Neurophysiology 113, no. 12 (December 2002): 1985–92. http://dx.doi.org/10.1016/s1388-2457(02)00345-0.

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Bijl, G. K., and F. Veringa. "Neural conduction time and steady-state evoked potentials." Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 62, no. 6 (November 1985): 465–67. http://dx.doi.org/10.1016/0168-5597(85)90059-0.

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Chole, Richard A., Timothy E. Hullar, and Lisa G. Potts. "Conductive Component After Cochlear Implantation in Patients With Residual Hearing Conservation." American Journal of Audiology 23, no. 4 (December 2014): 359–64. http://dx.doi.org/10.1044/2014_aja-14-0018.

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Purpose Changes in auditory thresholds following cochlear implantation are generally assumed to be due to damage to neural elements. Theoretical studies have suggested that placement of a cochlear implant can cause a conductive hearing loss. Identification of a conductive component following cochlear implantation could guide improvements in surgical techniques or device designs. The purpose of this study is to characterize new-onset conductive hearing losses after cochlear implantation. Method In a prospective study, air- and bone-conduction audiometric testing were completed on cochlear implant recipients. An air–bone gap equal to or greater than 15 dB HL at 2 frequencies determined the presence of a conductive component. Results Of the 32 patients with preoperative bone-conduction hearing, 4 patients had a new-onset conductive component resulting in a mixed hearing loss, with air-conduction thresholds ranging from moderate to profound and an average air–bone gap of 30 dB HL. One had been implanted through the round window, 2 had an extended round window, and 1 had a separate cochleostomy. Conclusions Loss of residual hearing following cochlear implantation may be due in part to a conductive component. Identifying the mechanism for this conductive component may help minimize hearing loss. Postoperative hearing evaluation should measure both air- and bone-conduction thresholds.
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Chen, Ley, S. Askarian, M. Mohammadzaheri, and F. Samadi. "Simulation and Experimental Study of Inverse Heat Conduction Problem." Advanced Materials Research 233-235 (May 2011): 2820–23. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2820.

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In this paper, a neural network method is proposed to solve a one dimensional inverse heat conduction problem (IHCP). The method relies on input/output data of an unknown system to create an intelligent neural network model. Multi layer perceptrons with recurrent properties are utilised in the model. Prepared input/output data are used to train the neural network. Reliable checking processes are also offered to justify the robustness of the method. A numerical sequential function specification (SFS) method is used as another technique to solve the IHCP. The numerical result is compared with that of the proposed method and good agreement is shown between the two methods. However, the numerical method can be only used to solve the IHCP off-line due to the high computation requirement. The proposed neural network method can be used in real-time situations as shown in the experimental tests.
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Pitta, Izabela Jardim Rodrigues, Bruno de Siqueira Mietto, Aron dos Santos Camilo, Sergio Luiz Gomes Antunes, Euzenir Nunes Sarno, and Marcia Rodrigues Jardim. "Clinical, Neurophysiological and Histopatological correlations in Pure Neural Leprosy." Brazilian Journal of Case Reports 1, no. 4 (November 5, 2021): 105–13. http://dx.doi.org/10.52600/2763-583x.bjcr.2021.1.4.105-113.

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Although neuropathy remains one of the most problematic issues faced by leprosy patients, the evolving process of its findings continues a challenge particularly in pure neural leprosy (PNL). We evaluated neurological examination, nerve conduction studies and histhopathological data of patients with PNL and ulnar neuropathy. Patients with longer duration of symptoms had reduction in the motor conduction velocities and patients with fibrosis in the biopsy had axonal damage in the nerve conduction studies. This suggests that focal demyelination may be present in leprosy patients at the moment of the diagnosis and be related to the duration of the neuropathy.
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Dissertations / Theses on the topic "Neural conduction"

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Chik, Tai-wai David. "A numerical study of Hodgkin-Huxley neurons." Hong Kong : University of Hong Kong, 2000. http://sunzi.lib.hku.hk/hkuto/record.jsp?B22424726.

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Nettleton, Jilda Suzanne. "Summation of AMPA-mediated EPSPs in rat neocortical pyramidal neurons /." Thesis, Connect to this title online; UW restricted, 1998. http://hdl.handle.net/1773/10531.

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Ma, Pei. "OPTICAL IMAGING OF EMBRYONIC CARDIAC CONDUCTION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1464714110.

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戚大衛 and Tai-wai David Chik. "A numerical study of Hodgkin-Huxley neurons." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2000. http://hub.hku.hk/bib/B31224210.

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Tanaka, Hisataka. "Conduction properties of identified neural pathways in the central nervous system of mice in vivo." Kyoto University, 2004. http://hdl.handle.net/2433/145280.

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Kyoto University (京都大学)
0048
新制・課程博士
博士(医学)
甲第11099号
医博第2779号
新制||医||877(附属図書館)
22649
UT51-2004-L896
京都大学大学院医学研究科脳統御医科学系専攻
(主査)教授 大森 治紀, 教授 金子 武嗣, 教授 橋本 信夫
学位規則第4条第1項該当
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Nunnally, Stephens W. "A study of electrical aspects of neural conduction with an emphasis on the phenomenon of pain /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1992. http://handle.dtic.mil/100.2/ADA260385.

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Nunnally, Stephens W. Jr. "A study of electrical aspects of neural conduction with an emphasis on the phenomenon of pain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/38510.

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Approved for public release; distribution is unlimited.
A study of the electrical nature of neural communication is performed. The study provides a background for the evaluation of an electrical mechanism, set forth by the author, as a possible explanation for the effect of acupuncture. The electrical and biological processes involved in the conduction of the nervous impulse, as well as the limits of understanding of those processes, are studied. The role of the arrangement of individual neurons into the human nervous system is examined. Also, the phenomena of pain and referred pain are investigated.
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Smart, Sharon Louise. "Involvement of shaker-like potassium channels in control of nervous system hyperexcitability /." Thesis, Connect to this title online; UW restricted, 1996. http://hdl.handle.net/1773/6265.

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Reid, Michael Steven. "The Role of Heterogeneity in Rhythmic Networks of Neurons." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14565.

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Engineers often view variability as undesirable and seek to minimize it, such as when they employ transistor-matching techniques to improve circuit and system performance. Biology, however, makes no discernible attempt to avoid this variability, which is particularly evident in biological nervous systems whose neurons exhibit marked variability in their cellular properties. In previous studies, this heterogeneity has been shown to have mixed consequences on network rhythmicity, which is essential to locomotion and other oscillatory neural behaviors. The systems that produce and control these stereotyped movements have been optimized to be energy efficient and dependable, and one particularly well-studied rhythmic network is the central pattern generator (CPG), which is capable of generating a coordinated, rhythmic pattern of motor activity in the absence of phasic sensory input. Because they are ubiquitous in biological preparations and reveal a variety of physiological behaviors, these networks provide a platform for studying a critical set of biological control paradigms and inspire research into engineered systems that exploit these underlying principles. We are directing our efforts toward the implementation of applicable technologies and modeling to better understand the combination of these two concepts---the role of heterogeneity in rhythmic networks of neurons. The central engineering theme of our work is to use digital and analog platforms to design and build Hodgkin--Huxley conductance-based neuron models that will be used to implement a half-center oscillator (HCO) model of a CPG. The primary scientific question that we will address is to what extent this heterogeneity affects the rhythmicity of a network of neurons. To do so, we will first analyze the locations, continuities, and sizes of bursting regions using single-neuron models and will then use an FPGA model neuron to study parametric and topological heterogeneity in a fully-connected 36-neuron HCO. We found that heterogeneity can lead to more robust rhythmic networks of neurons, but the type and quantity of heterogeneity and the population-level metric that is used to analyze bursting are critical in determining when this occurs.
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Murnane, Owen D., Faith W. Akin, J. Kip Kelly, and Stephanie Byrd. "Effects of Stimulus and Recording Parameters on the Air Conduction Ocular Vestibular Evoked Myogenic Potential." Digital Commons @ East Tennessee State University, 2011. https://dc.etsu.edu/etsu-works/1783.

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Background: Vestibular evoked myogenic potentials (VEMPs) have been recorded from the sternocleidomastoid muscle (cervical VEMP or cVEMP) and more recently from the eye muscles (ocular VEMP or oVEMP) in response to air conduction and bone conduction stimuli. Both cVEMPs and oVEMPs are mediated by the otoliths and thereby provide diagnostic information that is complementary to videonystagmography and rotational chair tests. In contrast to the air conduction cVEMP, which originates from the saccule/inferior vestibular nerve, recent evidence suggests the possibility that the air conduction oVEMP may be mediated by the utricle/superior vestibular nerve. The oVEMP, therefore, may provide complementary diagnostic information relative to the cVEMP. There are relatively few studies, however, that have quantified the effects of stimulus and recording parameters on the air conduction oVEMP, and there is a paucity of normative data. Purpose: To evaluate the effects of several stimulus and recording parameters on the air conduction oVEMP and to establish normative data for clinical use. Research Design: A prospective repeated measures design was utilized. Study Sample: Forty-seven young adults with no history of neurologic disease, hearing loss, middle ear pathology, open or closed head injury, cervical injury, or audiovestibular disorder participated in the study. Data Collection and Analysis: The effects of stimulus frequency, stimulus level, gaze elevation, and recording electrode location on the amplitude and latency of the oVEMP for monaural air conduction stimuli were assessed using repeated measures analyses of variance in an initial group of 17 participants. The optimal stimulus and recording parameters obtained in the initial group were used subsequently to obtain oVEMPs from 30 additional participants. Results: The effects of stimulus frequency, stimulus level, gaze elevation, and electrode location on the response prevalence, amplitude, and latency of the oVEMP for monaural air conduction stimuli were significant. The maximum N1-P1 amplitude and response prevalence were obtained for contralateral oVEMPs using a 500 Hz tone burst presented at 125 dB peak SPL during upward gaze at an elevation of 30°. Conclusions: The optimal stimulus and recording parameters quantified in this study were used to establish normative data that may be useful for the clinical application of the air conduction oVEMP.
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Books on the topic "Neural conduction"

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P, Schenk Michael, ed. Atlas of nerve conduction studies and electromyography. New York: Oxford University Press, 2012.

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K, Silver Julie, and Weiss Jay, eds. Easy EMG: A guide to performing nerve conduction studies and electromyography. Edinburgh: Butterworth-Heinemann, 2004.

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Films for the Humanities (Firm). The nature of the nerve impulse. Princeton, N.J: Films for the Humanities, 1986.

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Clinical electromyography: Nerve conduction studies. 2nd ed. Baltimore: Williams & Wilkins, 1993.

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Ellen, Shapiro Barbara, ed. Electromyography and neuromuscular disorders: Clinical-electrophysiologic correlations. Boston: Butterworth-Heinemann, 1998.

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Raimbault, Jean. Les conductions nerveuses chez l'enfant normal: Étude et évolution. Paris: Expansion scientifique française, 1988.

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K, Kaczmarek Leonard, and Levitan Irwin B, eds. Neuromodulation: The biochemical control of neuronal excitability. New York: Oxford University Press, 1987.

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Cronin, Jane. Mathematical aspects of Hodgkin-Huxley neural theory. Cambridge [Cambridgeshire]: Cambridge University Press, 1987.

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A, DeLisa Joel, and DeLisa Joel A, eds. Manual of nerve conduction velocity and clinical neurophysiology. 3rd ed. New York: Raven Press, 1994.

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1950-, Mackenzie Keith, Baran Ernest M, and DeLisa Joel A, eds. Manual of nerve conduction velocity and somatosensory evoked potentials. 2nd ed. New York: Raven Press, 1987.

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Book chapters on the topic "Neural conduction"

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Mallot, Hanspeter A. "Excitable Membranes and Neural Conduction." In Computational Neuroscience, 1–21. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00861-5_1.

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Li, Xiqing, and Kairu Li. "A Preliminary Tactile Conduction Model Based on Neural Electrical Properties Analysis." In Intelligent Robotics and Applications, 796–807. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13822-5_71.

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Halliday, David M. "Temporal Coding in Neuronal Populations in the Presence of Axonal and Dendritic Conduction Time Delays." In Emergent Neural Computational Architectures Based on Neuroscience, 285–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44597-8_21.

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Chen, Zhou, Huihan Li, Yanwen Sun, and Chaozhi Huang. "Conduction Angle Adaptive Control Base on NSGA-II Optimize BP Neural Network." In Lecture Notes in Electrical Engineering, 428–35. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-1447-6_44.

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Schirner, Michael, and Petra Ritter. "Integrating EEG–fMRI Through Brain Simulation." In EEG - fMRI, 745–77. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-07121-8_30.

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AbstractEEG and fMRI are thought to measure partly distinct, partly overlapping, and certainly incomplete aspects of neuronal activity. Brain network models (BNMs) are used to simulate neuronal activity, like the dynamics of postsynaptic potentials, or spike-firing activity, and may conjointly predict both, EEG and fMRI, and therefore allow for the integration and the analysis of the two signals. The usual motivation for EEG–fMRI integration is to use both techniques in a complementary fashion by combining their strengths, while ameliorating their weaknesses. For instance, EEG measures electric activity on the scalp with a high temporal sampling rate, but a low spatial resolution (e.g., due to volume conduction effects). On the other hand, fMRI BOLD contrast is an indirect (proxy) measure of neural activity that is sensitive for the fluctuation of blood oxygenation at a relatively low temporal resolution. Some of the appeal of brain simulation-based integration of EEG–fMRI data is related to the idea that after fitting a neural model to reproduce observed activity, the internal activity of the model can tell us something about unobservable activity, like neural firing, which can only be measured invasively and in a spatially restricted manner. Brain simulation-based approaches have the potential to not only integrate EEG and fMRI, but basically data from every modality that can either directly (like multi-electrode recordings) or indirectly (like fMRI) be linked with the neural model.
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Gittenberger-de Groot, Adriana C., Nico M. Blom, Naoyoshi Aoyama, Henri Sucov, Arnold C. G. Wenink, and Robert E. Poelmann. "The Role of Neural Crest and Epicardium-Derived Cells in Conduction System Formation." In Novartis Foundation Symposia, 125–41. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470868066.ch8.

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Gittenberger-de Groot, Adriana C., Nico A. Blom, Glenn I. Fishman, and Robert E. Poelmann. "Development of the Cardiac Conduction System and Contribution of Neural Crest and Epicardially Derived Cells." In Cardiovascular Development and Congenital Malformations, 98–100. Malden, Massachusetts, USA: Blackwell Publishing Ltd, 2007. http://dx.doi.org/10.1002/9780470988664.ch24.

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Zhang, Qichun, and Francisco Sepulveda. "Modelling and Control Design for Membrane Potential Conduction Along Nerve Fibre Using B-spline Neural Network." In Communications in Computer and Information Science, 53–62. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6370-1_6.

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Acharya, Swagatika, Vijay Kumar Mishra, Jitendra Kumar Patel, Gaurav Gupta, Mrityunjay K. Sah, and Pinky Shah. "Retrieval of Parameter in Combined Mode Conduction–Radiation Problem in Porous Ceramic Matrix by Artificial Neural Network." In Recent Advances in Thermofluids and Manufacturing Engineering, 277–88. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4388-1_25.

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Allahyari, Zahra, Shayan Gholizadeh, Hossein Derakhshankhah, Katayoun Derakhshandeh, Seyed Mohammad Amini, and Hadi Samadian. "Conducting Polymers for Neural Tissue Engineering." In Conducting Polymers, 179–92. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003205418-13.

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Conference papers on the topic "Neural conduction"

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Joseph, Laveeta, and Robert J. Butera. "Conduction Block in Unmyelinated Nerves Using High Frequency AC Stimulation." In 2007 3rd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2007. http://dx.doi.org/10.1109/cne.2007.369738.

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Arnold, Joshua, Peter Stratton, and Janet Wiles. "Conduction delay plasticity can robustly learn spatiotemporal patterns embedded in noise." In 2021 International Joint Conference on Neural Networks (IJCNN). IEEE, 2021. http://dx.doi.org/10.1109/ijcnn52387.2021.9533934.

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Bhadra, N., and K. L. Kilgore. "Block of Mammalian Motor Nerve Conduction Using High Frequency Alternating Current." In 2005 2nd International IEEE/EMBS Conference on Neural Engineering. IEEE, 2005. http://dx.doi.org/10.1109/cne.2005.1419664.

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Matsubara, Takashi. "Spike timing-dependent conduction delay learning model classifying spatio-temporal spike patterns." In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE, 2017. http://dx.doi.org/10.1109/ijcnn.2017.7966073.

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Mikki, Fabio Tokio, Edison Issamoto, Jefferson I. da Luz, Pedro Paulo Balbi de Oliveira, Haroldo F. Campos-Velho, and Jose Demisio Simoes da Silva. "A Neural Network Approach In A Backward Heat Conduction Problem." In 4. Congresso Brasileiro de Redes Neurais. CNRN, 2016. http://dx.doi.org/10.21528/cbrn1999-008.

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Ntagiantas, "Konstantinos, Dimitrios Panagopoulos, Wing M. Poon, Danya Agha-Jaffar, Nicholas S. Peters, Chris D. Cantwell, Anil A. Bharath, and Rasheda A. Chowdhury." "Electrogram-Based Estimation of Myocardial Conduction Using Deep Neural Networks." In 2022 Computing in Cardiology Conference. Computing in Cardiology, 2022. http://dx.doi.org/10.22489/cinc.2022.227.

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Jung, Bup Sung, Sun K. Kim, and Woo Il Lee. "Inverse Estimation of Surface Temperature in Nanoscale Using the Artificial Neural Network." In ASME 2005 Summer Heat Transfer Conference collocated with the ASME 2005 Pacific Rim Technical Conference and Exhibition on Integration and Packaging of MEMS, NEMS, and Electronic Systems. ASMEDC, 2005. http://dx.doi.org/10.1115/ht2005-72384.

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An inverse heat conduction problem (IHCP) for nanoscale structures was studied. The conduction phenomenon is modeled using the Boltzmann transfer equation. Phonon-mediated heat conduction in one dimension is considered. One boundary, where temperature observation takes place, is subjected to a known boundary condition and the other boundary is exposed to an unknown temperature. The artificial neural network (ANN) is employed to solve the described inverse problem. Sample results are presented and discussed.
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Mintch, Landan M., Ivette Muzquiz, M. Ryne Horn, Michael Carr, John H. Schild, and Ken Yoshida. "Reversible Conduction Block in Peripheral Mammalian Nerve Using Low Frequency Alternating Current*." In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2019. http://dx.doi.org/10.1109/ner.2019.8716901.

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Palar, Pramudita S., Muhamad A. Aziz, Lavi R. Zuhal, Poetro L. Sambegoro, and Duong V. Dung. "Using Physics-Informed Neural Networks to Solve Inverse Heat Conduction Problems." In AIAA SCITECH 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-0537.

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Peng, Chih-Wei, Shih-Ching Chen, Wen-Jia Fan, Chien-Hung Lai, and Jia-Jin Chen. "Influence of stimulus waveforms of high-frequency electrical current on nerve conduction block." In 2009 4th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2009. http://dx.doi.org/10.1109/ner.2009.5109237.

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Reports on the topic "Neural conduction"

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Hajj, Ramez, Marshall Thompson, Renan Santos Maia, Yuija Lu, Abhilash Vyas, Babak Asadi, and Bibek Regmi. Updates to Mechanistic-Empirical Design Inputs for Illinois Flexible Pavements. Illinois Center for Transportation, May 2024. http://dx.doi.org/10.36501/0197-9191/24-010.

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This study reviews the Illinois Department of Transportation’s full-depth asphalt, limiting strain criterion, and asphalt over rubblized concrete design procedures, considering technological advancements in hot-mix asphalt—namely, the increased use of recycled materials and modified asphalt binders. The researchers evaluated the current |E*| algorithm by conducting laboratory tests with four mix designs and seven asphalt binders of different Superpave performance grades. They compared predictive models, including the current Illinois modulus algorithm as well as the Witczak, Hirsch, and newly developed Illinois Center for Transportation (ICT) Bayesian neural network (BNN) models. The ultrasonic pulse velocity (UPV) nondestructive test for modulus and field modulus measurements was evaluated as well. Subsequent tasks involved reviewing current fatigue endurance limit (FEL) criteria from an adapted protocol for the four-point bending beam fatigue test. This study found that typical hot-mix asphalt modulus values in Illinois are underestimated compared to modern mixes, suggesting potential savings by allowing an increased modulus value to be used, reducing layer thickness design. Low-temperature performance grades significantly influenced modulus, and the developed ICT BNN predictive model outperformed traditional ones. While UPV was promising for modulus screening, challenges related to material properties assumptions were identified. The current stage of research on assessing modern mixes’ FEL reveals the complexity of observing FEL using simplified methods. To advance this objective, a concept of acceptable stiffness ratio (SR) is introduced. In these cases, exceptionally long fatigue life would be obtained for mixes that demonstrate an acceptable drop in SR within 10,000 cycles, with no apparent evolution of the damage state. The most conservative estimation of strain level that would result in acceptable SR aligned with IDOT’s current 70 microstrain FEL criteria. On the less conservative side, allowing a 10% drop in SR within 10,000 loading cycles could increase the acceptable SR < 1 strain level as high as approximately 180 microstrain, with caution needed regarding evolving mobility trends and environmental stressors including the changing climate.
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Performing motor and sensory neuronal conduction studies in adult humans. U.S. Department of Health and Human Services, Public Health Service, Centers for Disease Control, National Institute for Occupational Safety and Health, September 1990. http://dx.doi.org/10.26616/nioshpub90113.

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