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Статті в журналах з теми "Graphene neurons"
Sakai, Koji, Tetsuhiko F. Teshima, Hiroshi Nakashima, and Yuko Ueno. "Graphene-based neuron encapsulation with controlled axonal outgrowth." Nanoscale 11, no. 28 (2019): 13249–59. http://dx.doi.org/10.1039/c9nr04165f.
Повний текст джерелаD'Abaco, Giovanna M., Cristiana Mattei, Babak Nasr, Emma J. Hudson, Abdullah J. Alshawaf, Gursharan Chana, Ian P. Everall, Bryony Nayagam, Mirella Dottori, and Efstratios Skafidas. "Graphene foam as a biocompatible scaffold for culturing human neurons." Royal Society Open Science 5, no. 3 (March 2018): 171364. http://dx.doi.org/10.1098/rsos.171364.
Повний текст джерелаMarquez, Bicky A., Hugh Morison, Zhimu Guo, Matthew Filipovich, Paul R. Prucnal, and Bhavin J. Shastri. "Graphene-based photonic synapse for multi wavelength neural networks." MRS Advances 5, no. 37-38 (2020): 1909–17. http://dx.doi.org/10.1557/adv.2020.327.
Повний текст джерелаSahni, Deshdeepak, Andrew Jea, Javier A. Mata, Daniela C. Marcano, Ahilan Sivaganesan, Jacob M. Berlin, Claudio E. Tatsui, et al. "Biocompatibility of pristine graphene for neuronal interface." Journal of Neurosurgery: Pediatrics 11, no. 5 (May 2013): 575–83. http://dx.doi.org/10.3171/2013.1.peds12374.
Повний текст джерелаRawat, Sonali, Krishan Gopal Jain, Deepika Gupta, Pawan Kumar Raghav, Rituparna Chaudhuri, Pinky, Adeeba Shakeel, et al. "Graphene nanofiber composites for enhanced neuronal differentiation of human mesenchymal stem cells." Nanomedicine 16, no. 22 (September 2021): 1963–82. http://dx.doi.org/10.2217/nnm-2021-0121.
Повний текст джерелаTasnim, Nishat, Vikram Thakur, Munmun Chattopadhyay, and Binata Joddar. "The Efficacy of Graphene Foams for Culturing Mesenchymal Stem Cells and Their Differentiation into Dopaminergic Neurons." Stem Cells International 2018 (June 3, 2018): 1–12. http://dx.doi.org/10.1155/2018/3410168.
Повний текст джерелаBendali, Amel, Lucas H. Hess, Max Seifert, Valerie Forster, Anne-Fleur Stephan, Jose A. Garrido, and Serge Picaud. "Purified Neurons can Survive on Peptide-Free Graphene Layers." Advanced Healthcare Materials 2, no. 7 (January 8, 2013): 929–33. http://dx.doi.org/10.1002/adhm.201200347.
Повний текст джерелаSingaraju, Surya A., Dennis D. Weller, Thurid S. Gspann, Jasmin Aghassi-Hagmann, and Mehdi B. Tahoori. "Artificial Neurons on Flexible Substrates: A Fully Printed Approach for Neuromorphic Sensing." Sensors 22, no. 11 (May 25, 2022): 4000. http://dx.doi.org/10.3390/s22114000.
Повний текст джерелаDiFrancesco, Mattia L., Elisabetta Colombo, Ermanno D. Papaleo, José Fernando Maya-Vetencourt, Giovanni Manfredi, Guglielmo Lanzani, and Fabio Benfenati. "A hybrid P3HT-Graphene interface for efficient photostimulation of neurons." Carbon 162 (June 2020): 308–17. http://dx.doi.org/10.1016/j.carbon.2020.02.043.
Повний текст джерелаBaek, Soonbong, Jaesur Oh, Juhyun Song, Hwan Choi, Junsang Yoo, Gui-Yeon Park, Jin Han, et al. "Generation of Integration-Free Induced Neurons Using Graphene Oxide-Polyethylenimine." Small 13, no. 5 (November 7, 2016): 1601993. http://dx.doi.org/10.1002/smll.201601993.
Повний текст джерелаДисертації з теми "Graphene neurons"
CONVERTINO, Domenica. "Interfacing graphene with peripheral neurons: influence of neurite outgrowth and NGF axonal transport." Doctoral thesis, Scuola Normale Superiore, 2020. http://hdl.handle.net/11384/90468.
Повний текст джерелаVeliev, Farida. "Interfacing neurons with nanoelectronics : from silicon nanowires to carbon devices." Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAI001/document.
Повний текст джерелаIn line with the technological progress of last decades a variety of adapted bioelectrical interfaces was developed to record electrical activity from the nervous system reaching from whole brain activity to single neuron signaling. Although neural interfaces have reached clinical utility and are commonly used in fundamental neuroscience, their performance is still limited. In this work we investigated alternative materials and techniques, which could improve the monitoring of neuronal activity of cultured networks, and the long-term performance of prospective neuroprosthetics. While silicon nanowire transistor arrays and diamond based microelectrodes are proposed for improving the spatial resolution and the electrode stability in biological environment respectively, the main focus of this thesis is set on the evaluation of graphene based field effect transistor arrays for bioelectronics. Due to its outstanding electrical, mechanical and chemical properties graphene appears as a promising candidate for the realization of chemically stable flexible electronics required for long-term neural interfacing. Here we demonstrate the outstanding neural affinity of pristine graphene and the realization of highly sensitive fast graphene transistors for neural interfaces
Turco, Antonio. "Use of carbon nanotubes for novel approaches towards spinal network repairing." Doctoral thesis, Università degli studi di Trieste, 2013. http://hdl.handle.net/10077/8663.
Повний текст джерелаNanotechnology underwent a very rapid development in the last decades, thanks to the invention of different techniques that allow reaching the nanoscale. The great interest in this area arises from the variety of possible applications in different fields, such as electronics, where the miniaturization of components is a key factor, but also medicine. The creation of smart systems able to carry out a specific task in the body in a controlled way, either in diagnosis or therapy or tissue engineering, is the ultimate goal of a newborn area of research, called nanomedicine. In fact, to reach such an outstanding objective, a nanometer‐sized material is needed and carbon nanotubes (CNTs) are among the most promising candidates. The aim of this thesis was to study this opportunity and, in particular, the possible application of carbon nanotubes for spinal network repairing. After a review of the main features of neuronal network systems and the most common techniques to study their functionality, possible applications of nanotechnology for nanomedicine purposes are considered, focusing the attention on CNTs as neuronal interface in nerve tissue engineering. The work can be divided into two big parts. In the first part the impact of carbon nanotubes on various neuronal systems was studied. Different form of carbonaceous materials (carbon nanotubes, nanohorns and graphene) were deposited in a homogeneous way on a glass surface playing with organic functionalization and different deposition techniques. Hippocampal neuronal cells were grown on their surface to better understand how morphology and conductivity of the material could influence the activity of the neuronal network evidencing how both these characteristics could affect the electrophysiological properties of neurons. Then, also spinal neurons were grown on carbon nanotubes network deposited on a glass substrate to evaluate, for the first time, the impact of carbon nanotubes on this kind of cells. The tight interaction between these two materials appeared to cause a faster maturation of the spinal neurons with respect II to the control grown on a glass substrate. The long-term impact on a complex tissue (spinal cord slice) grown on carbon nanotubes carpet was also studied. The intimate interaction between the two materials observed by TEM and SEM analysis caused an increase in dimensions and number of neuronal fibers that comes out from the body of a spinal cord slice. An increase in electrophysiological activity of all neuronal network of the slice was also reported. In the second part of the work different conductive biocompatible nanocomposite materials based on carbon nanotubes and “artificial” polymers (such as Nafion, PVA, PET, PEI, PDMS and PANI) were investigated. The idea is to test these materials as neuronal prosthesis to repair spinal cord damage. All the prepared scaffolds showed CNTs on the surface favoring CNTs-neurons interaction. To address this aim different techniques and different organic functionalizations of CNTs were utilized to control supramolecular interactions between the nanomaterial and polymers orienting the deposition of the CNTs and preventing their aggregation. After that, an innovative method to study the possible ability of this nanocomposite materials to transmit a neuronal signal between two portions of spinal cord was designed. Functionalization of gold surfaces with thiolated carbon nanotubes have been conducted in order to develop suitable devices for neuronal stimulation and consequent spinal cord lesions repairing. In particular thiol groups were introduced on the graphitic surface of carbon nanotubes by means of covalent functionalization. First of all, the interaction of CNTs with gold nanoparticles has been evaluated, then a gold surface has been coated by means of contact printing technique with a homogeneous film of CNTs. This hybrid material could be useful to produce innovative electrodes for neuronal stimulation
XXV Ciclo
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Bourrier, Antoine. "Bioélectronique graphène pour un interfaçage neuronal in-vivo durable." Thesis, Université Grenoble Alpes (ComUE), 2017. http://www.theses.fr/2017GREAY011/document.
Повний текст джерелаGraphene, an atomically thin layer of carbon, is investigated as a biosensing andcoating material in order to address the long term durability issues of invasive intracorticalimplants. These devices are essential tools to record specific single motor neurons activity formedical applications aiming at healing neural injuries. Today’s implants suffer from their highinvasiveness. It is responsible for local inflammation that leads to the failure in unique neuronsactivity recordings in the motor cortex on a long term basis. By combining a monolayergraphene growth and transfer with an ultra-sensitive electronic integration and a biochemicalfunctionalization, this thesis proposes a new multidisciplinary approach to build intracorticalimplants with an improved bioacceptance. By using innovative methods of grapheneintegration in implants, and in-vitro and in-vivo studies to assess the reactions of living tissuesto graphene, we provide an overview of graphene’s potential contribution to future brainmachine interfaces for long term medical projects
Viana, Casals Damià. "EGNITE: Engineered Graphene for Neural Interface." Doctoral thesis, Universitat Autònoma de Barcelona, 2021. http://hdl.handle.net/10803/673330.
Повний текст джерелаLa tecnología de implantes neuronales en medicina tiene como objetivo restaurar la funcionalidad del sistema nervioso en casos de degeneración o daño grave registrando o estimulando la actividad eléctrica del tejido nervioso. Los implantes neurales disponibles actualmente ofrecen una eficacia clínica modesta, en parte debido a las limitaciones que plantean los metales utilizados en la interfaz eléctrica con el tejido. Dichos materiales comprometen la resolución de la interfaz y, por lo tanto, la restauración funcional con el rendimiento y la estabilidad. En este trabajo presento unos implantes neuronales flexibles basados en una película delgada de grafeno poroso nanoestructurado y biocompatible que proporciona una interfaz neural bidireccional estable y de alto rendimiento. En comparación con los dispositivos de microelectrodos de platino estándar, electrodos de 25 μm de diámetro basados en grafeno ofrecen una impedancia significativamente menor y pueden inyectar de forma segura 200 veces más carga durante más de 100 millones de pulsos. Aquí evaluo sus capacidades in vivo registrando actividad epicortical con alta fidelidad y alta resolución, estimulando subconjuntos de axones dentro del nervio ciático con umbrales de corriente bajos y alta selectividad y modulando la actividad de la retina con alta precisión. La tecnología de película fina de grafeno aquí descrita tiene el potencial de convertirse en el nuevo punto de referencia para la próxima generación de tecnología de implantes neuronales.
Neural implants technology in medicine aims to restore nervous system functionality in cases of severe degeneration or damage by recording or stimulating the electrical activity of the nervous tissue. Currently available neural implants offer a modest clinical efficacy partly due to the limitations posed by the metals used at the electrical interface with the tissue. Such materials compromise interfacing resolution, and therefore functional restoration, with performance and stability. In this work, I present flexible neural implants based on a biocompatible nanostructured porous graphene thin film that provides a stable and high performance bidirectional neural interface. Compared to standard platinum microelectrode devices, the graphene-based electrodes of 25 μm diameter offer significantly lower impedance and can safely inject 200 times more charge for more than 100 million pulses. I assessed their performance in vivo by recording high fidelity and high resolution epicortical activity, by stimulating subsets of axons within the sciatic nerve with low thresholds and high selectivity and by modulating the retinal activity with high precision. The graphene thin film technology I describe here has the potential to become the new performance benchmark for the next generation of neural implant technology.
Universitat Autònoma de Barcelona. Programa de Doctorat en Enginyeria Electrònica i de Telecomunicació
Bonaccini, 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.
Повний текст джерелаEn 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.
EL, MERHIE AMIRA. "Single Layer Graphene Biointerface: Studying Neuronal Network Development and Monitoring Cell Behavior over Time." Doctoral thesis, Università degli studi di Genova, 2019. http://hdl.handle.net/11567/939896.
Повний текст джерелаLimnios, Stratis. "Graph Degeneracy Studies for Advanced Learning Methods on Graphs and Theoretical Results Edge degeneracy: Algorithmic and structural results Degeneracy Hierarchy Generator and Efficient Connectivity Degeneracy Algorithm A Degeneracy Framework for Graph Similarity Hcore-Init: Neural Network Initialization based on Graph Degeneracy." Thesis, Institut polytechnique de Paris, 2020. http://www.theses.fr/2020IPPAX038.
Повний текст джерелаExtracting Meaningful substructures from graphs has always been a key part in graph studies. In machine learning frameworks, supervised or unsupervised, as well as in theoretical graph analysis, finding dense subgraphs and specific decompositions is primordial in many social and biological applications among many others.In this thesis we aim at studying graph degeneracy, starting from a theoretical point of view, and building upon our results to find the most suited decompositions for the tasks at hand.Hence the first part of the thesis we work on structural results in graphs with bounded edge admissibility, proving that such graphs can be reconstructed by aggregating graphs with almost-bounded-edge-degree. We also provide computational complexity guarantees for the different degeneracy decompositions, i.e. if they are NP-complete or polynomial, depending on the length of the paths on which the given degeneracy is defined.In the second part we unify the degeneracy and admissibility frameworks based on degree and connectivity. Within those frameworks we pick the most expressive, on the one hand, and computationally efficient on the other hand, namely the 1-edge-connectivity degeneracy, to experiment on standard degeneracy tasks, such as finding influential spreaders.Following the previous results that proved to perform poorly we go back to using the k-core but plugging it in a supervised framework, i.e. graph kernels. Thus providing a general framework named core-kernel, we use the k-core decomposition as a preprocessing step for the kernel and apply the latter on every subgraph obtained by the decomposition for comparison. We are able to achieve state-of-the-art performance on graph classification for a small computational cost trade-off.Finally we design a novel degree degeneracy framework for hypergraphs and simultaneously on bipartite graphs as they are hypergraphs incidence graph. This decomposition is then applied directly to pretrained neural network architectures as they induce bipartite graphs and use the coreness of the neurons to re-initialize the neural network weights. This framework not only outperforms state-of-the-art initialization techniques but is also applicable to any pair of layers convolutional and linear thus being applicable however needed to any type of architecture
Albano, Alice. "Dynamique des graphes de terrain : analyse en temps intrinsèque." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066260/document.
Повний текст джерелаWe are surrounded by a multitude of interaction networks from different contexts. These networks can be modeled as graphs, called complex networks. They have a community structure, i.e. groups of nodes closely related to each other and less connected with the rest of the graph. An other phenomenon studied in complex networks in many contexts is diffusion. The spread of a disease is an example of diffusion. These phenomena are dynamic and depend on an important parameter, which is often little studied: the time scale in which they are observed. According to the chosen scale, the graph dynamics can vary significantly. In this thesis, we propose to study dynamic processes using a suitable time scale. We consider a notion of relative time which we call intrinsic time, opposed to "traditional" time, which we call extrinsic time. We first study diffusion phenomena using intrinsic time, and we compare our results with an extrinsic time scale. This allows us to highlight the fact that the same phenomenon observed at two different time scales can have a very different behavior. We then analyze the relevance of the use of intrinsic time scale for detecting dynamic communities. Comparing communities obtained according extrinsic and intrinsic scales shows that the intrinsic time scale allows a more significant detection than extrinsic time scale
Hérault, Laurent. "Réseaux de neurones récursifs pour l'optimisation combinatoire : application à la théorie des graphes et à la vision par ordinateur." Grenoble INPG, 1991. http://www.theses.fr/1991INPG0019.
Повний текст джерелаЧастини книг з теми "Graphene neurons"
Yadav, Divyansh, and Seema Nara. "Nanozymes for Neurodegenerative Diseases." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022), 77–95. Dordrecht: Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_9.
Повний текст джерелаSparvoli, Marina, Jonas S. Marma, Gabriel F. Nunes, and Fábio O. Jorge. "Operation of Neuronal Membrane Simulator Circuit for Tests with Memristor Based on Graphene and Graphene Oxide." In Advances in Computational Intelligence, 93–102. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85099-9_8.
Повний текст джерелаТези доповідей конференцій з теми "Graphene neurons"
Berta, Kiara, Reka Varga, Nina Gyorfi, Kristof Tenzlinger, Akos Odry, Peter Odry, Aleksandar Szechenyi, et al. "The development of graphene coated measuring plate for the investigation of “dark” neurons with electrical impedance spectrum (EIS) measurement." In 2021 IEEE 15th International Symposium on Applied Computational Intelligence and Informatics (SACI). IEEE, 2021. http://dx.doi.org/10.1109/saci51354.2021.9465541.
Повний текст джерелаKalita, Hirokjyoti, Adithi Krishnaprasad, Nitin Choudhary, Sonali Das, Hee-Suk Chung, Yeonwoong Jung, and Tania Roy. "Artificial Neuron using MoS2/Graphene Threshold Switching Memristors." In 2018 76th Device Research Conference (DRC). IEEE, 2018. http://dx.doi.org/10.1109/drc.2018.8443301.
Повний текст джерелаSavtchenko, Alex, Janaina Sena De Souza, Andrew Setiadi, Erin LaMontagne, Yuhui Li, Alysson Muotri, and Elena Molokanova. "Graphene-Mediated Optical Stimulation for Modulation of Neuronal Activity." In Light Actuators for Optical Stimulation of Living Systems. València: FUNDACIO DE LA COMUNITAT VALENCIANA SCITO, 2022. http://dx.doi.org/10.29363/nanoge.liv-act.2022.014.
Повний текст джерелаAydın, Kemal Bartu, Levent Aydin, and Fethullah Güneş. "Stochastic Optimization of TiO2-Graphene Nanocomposite by Using Neuro-Regression Approach for Maximum Photocatalytic Degradation Rate." In International Students Science Congress. Izmir International Guest Student Association, 2021. http://dx.doi.org/10.52460/issc.2021.044.
Повний текст джерелаWang, H., N. Cucu Laurenciu, Y. Jiang, and S. D. Cotofana. "Ultra-Compact, Entirely Graphene-Based Nonlinear Leaky Integrate-and-Fire Spiking Neuron." In 2020 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE, 2020. http://dx.doi.org/10.1109/iscas45731.2020.9181092.
Повний текст джерелаSparvoli, Marina, and Jonas S. Marma. "Development of resistive memories based on silver doped graphene oxide for neuron simulation." In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE, 2018. http://dx.doi.org/10.1109/ijcnn.2018.8489460.
Повний текст джерелаLee, Sohee, Chaejeong Heo, Si Young Lee, Young Hee Lee, and Minah Suh. "The enhancement of neuronal cells wound healing with non-contact electric field stimulation by graphene electrodes." In Nano-Bio Sensing, Imaging and Spectroscopy, edited by Shin Won Kang, Seung-Han Park, Luke P. Lee, Ki-Bong Song, and Yo Han Choi. SPIE, 2013. http://dx.doi.org/10.1117/12.2020770.
Повний текст джерела