Relevant bibliographies by topics / Neural development

Contents

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

Academic literature on the topic 'Neural development'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Neural development"

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Katz, Lawrence C., Monica Driscoll, Kathy Zimmermann, and Torsten N. Wiesel. "Neural development." Brain Research Reviews 17, no. 2 (May 1992): 171–81. http://dx.doi.org/10.1016/0165-0173(92)90013-c.

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Lillien, Laura. "Neural development: Instructions for neural diversity." Current Biology 7, no. 3 (March 1997): R168—R171. http://dx.doi.org/10.1016/s0960-9822(97)70080-0.

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Dang, Lan, and Vincent Tropepe. "Neural induction and neural stem cell development." Regenerative Medicine 1, no. 5 (September 2006): 635–52. http://dx.doi.org/10.2217/17460751.1.5.635.

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Clinton, Julian. "POPLOG-Neural ? a neural network development toolkit." Expert Systems 7, no. 3 (August 1990): 174. http://dx.doi.org/10.1111/j.1468-0394.1990.tb00226.x.

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Yashchenko, V. O. "Neural-like growing networks in the development of general intelligence. Neural-like element (P. I)." Mathematical machines and systems 4 (2022): 15–36. http://dx.doi.org/10.34121/1028-9763-2022-4-15-36.

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The article discusses a new approach to the creation of artificial neurons and neural networks as the means of developing artificial intelligence similar to natural. The article consists of two parts. In the first one, the system of artificial intelligence formation is considered in comparison with the system of natural intelligence formation. Based on the consideration and analysis of the structure and functions of a biological neuron, it was concluded that memory is stored in brain neurons at the molecular level. Information perceived by a person from the moment of his birth and throughout his life is stored in the endoplasmic reticulum of the neuron. There are about 100 billion neurons in the human brain, and each neuron contains millions of ribosomes that synthesize a mediator consisting of about 10,000 molecules. If we assume that one mole-cule corresponds to one unit of information, then human memory is unlimited. In the nerve cell, there is a synthesis of biologically active substances necessary for the analysis and memorizing information. The “factory” for the production of proteins is the endoplasmic reticulum which accumulates millions of ribosomes. One ribosome synthesizes protein at a rate of 15–20 amino acids per second. Considering that the functional structure of ribosomes is similar to the Turing machine, we can conclude that the neuron is an analog multimachine complex – an ultra-fast molecular multimachine supercomputer with an unusually simple analog programming device. An artificial neuron proposed by J. McCulloch and W. Pitts is considered a highly simplified mathematical model of a biological neuron. A maximally approximate analogue of a biological neuron, a neural-like element, is proposed. A description of the neural-like element is given. The process of perception and memorizing information in a neuron-like element is shown in comparison with a similar process in a nerve cell of the brain.
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Doe, Chris Q., and Joshua R. Sanes. "Development: Neural development at the Millennium." Current Opinion in Neurobiology 10, no. 1 (February 2000): 31–37. http://dx.doi.org/10.1016/s0959-4388(99)00065-3.

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Jessen, Kristjan R., and Rhona Mirsky. "Neural Development: Fate diverted." Current Biology 4, no. 9 (September 1994): 824–27. http://dx.doi.org/10.1016/s0960-9822(00)00183-4.

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MEDNICK, SARNOFF A., and J. MEGGIN HOLLISTER. "Neural Development and Schizophrenia." Journal of Nervous and Mental Disease 184, no. 9 (September 1996): 575. http://dx.doi.org/10.1097/00005053-199609000-00011.

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Price, Jack. "Neural Development: Brain stems." Current Biology 5, no. 3 (March 1995): 232–34. http://dx.doi.org/10.1016/s0960-9822(95)00046-7.

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Fineberg, Sarah K., Kenneth S. Kosik, and Beverly L. Davidson. "MicroRNAs Potentiate Neural Development." Neuron 64, no. 3 (November 2009): 303–9. http://dx.doi.org/10.1016/j.neuron.2009.10.020.

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Dissertations / Theses on the topic "Neural development"

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De, Sousa C. A. Ferreira. "Vertebrate somite development and neural patterning." Thesis, Cranfield University, 2013. http://dspace.lib.cranfield.ac.uk/handle/1826/10567.

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The segmentation of the axial skeleton and peripheral nervous system involves a complex integration of multiple patterning molecules. For the latter, axon-repelling molecules in the posterior half-sclerotome are particularly important. This study built on a previously performed mouse microarray screen for novel candidate genes in the posterior half-sclerotome. Multiple candidates were selected for whole-mount in situ hybridization in chick. Two were expressed in the posterior half-sclerotome: thrombin receptor (F2R) and fibronectin leucine rich transmembrane protein-2 (Flrt2). Flrt2 was selected for siRNA-mediated knockdown and a new in ovo transfection technique for somites successfully developed. Scrambled siRNA-transfection did not affect morphogenesis, somite patterning or axon guidance. However, Flrt2 siRNA-transfection resulted in defects in notochord, dermomyotome and neural tube morphogenesis, and in the de-fasciculation and mis-targeting of spinal axons into the posterior half-sclerotome and dermomyotome. Hence, Flrt2 may be a chemorepellent for spinal axons. An unidentified peanut agglutinin (PNA)-binding glycoprotein in the posterior half-sclerotome was previously shown to repel spinal axons. In this project, the expression of a family of mucin-type O-glycosylation enzymes (which could glycosylate the PNA-binding protein) was investigated by whole-mount in situ hybridization in chick, but none was differentially expressed in the posterior half-sclerotome. One candidate for the PNA-binding glycoprotein, Presenilin1, was investigated because of previously published loss of spinal nerve segmentation in Presenilin1 mutants. However, analysis of Presenilin1-hypomorphic mutant mouse embryos showed this was not the PNA-binding molecule. Live-immunostaining for a second candidate, prolyl 4-hydroxylase, beta polypeptide (P4HB), showed its expression coincided with PNA-binding at the surface of posterior half-sclerotome cells. P4HB siRNA-transfection into somites reduced PNA binding and disrupted spinal axon segmentation and expression of a posterior sclerotome marker, Uncx4.1. Overall, these results suggest that P4HB is a strong candidate to be the key PNA-binding glycoprotein in the posterior half-sclerotome that repels spinal axons.
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Mayer, Eric. "Development of intrastriatal neural transplantation techniques." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.282924.

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Fourla, Danai-Dionysia. "Retinoid signalling in Xenopus neural development." Thesis, University of Portsmouth, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.439189.

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This thesis focuses on the use of animal caps to address questions concerning neural induction, patterning and neurogenesis in Xenopus embryos. Animal caps can be neuralised by the inhibition of BMPs through the action of inhibitors, such as noggin, but they do not form neurons. Instead, primary neurogenesis requires additional signalling by retinoic acid (RA), acting through the retinoid receptors, RARIRXR. In the presence of RA, retinoid signalling (RS) activates gene expression. Key target genes of RS during development are the Hox genes that are believed to be important for both posterior patterning and neurogenesis. In contrast, in the absence of RA, RS promotes active repression; a process that is required for normal head formation. However, much less is known of the genes that are affected by active repression. Although RS is sufficient to promote neurogenesis in noggin-neuralised animal caps, it is likely to be working in conjunction with other, endogenous, signalling pathways, mediated for example by FGF and Wnt. In this study, animal caps were analysed for signalling molecule expression after neuralisation by noggin and treatment with RARlRXR (±RA) and it was shown that the expression of some, but not all, FGFs and Wnts respond to RS in the animal cap. This may be significant for neural induction, patterning and neurogenesis; related processes in the animal cap. In addition, the receptor isotypes RARa and RARy seem to elicit different responses from some genes. Positive RS (RARlRXR+RA) was shown to promote posterior markers, such as Hox genes and the expression of Wnt3A. However, negative RS (RARlRXR-RA) was shown to inhibit Wnt3A and activate xSaiF. Consequently, RARs promote Wnt signalling posteriorly via positive signalling and inhibit Wnt signalling anteriorly by negative signalling. In conclusion, this thesis shows that animal caps can be used to investigate the effects of RS in Xenopus neural development and indicates a major role for RS along the length of the AlP axis of the embryo.
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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbolic Integration [4, 16, 21, 40], in which ANNs are used to produce interpretable, adaptable representations of logic programs and other symbolic models. One neural-symbolic model of reasoning is SHRUTI [89, 95], argued to exhibit biological plausibility in that it captures some aspects of real biological processes. SHRUTI's original developers also suggest that further biological plausibility can be ascribed to the fact that SHRUTI networks can be represented by a model of genetic development [96, 120]. The aims of this thesis are to support the claims of SHRUTI's developers by producing the first such genetic representation for SHRUTI networks and to explore biological plausibility further by investigating the evolvability of the proposed SHRUTI genome. The SHRUTI genome is developed and evolved using principles from Generative and Developmental Systems and Artificial Development [13, 105], in which genomes use indirect encoding to provide a set of instructions for the gradual development of the phenotype just as DNA does for biological organisms. This thesis presents genomes that develop SHRUTI representations of logical relations and episodic facts so that they are able to correctly answer questions on the knowledge they represent. The evolvability of the SHRUTI genomes is limited in that an evolutionary search was able to discover genomes for simple relational structures that did not include conjunction, but could not discover structures that enabled conjunctive relations or episodic facts to be learned. Experiments were performed to understand the SHRUTI fitness landscape and demonstrated that this landscape is unsuitable for navigation using an evolutionary search. Complex SHRUTI structures require that necessary substructures must be discovered in unison and not individually in order to yield a positive change in objective fitness that informs the evolutionary search of their discovery. The requirement for multiple substructures to be in place before fitness can be improved is probably owed to the localist representation of concepts and relations in SHRUTI. Therefore this thesis concludes by making a case for switching to more distributed representations as a possible means of improving evolvability in the future.
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Cottens, Pablo Eduardo Pereira de Araujo. "Development of an artificial neural network architecture using programmable logic." Universidade do Vale do Rio dos Sinos, 2016. http://www.repositorio.jesuita.org.br/handle/UNISINOS/5411.

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Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2016-06-29T14:42:16Z No. of bitstreams: 1 Pablo Eduardo Pereira de Araujo Cottens_.pdf: 1315690 bytes, checksum: 78ac4ce471c2b51e826c7523a01711bd (MD5)
Made available in DSpace on 2016-06-29T14:42:16Z (GMT). No. of bitstreams: 1 Pablo Eduardo Pereira de Araujo Cottens_.pdf: 1315690 bytes, checksum: 78ac4ce471c2b51e826c7523a01711bd (MD5) Previous issue date: 2016-03-07
Nenhuma
Normalmente Redes Neurais Artificiais (RNAs) necessitam estações de trabalho para o seu processamento, por causa da complexidade do sistema. Este tipo de arquitetura de processamento requer que instrumentos de campo estejam localizados na vizinhança da estação de trabalho, caso exista a necessidade de processamento em tempo real, ou que o dispositivo de campo possua como única tarefa a de coleta de dados para processamento futuro. Este projeto visa criar uma arquitetura em lógica programável para um neurônio genérico, no qual as RNAs podem fazer uso da natureza paralela de FPGAs para executar a aplicação de forma rápida. Este trabalho mostra que a utilização de lógica programável para a implementação de RNAs de baixa resolução de bits é viável e as redes neurais, devido à natureza paralelizável, se beneficiam pela implementação em hardware, podendo obter resultados de forma muito rápida.
Currently, modern Artificial Neural Networks (ANN), according to their complexity, require a workstation for processing all their input data. This type of processing architecture requires that the field device is located somewhere in the vicintity of a workstation, in case real-time processing is required, or that the field device at hand will have the sole task of collecting data for future processing, when field data is required. This project creates a generic neuron architecture in programmabl logic, where Artifical Neural Networks can use the parallel nature of FPGAs to execute applications in a fast manner, albeit not using the same resolution for its otputs. This work shows that the utilization of programmable logic for the implementation of low bit resolution ANNs is not only viable, but the neural network, due to its parallel nature, benefits greatly from the hardware implementation, giving fast and accurate results.
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Waldhuber, Markus. "Neural stem cells in development and cancer." Diss., lmu, 2008. http://nbn-resolving.de/urn:nbn:de:bvb:19-92214.

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Subbarao, Tara. "Effects of nicotine on embryological neural development." Connect to resource, 2007. http://hdl.handle.net/1811/24616.

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Thesis (Honors)--Ohio State University, 2007.
Title from first page of PDF file. Document formatted into pages: contains 18 p.; also includes graphics. Includes bibliographical references. Available online via Ohio State University's Knowledge Bank.
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Chuong, Amy (Amy S. ). "Development of next-generation optical neural silencers." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/69521.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 65-74).
The ability to rapidly and safely silence the electrical activity of individual neurons or neuron populations is invaluable in the study of brain circuit mapping. The expression of light-driven ion channels and pumps allows these pathways to be observed, mapped and controlled with millisecond timescale resolution. We here show that it is possible to mediate the powerful multiple-color silencing of neural activity through the heterologous expression of light-driven outward proton pumps and inward chloride pumps. We characterized a number of novel opsins through an exploration of ecological and genomic diversity, and further boosted opsin function and trafficking through the appendage of signal sequences. The green-light drivable archaerhodopsin-3 (Arch) from Halorubrum sodomense and the yellow-light drivable archaerhodopsin from Halorubrum strain TP009 (ArchT) are able to mediate complete neuron silencing in the in vivo awake mouse brain, and the blue-light drivable proton pump from Leptosphaeria maculans (Mac) opens up the potential for the multiple-color control of independent neuron populations. Finally, the principles outlined here can be extrapolated to the larger context of synthetic physiology.
by Amy Chuong.
S.M.
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Canales, Andrés. "Development of neural probes using thermal drawing." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111316.

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Thesis: Ph. D., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 127-147).
The treatment of neurodegenerative and neurological conditions relies on better understanding the system that they afflict. However, the tools currently available to probe neural circuits are often limited to use in short-term studies primarily due to poor of biocompatibility. To address this challenge, flexible, minimally invasive neural probes were fabricated using a thermal drawing process, with polymers serving as their main structural constituent. Through the use of different polymers, probes containing arrays of tin electrodes as small as 5 [mu]m were fabricated, as were probes combining capabilities for electrical recording, optical stimulation, and drug delivery. A technique was developed to combine functionalities of these devices into a single probe to study the effect of optical stimulation with different waveforms on the brain activity. To break the longitudinal symmetry inherent to probes fabricated using the thermal drawing process, and to allow the incorporation of functionalities along the probe length, a method to combine thermal drawing with a method commonly used to fabricate neural probes, photolithography, was developed, along with the selection of the polymer that would allow consecutive processing using these two techniques. All of the fabricated probes were characterized and tested in vivo by implantation into mice and assessing their functionality. High signal-to-noise ratio (13±6) recordings were obtained using multielectrode arrays. Recordings of neural activity during simultaneous optical stimulation and drug delivery were performed with multifunctional probes. Hybrid probes combining metal electrodes with a polymer waveguide were used to study the response of large groups of neurons to different forms of optical stimuli. Most importantly, the biocompatibility of these probes was assessed over a 3 month period and compared favorably to that of steel microwires of similar size.
by Andrés Canales.
Ph. D.
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Biswas, Sayantanee. "Role of Protocadherins in Zebrafish Neural Development." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1354720718.

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Books on the topic "Neural development"

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Zhou, Renping, and Lin Mei, eds. Neural Development. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-444-9.

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1935-, Uyemura K., Kawamura Kōki 1934-, Yazaki T. 1958-, and Keio University International Symposium for Life Sciences and Medicine (2nd : 1997 : Tokyo, Japan), eds. Neural development. Tokyo: Springer, 1999.

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name, No. Modeling neural development. Cambridge, MA: MIT Press, 2003.

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Brown, M. C. Essentials of neural development. Cambridge: Cambridge University Press, 1991.

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1951-, Lichtman Jeff W., ed. Principles of neural development. Sunderland, Mass: Sinauer Associates, 1985.

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Mednick, Sarnoff A., and J. Meggin Hollister, eds. Neural Development and Schizophrenia. Boston, MA: Springer US, 1995. http://dx.doi.org/10.1007/978-1-4615-1955-3.

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Gorio, A., J. R. Perez-Polo, J. de Vellis, and B. Haber, eds. Neural Development and Regeneration. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73148-8.

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service), ScienceDirect (Online, ed. Development of neural circuitry. Amsterdam: Academic Press, 2009.

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Ranney, Mize R., and Erzurumlu Reha S, eds. Neural development and plasticity. Amsterdam: Elsevier, 1996.

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Tucker, Don M. Cognition and neural development. New York, N.Y: Oxford University Press, 2012.

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Book chapters on the topic "Neural development"

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Scott, Jill, and Esther Stoeckli. "Neural Development." In Neuromedia, 23–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30322-7_2.

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Ellis, George F. R., and Judith A. Toronchuk. "Neural development." In Consciousness & Emotion, 81–119. Amsterdam: John Benjamins Publishing Company, 2005. http://dx.doi.org/10.1075/ceb.1.06ell.

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Francis-West, P. H., L. Robson, and Darell J. R. Evans. "Neural Crest Development." In Craniofacial Development The Tissue and Molecular Interactions That Control Development of the Head, 31–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-642-55570-1_4.

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Schor, Nina Felice. "Neural Crest Development." In The Neurology of Neuroblastoma, 1–13. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-1-4615-1057-4_1.

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Nelson, Charles A., Michelle de Haan, and Kathleen M. Thomas. "Neural Plasticity." In Neuroscience of Cognitive Development, 30–43. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2015. http://dx.doi.org/10.1002/9780470939413.ch2.

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Palm, Günther. "The Development of Brain Theory." In Neural Assemblies, 229–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00311-0_10.

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Morita, Hitoshi, Makoto Suzuki, and Naoto Ueno. "Neural Tube Closure inXenopus." In Xenopus Development, 163–85. Oxford: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118492833.ch9.

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Tušar, Marjan, and Jure Zupan. "Neural Networks." In Software Development in Chemistry 4, 363–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-75430-2_38.

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Wang, Yuekai, Xiaofeng Wu, and Juyang Weng. "Skull-Closed Autonomous Development." In Neural Information Processing, 209–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24955-6_25.

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Moreno, Mauricio, Karina Tapia, and Juan Larrain. "Neural Regeneration inXenopusTadpoles during Metamorphosis." In Xenopus Development, 293–308. Oxford: John Wiley & Sons, Inc, 2014. http://dx.doi.org/10.1002/9781118492833.ch15.

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Conference papers on the topic "Neural development"

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Sandjakoska, Ljubinka, Ana Madevska Bogdanova, and Nevena Ackovska. "Predictive modeling of robot movements in drug development." In 2016 13th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2016. http://dx.doi.org/10.1109/neurel.2016.7800099.

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Lacrama, Dan L., Florin Alexa, Florentina A. Pintea, and Tiberiu M. Karnyanszky. "Brain - Machine Interfaces in the Context of Artificial Intelligence Development." In 2018 14th Symposium on Neural Networks and Applications (NEUREL). IEEE, 2018. http://dx.doi.org/10.1109/neurel.2018.8586979.

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Page, Edward W., and Gene A. Tagliarini. "Algorithm Development For Neural Networks." In 1988 Los Angeles Symposium--O-E/LASE '88, edited by David P. Casasent. SPIE, 1988. http://dx.doi.org/10.1117/12.944027.

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Umadevi Venkataraju, Kannan U., James Gornet, Gayathri Murugaiyan, Zhuhao Wu, and Pavel Osten. "Development of brain templates for whole brain atlases." In Neural Imaging and Sensing 2019, edited by Qingming Luo, Jun Ding, and Ling Fu. SPIE, 2019. http://dx.doi.org/10.1117/12.2505295.

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Johrendt, Jennifer L., and Peter R. Frise. "Neural Network Bushing Model Development Using Simulation." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28103.

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Neural networks are computationally efficient mathematical models that can be used to model quantitative and qualitative data. A neural network can be created through training with known input and output load-deflection data such that it learns to generalize the material characteristics without over-predicting the training data and losing its ability to anticipate behavior outside the training set. The challenge in creating a neural network model of a rubber bushing in a virtual model of a prototype assembly, for instance, is the lack of a physical prototype assembly. This paper describes a method by which data can be measured from a virtual prototype and used to define an appropriate data acquisition for the physical bushing. Training data can then be acquired using these guidelines and used for neural network model development. Subsequently, the enhanced model can then be used in the virtual simulation environment to increase the accuracy of the simulation results.
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DINIZ, IVANDO SEVERINO, HIAGO CASSIANO PINHEIRO MACIEL, and WESLEY ANGELINO DE SOUZA. "Aplicação de Deep Learning para Detecção de Padrões Cardíacos em Diagnóstico Clínico." In II Brazilian Congress of Development. DEV2021, 2021. http://dx.doi.org/10.51162/brc.dev2021-0031.

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Este artigo apresenta a implementação de redes neurais artificiais, na abordagem de redes neurais profundas, voltada à análise clínica de eletrocardiograma, para facilitar o diagnóstico e assistir o cuidado aos pacientes. Neste processo, são utilizadas técnicas de tratamento e engenharia de dados, bem como o uso de aprendizado de máquina, na qual tem-se como objetivo encontrar o erro mínimo, melhor desempenho possível na assertividade do diagnóstico. Os dados para o treinamento, testes e validação da rede neural foram extraídos do Instituto Nacional de Metrologia da Alemanha, que fornece informação clínica através do site physionet.org. A base de dados de eletrocardiograma possui 549 entradas, com informação dados dos batimentos cardíacos e de outros exames anamnésicos. O processamento de dados, o desenvolvimento da rede neural e a sua estruturação, bem como os gráficos e resultados, foram desenvolvidos na linguagem python. Os resultados apresentam a viabilidade de se criar um algoritmo que possa dar a um paciente a maioria (se não toda) informação obtida através do eletrocardiograma e da anamnese e informar, com alguma precisão, a possibilidade de alguma patologia ao paciente.,
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Samuilik, Inna, Felix Sadyrbaev, and Svetlana Atslega. "On mathematical models of artificial neural networks." In 22nd International Scientific Conference Engineering for Rural Development. Latvia University of Life Sciences and Technologies, Faculty of Engineering, 2023. http://dx.doi.org/10.22616/erdev.2023.22.tf007.

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Artificial Neural Networks are in focus. The four-dimensional and the five-dimensional systems are considered. The activation function – hyperbolic tangent is used to model the Artificial Neural Networks. By changing one of the parameters in the system, different types of solutions are obtained: periodic solutions and chaotic solutions. The graphs of all solutions, the dynamics of Lyapunov exponents and 2D and 3D projections of attractors are provided using Wolfram Mathematica.
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Choi, Sang H., Kyo D. Song, Yeonjoon Park, and Uhn Lee. "Development of Neural Probe With Wireless Power Feed." In ASME 2010 First Global Congress on NanoEngineering for Medicine and Biology. ASMEDC, 2010. http://dx.doi.org/10.1115/nemb2010-13055.

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New medical device technology is essential for diagnosing, monitoring, and curing wide spectrum of diseases, anomalies and inflictions. For neural applications, no matter whether non-intrusive or not, currently available devices are generally limited to either a curing or a probing function. In this paper we review the technology requirements for new neural probe and cure device technology. Recent advances in micro and nano-scale devices engineering and wireless power technology offer a great potential to revolutionize many health care systems. The integration of wireless power technology into smart microsensor and probe systems greatly simplifies the healthcare devices and systems and also offers additional device functions for even complex jobs. The wireless power feed technology eliminates the pains and irritations associated with implanted power devices and wires. Neural electronics interfaces (NEI) can be coupled and integrated with the wireless power receiver (WPR). The implantable probe-pin devices (PPD) that include the NEI and WPR allow real-time measurement and control/feedback possible for remedial process of neural anomaly from normal functions. Such a system like a PPD should have an embedded expert system that performs semi-autonomous functions through a routine of sensing, judging, and controlling the neural anomaly.
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Boll-Avetisyan, Natalie, Jessie S. Nixon, Tomas O. Lentz, Liquan Liu, Sandrien van Ommen, Çağri Çöltekin, and Jacolien van Rij. "Neural Response Development During Distributional Learning." In Interspeech 2018. ISCA: ISCA, 2018. http://dx.doi.org/10.21437/interspeech.2018-2072.

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Polimac, J. "Conceptual development of neural management maintenance." In Fifth International Conference on Power System Management and Control. IEE, 2002. http://dx.doi.org/10.1049/cp:20020078.

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Reports on the topic "Neural development"

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Maloney, Laurence T. Visual Neural Development and Chromatic Aberration. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada277402.

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Maloney, Laurence T. Visual Neural Development and Chromatic Aberration. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada285064.

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Ess, Kevin. Neural Development in tsc2-Deficient Zebrafish. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada590191.

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Leij, F. J., and M. T. Van Genuchten. Development of Pedotransfer Functions with Neural Network Models. Fort Belvoir, VA: Defense Technical Information Center, June 2001. http://dx.doi.org/10.21236/ada394563.

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Allgood, G. O. Development of a neural net paradigm that predicts simulator sickness. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/6178481.

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Fox-Rabinovitz, M. S., and V. M. Krasnopolsky. Development of Ensemble Neural Network Convection Parameterizations for Climate Models. Office of Scientific and Technical Information (OSTI), May 2012. http://dx.doi.org/10.2172/1039344.

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Allgood, G. O. Development of a neural net paradigm that predicts simulator sickness. Office of Scientific and Technical Information (OSTI), March 1993. http://dx.doi.org/10.2172/10172277.

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Bannerman, Peter G. The Functional Role(s) of Neurofibromin During Neural Crest Cell Development. Fort Belvoir, VA: Defense Technical Information Center, August 2001. http://dx.doi.org/10.21236/ada398169.

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Bannerman, Peter G. The Functional Role(s) of Neurofibromin During Neural Crest Cell Development. Fort Belvoir, VA: Defense Technical Information Center, August 2002. http://dx.doi.org/10.21236/ada411420.

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Zervas, Mark. Temporal Loss of Tsc1: Neural Development and Brain Disease in Tuberous Sclerosis. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada609442.

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