Dissertations / Theses on the topic 'Neural fields'
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Ueda, Hiroyuki. "Studies on low-field functional MRI to detect tiny neural magnetic fields." Doctoral thesis, Kyoto University, 2021. http://hdl.handle.net/2433/263666.
Full text京都大学
新制・課程博士
博士(工学)
甲第23205号
工博第4849号
京都大学大学院工学研究科電気工学専攻
(主査)教授 小林 哲生, 教授 松尾 哲司, 特定教授 中村 武恒
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DFAM
Webber, Matthew. "Stochastic neural field models of binocular rivalry waves." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:c444a73e-20e3-454d-85ae-bbc8831fdf1f.
Full textDavenport, Christopher M. "Neural circuitry of retinal receptive fields in primate /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/10652.
Full textArocena, Miguel. "Control of neural stem cell migration by electric fields." Thesis, University of Aberdeen, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.540498.
Full textFerguson, Archibald Stewart. "Theoretical calculation of magnetic fields generated by neural currents." Case Western Reserve University School of Graduate Studies / OhioLINK, 1991. http://rave.ohiolink.edu/etdc/view?acc_num=case1055524502.
Full textQi, Yang. "Anomalous neural pattern dynamics: formation mechanisms and functional roles." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18808.
Full textRohlén, Andreas. "UAV geolocalization in Swedish fields and forests using Deep Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300390.
Full textObemannade autonoma luftburna fordons (UAV) förmåga att lokaliera sig själva är fundamental för att de ska fungera, även om de inte har tillgång till globala positioneringssystem. Med den nyliga framgången hos djupinlärning applicerat på visuella problem har det kommit metoder för absolut geolokalisering med visuell djupinlärning med satellit- och UAV-bilder. De flesta av dessa metoder har bara blivit testade i stadsmiljöer, vilket leder till frågan: Hur väl fungerar dessa metoder i icke-urbana områden som fält och skogar? En av nackdelarna med djupinlärning är att dessa modeller ofta ses som svarta lådor eftersom det är svårt att veta varför modellerna gör de gissningar de gör, alltså vilken information som är viktig och används för gissningen. För att lösa detta har flera metoder för att tolka neurala nätverk utvecklats. Dessa metoder ger förklaringar så att vi kan förstå dessa modeller bättre. Denna uppsats undersöker lokaliseringsprecisionen hos en geolokaliseringsmetod i både urbana och icke-urbana miljöer och applicerar även en tolkningsmetod för neurala nätverk för att se ifall den kan förklara den potentialla skillnaden i precision hos metoden i dessa olika miljöer. Resultaten visar att metoden fungerar bäst i urbana miljöer där den får ett genomsnittligt absolut horisontellt lokaliseringsfel på 38.30m och ett genomsnittligt absolut vertikalt fel på 16.77m medan den presterade signifikant sämre i icke-urbana miljöer där den fick ett genomsnittligt absolut horisontellt lokaliseringsfel på 68.11m och ett genomsnittligt absolut vertikalt fel på 22.83m. Vidare visar resultaten att om satellitbilderna och UAV-bilderna är tagna från olika årstider blir lokaliseringsprecisionen ännu sämre, där metoden får genomsnittligt absolut horisontellt lokaliseringsfel på 86.91m och ett genomsnittligt absolut vertikalt fel på 23.05m. Tolkningsmetoden hjälpte inte i att förklara varför metoden fungerar sämre i icke-urbana miljöer och är inte passande att använda för denna sortens problem.
Curtis, Maurice A. "Neural progenitor cells in the Huntington's Disease human brain." Thesis, University of Auckland, 2004. http://hdl.handle.net/2292/3114.
Full textZhang, Yiming. "Applications of artificial neural networks (ANNs) in several different materials research fields." Thesis, Queen Mary, University of London, 2010. http://qmro.qmul.ac.uk/xmlui/handle/123456789/362.
Full textHarris, William H. (William Hunt). "Machine learning transferable physics-based force fields using graph convolutional neural networks." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/128979.
Full textCataloged from student-submitted PDF of thesis.
Includes bibliographical references (pages 22-24).
Molecular dynamics and Monte Carlo methods allow the properties of a system to be determined from its potential energy surface (PES). In the domain of crystalline materials, the PES is needed for electronic structure calculations, critical for modeling semiconductors, optical, and energy-storage materials. While first principles techniques can be used to obtain the PES to high accuracy, their computational complexity limits applications to small systems and short timescales. In practice, the PES must be approximated using a computationally cheaper functional form. Classical force field (CFF) approaches simply define the PES as a sum over independent energy contributions. Commonly included terms include bonded (pair, angle, dihedral, etc.) and non bonded (van der Waals, Coulomb, etc.) interactions, while more recent CFFs model polarizability, reactivity, and other higher-order interactions.
Simple, physically-justified functional forms are often implemented for each energy type, but this choice - and the choice of which energy terms to include in the first place - is arbitrary and often hand-tuned on a per-system basis, severely limiting PES transferability. This flexibility has complicated the quest for a universal CFF. The simplest usable CFFs are tailored to specific classes of molecules and have few parameters, so that they can be optimally parameterized using a small amount of data; however, they suffer low transferability. Highly-parameterized neural network potentials can yield predictions that are extremely accurate for the entire training set; however, they suffer over-fitting and cannot interpolate.
We develop a tool, called AuTopology, to explore the trade-offs between complexity and generalizability in fitting CFFs; focus on simple, computationally fast functions that enforce physics-based regularization and transferability; use message-passing neural networks to featurized molecular graphs and interpolate CFF parameters across chemical space; and utilize high performance computing resources to improve the efficiency of model training and usage. A universal, fast CFF would open the door to high-throughput virtual materials screening in the pursuit of novel materials with tailored properties.
by William H. Harris.
S.M.
S.M. Massachusetts Institute of Technology, Department of Materials Science and Engineering
Veltz, Romain, and Romain Veltz. "Nonlinear analysis methods in neural field models." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00686695.
Full textLiu, Xinhe. "Implementation of dynamical systems with plastic self-organising velocity fields." Thesis, Loughborough University, 2015. https://dspace.lboro.ac.uk/2134/19550.
Full textVeltz, Romain. "Nonlinear analysis methods in neural field models." Thesis, Paris Est, 2011. http://www.theses.fr/2011PEST1056/document.
Full textThis thesis deals with mesoscopic models of cortex called neural fields. The neural field equations describe the activity of neuronal populations, with common anatomical / functional properties. They were introduced in the 1950s and are called the equations of Wilson and Cowan. Mathematically, they consist of integro-differential equations with delays, the delays modeling the signal propagation and the passage of signals across synapses and the dendritic tree. In the first part, we recall the biology necessary to understand this thesis and derive the main equations. Then, we study these equations with the theory of dynamical systems by characterizing their equilibrium points and dynamics in the second part. In the third part, we study these delayed equations in general by giving formulas for the bifurcation diagrams, by proving a center manifold theorem, and by calculating the principal normal forms. We apply these results to one-dimensional neural fields which allows a detailed study of the dynamics. Finally, in the last part, we study three models of visual cortex. The first two models are from the literature and describe respectively a hypercolumn, i.e. the basic element of the first visual area (V1) and a network of such hypercolumns. The latest model is a new model of V1 which generalizes the two previous models while allowing a detailed study of specific effects of delays
Prisilla, L., P. Simon Vasantha Rooban, and L. Arockiam. "A Novel Method for Water irrigation System for paddy fields using ANN." IJCSN, 2012. http://hdl.handle.net/10150/219532.
Full textWater is an essential resource in the earth. It is also essential for irrigation, so irrigation technique is essential for agriculture. To irrigate large area of lands is a tedious process. In our country farmers are not following proper irrigation techniques. Currently, most of the irrigation scheduling systems and their corresponding automated tools are in fixed rate. Variable rate irrigation is very essential not only for the improvement of irrigation system but also to save water resource for future purpose. Most of the irrigation controllers are ON/OFF Model. These controllers cannot give optimal results for varying time delays and system parameters. Artificial Neural Network (ANN) based intelligent control system is used for effective irrigation scheduling in paddy fields. The input parameters like air, temperature, soil moisture, radiations and humidity are modeled. Using appropriate method, ecological conditions, Evapotranspiration, various growing stages of crops are considered and based on that the amount of water required for irrigation is estimated. Using this existing ANN based intelligent control system, the water saving procedure in paddy field can be achieved. This model will lead to avoid flood in paddy field during the rainy seasons and save that water for future purposes.
Hutt, Axel. "The study of neural oscillations by traversing scales in the brain." Habilitation à diriger des recherches, Université de Nice Sophia-Antipolis, 2011. http://tel.archives-ouvertes.fr/tel-00603975.
Full textJohnson, Jeffrey S. "A dynamic neural field model of visual working memory and change detection." Diss., University of Iowa, 2008. http://ir.uiowa.edu/etd/12.
Full textMcNair, Nicolas A. "Input-specificity of sensory-induced neural plasticity in humans." Thesis, University of Auckland, 2008. http://hdl.handle.net/2292/3285.
Full textJenkins, Glenn Llewellyn. "Evolved neural network approximation of discontinuous vector fields in unit quaternion space (S³) for anatomical joint constraint." Thesis, University of South Wales, 2007. https://pure.southwales.ac.uk/en/studentthesis/evolved-neural-network-approximation-of-discontinuous-vector-fields-in-unit-quaternion-space-s3-for-anatomical-joint-constraint(f375e712-038c-4a78-862a-944c0e36e360).html.
Full textAL-Rashidi, Abdulrahman F. "Designing neural networks for the prediction of the drilling parameters for Kuwait oil and gas fields." Morgantown, W. Va. : [West Virginia University Libraries], 1999. http://etd.wvu.edu/templates/showETD.cfm?recnum=1209.
Full textTitle from document title page. Document formatted into pages; contains x, 76 p. : ill. (some col.), map (some col.). Includes abstract. Includes bibliographical references (p. 54-55).
Lohmann, Kenneth John. "Neural correlates of magnetic field detection and geomagnetic orientation by the marine mollusk Tritonia diomedea / by Kenneth John Lohmann." Thesis, Connect to this title online; UW restricted, 1988. http://hdl.handle.net/1773/5247.
Full textLiu, Xiaochen. "Modelling Functional Maps and Associated Visual Gamma Activities in the Primary Visual Cortex." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28536.
Full textLang, Eva [Verfasser], Wilhelm [Akademischer Betreuer] Stannat, Wilhelm [Gutachter] Stannat, Dirk [Gutachter] Blömker, and Olivier [Gutachter] Faugeras. "Traveling waves in stochastic neural fields / Eva Lang ; Gutachter: Wilhelm Stannat, Dirk Blömker, Olivier Faugeras ; Betreuer: Wilhelm Stannat." Berlin : Technische Universität Berlin, 2016. http://d-nb.info/1156011817/34.
Full textHallström, Eric. "Relation Extraction on Swedish Text by the Use of Semantic Fields and Deep Multi-Channel Convolutional Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262494.
Full textDetta arbete bidrar med två insikter till forskning inom relationsextrahering (RE), det vill säga, att automatiskt upptäcka semantiska länkar i ostrukturerad text. Det första bidraget är en metod för att skapa ett dataset för RE och för att använda det till att skapa ett svenskt RE-dataset som involverar nio relationer mellan personer, platser och fordon. Det andra bidraget är en baslinje via experiment på detta nya dataset. Relationsextraheringssystemet skapat i detta arbete inkluderar ett djupt flerkanaligt faltningsnätverk med ordvektorer viaWord2Vec-algoritmen. En manuell kategorisering av en delmängd av datan visar en tillförlitlighet på 73%. Resultaten visar att användningen av en diskret representation av ordklasser och beroende-taggar i det flerkanaliga neurala nätverket presterar bäst med ett medelvärde av mikro-F1 på 77%. Detta arbete diskuterar problem och framtida tillämpningar, inkluderat den underliggande motiveringen för detta arbete: automatisk summering av svenska polisrapporter.
Sanz, Leon Paula. "Development of a computational and neuroinformatics framework for large-scale brain modelling." Thesis, Aix-Marseille, 2014. http://www.theses.fr/2014AIXM5036/document.
Full textThe central theme of this thesis is the development of both a generalised computational model for large-scale brain networks and the neuroinformatics platform that enables a systematic exploration and analysis of those models. In this thesis we describe the mathematical framework of the computational model at the core of the tool The Virtual brain (TVB), designed to recreate collective whole brain dynamics by virtualising brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. We also review previous studies related to brain network models and multimodal neuroimaging integration and detail how they are related to the general model presented in this work. Practical examples describing how to build a minimal *in silico* primate brain model are given. Finally, we explain how the resulting software tool, TVB, facilitates the collaboration between experimentalists and modellers by exposing both a comprehensive simulator for brain dynamics and an integrative framework for the management, analysis, and simulation of structural and functional data in an accessible, web-based interface
Grehl, Stephanie. "Stimulation-specific effects of low intensity repetitive magnetic stimulation on cortical neurons and neural circuit repair in vitro (studying the impact of pulsed magnetic fields on neural tissue)." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066706/document.
Full textElectromagnetic fields are widely used to non-invasively stimulate the human brain in clinical treatment and research. This thesis investigates the effects of different low intensity (mT) repetitive magnetic stimulation (LI-rMS) parameters on single neurons and neural networks and describes key aspects of custom tailored LI-rMS delivery in vitro. Our results show stimulation specific effects of LI-rMS on cell survival, neuronal morphology, neural circuit repair and gene expression. We show novel mechanisms underlying cellular responses to stimulation below neuronal firing threshold, extending our understanding of the fundamental effects of LI-rMS on biological tissue which is essential to better tailor therapeutic applications
Ariza, Carlos Atico. "Topography, extracellular matrix proteins, secreted molecules and endogenous electric fields cues that influence the differentiation of neural progenitor cells /." [Ames, Iowa : Iowa State University], 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3389082.
Full textPerone, Sammy. "Using dynamic neural fields to understand the development of metric representations in typically developing and at-risk infant populations." Diss., University of Iowa, 2010. https://ir.uiowa.edu/etd/872.
Full textYu, Hsin-Hao. "Integration of visual information and the organization of receptive fields in V1 of the California ground squirrel." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3283974.
Full textTitle from first page of PDF file (viewed January 8, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 112-124).
Khouzam, Bassem. "Neural networks as cellular computing models for temporal sequence processing." Thesis, Supélec, 2014. http://www.theses.fr/2014SUPL0007/document.
Full textThe thesis proposes a sequence learning approach that uses the mechanism of fine grain self-organization. The manuscript initially starts by situating this effort in the perspective of contributing to the promotion of cellular computing paradigm in computer science. Computation within this paradigm is divided into a large number of elementary calculations carried out in parallel by computing cells, with information exchange between them.In addition to their fine grain nature, the cellular nature of such architectures lies in the spatial topology of the connections between cells that complies with to the constraints of the technological evolution of hardware in the future. In the manuscript, most of the distributed architecture known in computer science are examined following this perspective, to find that very few of them fall within the cellular paradigm.We are interested in the learning capacity of these architectures, because of the importance of this notion in the related domain of neural networks for example, without forgetting, however, that cellular systems are complex dynamical systems by construction.This inevitable dynamical component has motivated our focus on the learning of temporal sequences, for which we reviewed the different models in the domains of neural networks and self-organization maps.At the end, we proposed an architecture that contributes to the promotion of cellular computing in the sense that it exhibits self-organization properties employed in the extraction of a representation of a dynamical system states that provides the architecture with its entries, even if the latter are ambiguous such that they partially reflect the system state. We profited from an existing supercomputer to simulate complex architecture, that indeed exhibited a new emergent behavior. Based on these results we pursued a critical study that sets the perspective for future work
Köhler, Rolf [Verfasser], and Hendrik [Akademischer Betreuer] Lensch. "Advances in computational imaging : Benchmarking Deblurring Algorithms, Deep Neural Inpainting, Depth Estimation from Light Fields / Rolf Köhler ; Betreuer: Hendrik Lensch." Tübingen : Universitätsbibliothek Tübingen, 2016. http://d-nb.info/1164170279/34.
Full textPlaut, Maxwell Ethan. "Characterizing hydrogel imposed strain fields on brain tissue phantom for use in neural implant device coatings in presence of micromotion." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/89977.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (pages 27-30).
Glial scar tissue forms in the brain as a response to the implant injury and hampers the effectiveness of the implant treatment. Constant relative micromotion between the mechanically mismatched neural implant and brain tissue is thought to play a key role glial scar formation. This study investigated the effects of poly(ethylene glycol) (PEG) hydrogel coatings for glass brain implant devices on strain fields imposed by those devices to brain tissue due to micromotion in the brain. PEG hydrogels were created using macromers of 2000-8000 Mw and 5-20 wt.% in solution. The moduli of the hydrogels were calculated via Hertzian analysis of force-deflection curves produced using an AFM tip as a nanoindenter. The moduli of the samples did not change significantly with change in macromer Mw, but did change with solution concentration. 20% gels had moduli of 120-180 kPa and 5-10% gels had moduli of 0-20 kPa. The strains imposed by the coated devices were found to be lower at the surface by ~30% as compared to uncoated and the strain field dropped off much more quickly.
by Maxwell Ethan Plaut.
S.B.
Winter, Luca. "Algoritmy pro rozpoznávání pojmenovaných entit." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2017. http://www.nusl.cz/ntk/nusl-320108.
Full textLukačovič, Martin. "Segmentace obrazu s využitím hlubokého učení." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2017. http://www.nusl.cz/ntk/nusl-317124.
Full textHassan, K. J. "Application of artificial neural networks for understanding and diagnosing the state of mastitis in dairy cattle." Lincoln University, 2007. http://hdl.handle.net/10182/633.
Full textLing, Hong. "Implementation of Stochastic Neural Networks for Approximating Random Processes." Master's thesis, Lincoln University. Environment, Society and Design Division, 2007. http://theses.lincoln.ac.nz/public/adt-NZLIU20080108.124352/.
Full textTAVARES, Hugo Saraiva. "Conjunto invariantes para tricotomia exponencial e aplicações a campos neurais." Universidade Federal de Campina Grande, 2016. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1409.
Full textMade available in DSpace on 2018-08-11T14:07:58Z (GMT). No. of bitstreams: 1 HUGO SARAIVA TAVARES - DISSERTAÇÃO PPGMAT 2016..pdf: 830543 bytes, checksum: d315a3d95e0d2b08d4251cbb487964b6 (MD5) Previous issue date: 2016-06
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Sun, Zhibin. "Application of artificial neural networks in early detection of Mastitis from improved data collected on-line by robotic milking stations." Lincoln University, 2008. http://hdl.handle.net/10182/665.
Full textFörster, Jona. "ERP and MEG Correlates of Visual Consciousness : An Update." Thesis, Högskolan i Skövde, Institutionen för biovetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-17375.
Full textUwamahoro, Jean. "An analysis of sources and predictability of geomagnetic storms." Thesis, Rhodes University, 2011. http://hdl.handle.net/10962/d1005236.
Full textPontin, David R. "Factors influencing the occurrence of stinging jellyfish (Physalia spp.) at New Zealand beaches." Lincoln University, 2009. http://hdl.handle.net/10182/1580.
Full textRoy, Nipa. "Neural Field Theory of Nonlinear Wave-Wave and Wave-Neuron Processes." Thesis, The University of Sydney, 2018. http://hdl.handle.net/2123/18791.
Full textVenkov, Nikola A. "Dynamics of neural field models." Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.517742.
Full textChappet, de Vangel Benoît. "Modèles cellulaires de champs neuronaux dynamiques." Thesis, Université de Lorraine, 2016. http://www.theses.fr/2016LORR0194/document.
Full textIn the constant search for design going beyond the limits of the von Neumann architecture, non conventional computing offers various solutions like neuromorphic engineering and cellular computing. Like von Neumann who roughly reproduced brain structures to design computers architecture, neuromorphic engineering takes its inspiration directly from neurons and synapses using analog substratum. Cellular computing influence comes from natural substratum (chemistry, physic or biology) imposing locality of interactions from which organisation and computation emerge. Research on neural mechanisms was able to demonstrate several emergent properties of the neurons and synapses. One of them is the attractor dynamics described in different frameworks by Amari with the dynamic neural fields (DNF) and Amit and Zhang with the continuous attractor neural networks. These neural fields have various computing properties and are particularly relevant for spatial representations and early stages of visual cortex processing. They were used, for instance, in autonomous robotics, classification and clusterization. Similarly to many neuronal computing models, they are robust to noise and faults and thus are good candidates for noisy hardware computation models which would enable to keep up or surpass the Moore law. Indeed, transistor area reductions is leading to more and more noise and the relaxation of the approx. 0% fault during production and operation of integrated circuits would lead to tremendous savings. Furthermore, progress towards many-cores circuits with more and more cores leads to difficulties due to the centralised computation mode of usual parallel algorithms and their communication bottleneck. Cellular computing is the natural answer to these problems. Based on these different arguments, the goal of this thesis is to enable rich computations and applications of dynamic neural fields on hardware substratum with neuro-cellular models enabling a true locality, decentralization and scalability of the computations. This work is an attempt to go beyond von Neumann architectures by using cellular and neuronal computing principles. However, we will stay in the digital framework by exploring performances of proposed architectures on FPGA. Analog hardware like VLSI would also be very interesting but is not studied here. The main contributions of this work are : 1) Neuromorphic DNF computation ; 2) Local DNF computations with randomly spiking dynamic neural fields (RSDNF model) ; 3) Local and asynchronous DNF computations with cellular arrays of stochastic asynchronous spiking DNFs (CASAS-DNF model)
Kilingar, Nanda Gopala. "Generation and data-driven upscaling of open foam representational volume elements." Doctoral thesis, Universite Libre de Bruxelles, 2021. https://dipot.ulb.ac.be/dspace/bitstream/2013/313595/4/toc.pdf.
Full textDans ce travail, un générateur de volumes élémentaires représentatifs (VER) basé sur les champs de distance d'un agrégat d'inclusions de forme arbitraire est développé dans le cadre de matériaux moussés à structure ouverte. Lorsque les inclusions sont sphériques, la tessellation de l'agrégat résulte en des morphologies similaires aux échantillons de mousse physique en termes de rapports des nombres de face par pores et de bords par faces, ainsi que de la distribution de la longueur des entretoises, entre autres. Les fonctions qui combinent les champs de distance peuvent être utilisées pour obtenir des tesselations avec les variations nécessaires aux géométries des entretoises et extraire ces morphologies de mousse ouverte. Il est également possible de remplacer l'agrégat d'inclusions par un ensemble prédéfini d'inclusions qui sont directement extraites d'images tomographiques.L'utilisation de fonctions de niveaux discrètes entraîne de fortes discontinuités dans les dérivées des champs de distance. Une approche basée sur des ensembles de niveaux multiples est présentée qui peut capturer de manière appropriée les arêtes vives des entretoises des mousses ouvertes à partir des champs de distance résultants. Une telle approche peut contourner les discontinuités présentées par les champs de distance qui pourraient conduire à des concentrations de contraintes parasites dans une analyse ducomportement des matériaux.Les pores individuels sont ensuite extraits en tant que surfaces d'inclusions sur la base desdites combinaisons des fonctions de distance et de leurs modifications. Ces surfaces peuvent être réunies pour obtenir la géométrie finale des morphologies de mousse ouverte. Les attributs physiques des géométries extraites sont comparés aux données expérimentales. Une comparaison statistique est présentée décrivant les différentes caractéristiques. L'étude est étendue aux morphologies qui ont été extraites à l'aide d'images tomographiques.À l'aide d'outils d'optimisation de maillage, les triangulations des surfaces peuvent être obtenues, fusionnées et développées sous forme de modèles d'éléments finis (FE). Les modèles sont prêts à être utilisés dans une étude multi-échelle pour obtenir le comportement homogénéisé du matériau. La mise à l'échelle peut aider à évaluer les applications pratiques de ces modèles en les comparant aux données expérimentales d'échantillons physiques. Le comportement des matériaux des VERs est également comparé aux observations expérimentales.Pour augmenter l'efficacité de calcul de l'étude, un modèle de substitution basé sur un réseau neuronal est présenté. Ce modèle peut remplacer le problème aux valeurs limites à l'échelle micro dans une analyse multi-échelle. Les réseaux de neurones sont construits à l'aide de modules spécialement conçus pour prédire le comportement dépendant de l'histoire et sont appelés réseaux de neurones récurrents (RNN). Les modèles de substitution sont entrainés pour prendre en compte le caractère aléatoire du chargement que subit un matériau complexe lors d'une analyse de comportement d'un matériau.
Doctorat en Sciences de l'ingénieur et technologie
info:eu-repo/semantics/nonPublished
Schmidt, Helmut. "Interface dynamics in neural field models." Thesis, University of Nottingham, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.597110.
Full textBonaccini, Calia Andrea. "Graphene field-effect transistors as flexible neural interfaces for intracortical electrophysiology." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/671635.
Full textEn los últimos años se han producido nuevos desarrollos tecnológicos en el campo de los implantes neuronales para aplicaciones médicas. La comprensión del cerebro humano se considera uno de los mayores desafíos científicos de nuestro tiempo; como consecuencia, estamos siendo testigos de una intensificación de la investigación en el desarrollo de las interfaces cerebro-máquina (IMC) para leer y estimular la actividad cerebral. No obstante, los implantes neuronales actualmente disponibles ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantea la invasividad de los materiales. Esos materiales comprometen la resolución de la interfaz, el rendimiento y la estabilidad a largo plazo de los implantes neurales. El desarrollo de una electrónica flexible que utilice materiales biocompatibles es clave para la realización de implantes neuronales mínimamente invasivos que puedan implantarse de forma crónica. Un campo de investigación muy prometedor es el uso de materiales bidimensionales, como el grafeno, para aplicaciones bioelectrónicas. El transistor de efecto de campo en solución de grafeno (gSGFET) es una de dichas nuevas tecnologías neurales emergentes. Estos dispositivos pueden superar las limitaciones mencionadas anteriormente gracias a las extraordinarias propiedades del grafeno, como su alta flexibilidad mecánica, estabilidad electroquímica, biocompatibilidad y sensibilidad. En esta tesis doctoral, se han fabricado matrices de gSGFET y se han optimizado iterativamente en términos de sensibilidad y relación señal/ruido, adoptando métodos de microfabricación a escala de oblea. Se ha caracterizado el ruido 1/f en los gSGFETs y optimizado haciendo un tratamiento UVO en la interfaz metal/grafeno y desacoplando el canal de grafeno del sustrato utilizando diferentes nanomateriales como la encapsulación con nitruro de boro hexagonal (hBN), monocapas autoensambladas y bicapas de grafeno. Además, se han fabricado con éxito sondas neurales epicorticales e intracorticales flexibles con matrices de gSGFET y se han utilizado durante las medidas de microelectrocorticografía in vivo en roedores. Se han insertado dispositivos intracorticales flexibles en el cerebro utilizando un protocolo de refuerzo de la capa posterior de los dispositivos con proteína de fibroína de seda biorresistente. Los resultados presentados en esta tesis demuestran la superior resolución espacio-temporal de los gSGFET en comparación con la tecnología estándar de microelectrodos; en particular, referente a la capacidad de mapear con alta fidelidad, la actividad de muy baja frecuencia (ISA, < 0,1 Hz) junto con las señales en el típico ancho de banda LFP. Hoy en día se sabe que la actividad cerebral de muy baja frecuencia, contribuye a la fisiopatología de varios trastornos neurológicos como el derrame cerebral, la lesión cerebral traumática, la migraña y la epilepsia. Sin embargo, esta actividad rara vez se registra debido a las limitaciones técnicas intrínsecas de los electrodos convencionales acoplados a la CA. Se han obtenido registros con sondas neuronales de profundidad de grafeno (gDNP) en modelos animales de epilepsia. Se detectó ISA a través de diferentes capas corticales y regiones subcorticales, registrando simultáneamente la actividad epiléptica en bandas de frecuencia más convencionales (1-600Hz). Además, se ha demostrado también la evaluación de la estabilidad y funcionalidad en registros crónicos, así como la biocompatibilidad del gDNP. La tecnología bioelectrónica basada en el grafeno aquí descrita tiene el potencial de convertirse en una herramienta de referencia para la electrofisiología de ancho de banda completo. Se prevé que esta tecnología tenga un gran impacto en una comunidad amplia y multidisciplinaria que incluya investigadores en neurotecnología, ingenieros biomédicos, neurocientíficos que estudien la dinámica cortical de banda ancha asociada con el comportamiento espontáneo y/o los estados cerebrales, así como investigadores clínicos interesados en la actividad de baja frecuencia en la epilepsia, los accidentes cerebrovasculares y la migraña.
Recent years have witnessed novel technology developments of neural implants for medical applications which are expected to pave the way to unveil functionalities of the central nervous system. Understanding the human brain is commonly considered one of the biggest scientific challenges of our time; as a consequence, we are witnessing an intensified research in the development of brain-machine-interfaces (BMIs), which would allow us to both read and stimulate brain activity. Nevertheless, currently available neural implants offer a modest clinical efficacy, partly due to the limitations posed by the invasiveness of the implants materials and technology and by the metals used at the electrical interface with the tissue. Such materials compromise the interfacing resolution, the performance and the long term stability of neural implants. Development of flexible electronics using biocompatible materials is key for the realisation of minimally invasive neural implants, which can be chronically implanted without causing rejection from the immune system. A relatively young yet very promising research field, that is increasingly drawing attention is the use of two dimensional materials, such as graphene, for bioelectronic applications. Graphene solution-gated field effect transistor (gSGFET) is one of several emerging new neural technologies. These devices can overcome the above-mentioned limitations thanks to the outstanding properties of graphene, such as mechanical flexibility, electrochemical inertness, biocompatibility and high sensitivity. In this PhD thesis, arrays of gSGFETs have been fabricated and iteratively optimized in terms of sensitivity and signal-to-noise ratio, adopting wafer-scale micro-fabrication methods. The 1/f noise in gSGFETs has been characterised and the optimisation of both, contact and channel noises was achieved by UVO-treatment at the metal/graphene interface, as well as by decoupling the graphene channel from the substrate, using different nanomaterials such as graphene encapsulation with hexagonal boron nitride (hBN), self assembled monolayers and double transferred graphene. Moreover, flexible and ultra-thin epicortical and intracortical neural probes, containing arrays of gSGFETs, have been successfully fabricated and used during in vivo microelectrocorticography recordings in anaesthesized and awake rodents. Flexible intracortical devices were inserted into the brain using a back-coating stiffening protocol with bioresobable silk fibroin protein, developed during this PhD thesis. The results presented in this PhD demonstrate the superior spatio-temporal resolution of gSGFETs compared to standard microelectordes technology; particularly the ability to map with high fidelity, infraslow activity (ISA, < 0.1 Hz) together with signals in the typical local field potential bandwidth. Today it is known that infraslow brain activity, including spreading depolarisations, contribute to the pathophysiology of several neurological disorders such as stroke, traumatic brain injury, migraine and epilepsy. However, this activity is seldom recorded due to intrinsic technical limitations of conventional AC-coupled electrodes. To demonstrate the usefulness of the developed flexible gSGFET arrays technology, recordings have been obtained with multichannel flexible graphene depth neural probes (gDNP) in relevant awake animal models of seizures and established epilepsy. ISA was detected and mapped through different cortical layers and subcortical regions, whilst simultaneously recording epileptiform activity in more conventional frequency bands (1-600Hz). Furthermore, the assessment of the long term recording stability and functionality, as well as biocompatibility of the gDNP has also been demonstrated as part of this thesis. The graphene based bioelectronic technology here described has the potential to become a gold standard tool for full bandwidth electrophysiology. This technology is envisioned to have a great impact on a broad and multidisciplinary community including neurotechnology researchers, biomedical engineers, neuroscientists studying wide-band cortical dynamics associated with spontaneous behaviour and/or brain states, as well as clinical researchers interested in the role of infraslow activity in epilepsy, stroke and migraine.
Martí, Edo Albert. "Transmissió de potencials d'acció basada en la propagació de camps elèctrics intra-membrana." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/457974.
Full textLes actuals teories i mecanismes de propagació de l'impuls nerviós per l'interior de les neurones no donen una explicació suficientment satisfactòria ni als principis físics als quals estan subjectes, ni als processos nanomètrics que es produeixen al seu interior. La propagació a les zones dendrítiques i als axons mielinitzats i no mielinitzats presenten efectes i esdeveniments que si bé el càlcul numèric dels actuals models concorda amb els resultats biològics obtinguts amb mesures macroscòpiques, estan lluny de donar una explicació al funcionament real dels mecanismes biològics i als efectes i comportaments que presenten la resta d'elements biològics participants que hi donen suport. Els actuals coneixements sobre l'estructura i comportament de les membranes biològiques, així com el coneixement sobre l'estructura i el funcionament dels canals iònics regulats per voltatge, ens permet plantejar una opció alternativa als mecanismes existents de propagació, donant explicació als efectes i comportaments tant des d'una escala nanomètrica com des d'una escala macroscòpica. En aquest treball teòric es proposa la propagació de camps elèctrics intra-membrana com un mecanisme possible de propagació amb la capacitat de donar explicació tant als efectes macroscòpics com als nanomètrics. Amb les teories i mecanismes actuals, la generació, la propagació i la reemissió de l'impuls nerviós es fonamenta en un efecte anomenat potencial d'acció. El potencial d'acció el podem definir com el desequilibri temporal que pateix el potencial de membrana, durant la propagació de l'impuls nerviós, i que és conseqüència de l'obertura dels canals iònics. El progrés en el coneixement sobre les estructures biològiques existents a les membranes neuronals ha permès plantejar una nova hipòtesi sobre el funcionament del potencial d'acció. A més de la variació del potencial de membrana, la càrrega elèctrica que travessa la membrana genera un camp elèctric que es propaga per l'interior de la membrana cel·lular, activant els canals iònics propers. Aquest mecanisme aporta una explicació consistent, compatible amb els components biològics existents i amb altres efectes difícilment explicables amb els mecanismes estàndard actuals. A més, el mecanisme de propagació per camp elèctric intra-membrana proposat permet donar una explicació diferent de l'evolució dels potencials d'acció, així com un nivell de tràfic iònic més en consonància amb les capacitats de la realitat biològica, tant en el nivell de tràfic existent com en el consum energètic. El mecanisme proposat permet explicar l'anomenada propagació saltatòria del potencial d'acció dels axons mielinitzats, i també aplicar la propagació saltatòria als axons no mielinitzats. En aquest cas com una propagació saltatòria de salts curts; per als axons mielinitzats, com una propagació saltatòria de salts llargs, donant també una resposta coherent a la propagació en temps constant independent de la distància entre nodes de Ranvier. El mecanisme proposat permet plantejar que els efectes que presenta el potencial d'acció són més el resultat d'un desplaçament de càrrega a l'interior dels canals de sodi i de potassi, que d'un corrent que realment travessa els canals iònics. Un factor important a tenir en compte en el mecanisme proposat és la minimització del cost energètic, premissa fonamental de la biologia per a la màxima optimització en el funcionament de processos.
Zhao, Xue Long. "Bursting Dynamics in Corticothalamic Neural-Field Theory." Thesis, The University of Sydney, 2015. http://hdl.handle.net/2123/14201.
Full textCanning, Andrew Magnus. "Ising spin models of partially connected neural networks." Thesis, University of Edinburgh, 1988. http://hdl.handle.net/1842/13304.
Full textHurley, S. J. "Neutral hydrogen observations of three fields of galaxies." Thesis, University of Manchester, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.376579.
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