Littérature scientifique sur le sujet « Artificial dendrite »
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Articles de revues sur le sujet "Artificial dendrite"
Jia, Dongbao, Weixiang Xu, Dengzhi Liu, Zhongxun Xu, Zhaoman Zhong et Xinxin Ban. « Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning ». Discrete Dynamics in Nature and Society 2022 (4 juillet 2022) : 1–14. http://dx.doi.org/10.1155/2022/3259222.
Texte intégralTanaka, Makito, Tetsuro Sasada, Tetsuya Nakamoto, Sascha Ansén, Osamu Imataki, Alla Berezovskaya, Marcus Butler, Lee Nadler et Naoto Hirano. « Immunogenicity of Artificial Dendritic Cells Is Upregulated by ROCK Inhibition-Mediated Dendrite Formation. » Blood 114, no 22 (20 novembre 2009) : 3022. http://dx.doi.org/10.1182/blood.v114.22.3022.3022.
Texte intégralLiu, Yang. « Overview of the Recent Progress of Suppressing the Dendritic Growth on Lithium Metal Anode for Rechargeable Batteries ». Journal of Physics : Conference Series 2152, no 1 (1 janvier 2022) : 012060. http://dx.doi.org/10.1088/1742-6596/2152/1/012060.
Texte intégralMu, Yanlu, Tianyi Zhou, Zhaoyi Zhai, Shuangbin Zhang, Dexing Li, Lan Chen et Guanglu Ge. « Metal organic complexes as an artificial solid-electrolyte interface with Zn-ion transfer promotion for long-life zinc metal batteries ». Nanoscale 13, no 48 (2021) : 20412–16. http://dx.doi.org/10.1039/d1nr05753g.
Texte intégralJing, Zhaokun, Yuchao Yang et Ru Huang. « Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors ». Semiconductor Science and Technology 37, no 2 (23 décembre 2021) : 024002. http://dx.doi.org/10.1088/1361-6641/ac3f21.
Texte intégralPeng, Hong, Tingting Bao, Xiaohui Luo, Jun Wang, Xiaoxiao Song, Agustín Riscos-Núñez et Mario J. Pérez-Jiménez. « Dendrite P systems ». Neural Networks 127 (juillet 2020) : 110–20. http://dx.doi.org/10.1016/j.neunet.2020.04.014.
Texte intégralBerger, Thomas, Matthew E. Larkum et Hans-R. Lüscher. « High I h Channel Density in the Distal Apical Dendrite of Layer V Pyramidal Cells Increases Bidirectional Attenuation of EPSPs ». Journal of Neurophysiology 85, no 2 (1 février 2001) : 855–68. http://dx.doi.org/10.1152/jn.2001.85.2.855.
Texte intégralZhang, Xiliang, Sichen Tao, Zheng Tang, Shuxin Zheng et Yoki Todo. « The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images ». Mathematics 11, no 12 (15 juin 2023) : 2715. http://dx.doi.org/10.3390/math11122715.
Texte intégralChakilam, Shashikanth, Dan Ting Li, Zhang Chuan Xi, Rimvydas Gaidys et Audrone Lupeikiene. « Morphological Study of Insect Mechanoreceptors to Develop Artificial Bio-Inspired Mechanosensors ». Engineering Proceedings 2, no 1 (14 novembre 2020) : 70. http://dx.doi.org/10.3390/ecsa-7-08199.
Texte intégralGong, Mingchen. « The growth mechanism and strategies of dendrite in lithium metal anode ». Highlights in Science, Engineering and Technology 83 (27 février 2024) : 533–37. http://dx.doi.org/10.54097/0wy2hf86.
Texte intégralThèses sur le sujet "Artificial dendrite"
Cheng, Long. « Relaxor ferroelectrics for neuromorphic computing ». Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST073.
Texte intégralTo overcome challenges posed by traditional von Neumann architectures, neuromorphic computing draws inspiration from brain science to create energy-efficient hardware adaptable to complex tasks. Memristors, though novel, face issues like Joule heat hindering ultra-low-power neural computing.To address this, we propose a memcapacitor mechanism - the electric-field-induced phase transition. Memcapacitors, expressing signals as voltage, offer lower power consumption than memristors (current-based). Our study on relaxor ferroelectric materials (PMN-28PT, PZN-4.5PT) and conventional ferroelectric BTO (001) demonstrates the universal nature ofelectric-field-induced phase transitions. Customized pulses enable the replication of long-term potentiation (LTP), depression (LTD), and spike-timing-dependent plasticity (STDP).Additionally, relaxor ferroelectrics exhibit a dendrite effect absent in conventional counterparts. Implementing PZN-4.5PT dendrites in neural networks improves accuracy (83.44%), surpassing memristor networks with linear dendrites (81.84%) and significantly outperforming networks without dendrites (80.1%).Ultimately, we successfully implement a relaxor memcapacitor using a PMN thin film.This metal/ferroelectric/metal/insulator structure achieves 3-bit capacitance states through field-induced phase transitions. 8 robust memcapacitive states exhibit consistent maintenance over 100 seconds and exceptional endurance exceeding 5×10^5cycles. Tailored pulses effectively emulate LTP and LTD, and enable the exploration of temperature-dependent synaptic functionalities
Chan, Erwin Pai Hsiung. « Immune reactivity to metal implants ». University of Western Australia. School of Anatomy and Human Biology, 2009. http://theses.library.uwa.edu.au/adt-WU2009.0194.
Texte intégralTakeda, Shigeo. « Functionalization of Glucan Dendrimers and Bio-applications ». Kyoto University, 2020. http://hdl.handle.net/2433/253505.
Texte intégralJanzakova, Kamila. « Développement de dendrites polymères organiques en 3D comme dispositif neuromorphique ». Electronic Thesis or Diss., Université de Lille (2022-....), 2023. http://www.theses.fr/2023ULILN017.
Texte intégralNeuromorphic technologies is a promising direction for development of more advanced and energy-efficient computing. They aim to replicate attractive brain features such as high computational efficiency at low power consumption on a software and hardware level. At the moment, brain-inspired software implementations (such as ANN and SNN) have already shown their successful application for different types of tasks (image and speech recognition). However, to benefit more from the brain-like algorithms, one may combine them with appropriate hardware that would also rely on brain-like architecture and processes and thus complement them. Neuromorphic engineering has already shown the utilization of solid-state electronics (CMOS circuits, memristor) for the development of brain-inspired devices. Nevertheless, these implementations are fabricated through top-down methods. In contrast, brain computing relies on bottom-up processes such as interconnectivity between cells and the formation of neural communication pathways.In the light of mentioned above, this work reports on the development of programmable 3D organic neuromorphic devices, which, unlike most current neuromorphic technologies, can be created in a bottom-up manner. This allows bringing neuromorphic technologies closer to the level of brain programming, where necessary neural paths are established only on the need.First, we found out that PEDOT:PSS based 3D interconnections can be formed by means of AC-bipolar electropolymerization and that they are capable of mimicking the growth of neural cells. By tuning individually the parameters of the waveform (peak amplitude voltage -VP, frequency - f, duty cycle - dc and offset voltage - Voff), a wide range of dendrite-like structures was observed with various branching degrees, volumes, surface areas, asymmetry of formation, and even growth dynamics.Next, it was discovered that dendritic morphologies obtained at various frequencies are conductive. Moreover, each structure exhibits an individual conductance value that can be interpreted as synaptic weight. More importantly, the ability of dendrites to function as OECT was revealed. Different dendrites exhibited different performances as OECT. Further, the ability of PEDOT:PSS dendrites to change their conductivity in response to gate voltage was used to mimic brain memory functions (short-term plasticity -STP and long-term plasticity -LTP). STP responses varied depending on the dendritic structure. Moreover, emulation of LTP was demonstrated not only by means of an Ag/AgCl gate wire but as well by means of a self-developed polymer dendritic gate.Finally, structural plasticity was demonstrated through dendritic growth, where the weight of the final connection is governed according to Hebbian learning rules (spike-timing-dependent plasticity - STDP and spike-rate-dependent plasticity - SRDP). Using both approaches, a variety of dendritic topologies with programmable conductance states (i.e., synaptic weight) and various dynamics of growth have been observed. Eventually, using the same dendritic structural plasticity, more complex brain features such as associative learning and classification tasks were emulated.Additionally, future perspectives of such technologies based on self-propagating polymer dendritic objects were discussed
Almeida, Fernando Mendonça de. « Autoproteção para a internet das coisas ». Universidade Federal de Sergipe, 2016. https://ri.ufs.br/handle/riufs/3361.
Texte intégralThe Internet of Things is a new paradigm of communication based on the ubiquitous presence of objects that, having unique address, they can cooperate with their peers to achieve a common goal. Applications in several areas can benefit from this new paradigm, but the Internet of Things is very vulnerable to attack. The large number of connected devices make an autonomic approach necessary and the small amount of resources requires the use of efficient techniques. This paper proposes a self-protection architecture for the Internet of Things using Artificial Neural Network and Dendritic Cells Algorithm, two bio-inspired techniques. The experiments of this paper show that the use of these two techniques is possible. The Artificial Neural Network implementation consume a small memory footprint, having a high accuracy rate and the Dendritic Cells Algorithm show to be interesting for it distributivity, allowing better use of network resources.
A Internet das Coisas é um novo paradigma de comunicação baseado na presença ubíqua de objetos que, através de endereçamento único, cooperam com seus pares para atingir um objetivo em comum. Aplicações em diversas áreas podem se beneficiar dos conceitos da Internet das Coisas, porém esta rede é muito vulnerável a ataques, seja pela possibilidade de ataque físico, pela alta conectividade dos dispositivos, a enorme quantidade de dispositivos conectados ou a baixa quantidade de recursos disponíveis. A grande quantidade de dispositivos conectados faz com que abordagens autonômicas sejam necessárias e a reduzida quantidade de recursos exige a utilização de técnicas eficientes. Este trabalho propõe uma arquitetura de autoproteção para a Internet das Coisas utilizando as técnicas de Rede Neural Artificial e Algoritmo de Células Dendríticas, duas técnicas bio-inspiradas que, através de experimentos, mostraram a possibilidade de serem utilizadas na Internet das Coisas. A implementação da Rede Neural Artificial utilizada consumiu poucos recursos de memória do dispositivo, mantendo uma alta taxa de acerto, comparável a trabalhos correlatos que não se preocuparam com o consumo de recursos. A utilização do Algoritmo de Células Dendríticas se mostrou interessante pela sua distributividade, permitindo uma melhor utilização dos recursos da rede, como um todo.
Lin, Yu-Sheng, et 林侑陞. « Synthesis of Peptide Conjugated Poly(amidoamine) Dendrimer as Artifical Racemerase ». Thesis, 2010. http://ndltd.ncl.edu.tw/handle/97525607925182174395.
Texte intégral高雄醫學大學
醫藥暨應用化學研究所
98
Pyridoxal 5′-Phosphate (PLP) is the active member of of vitamin B6. PLP are known to perform numbers of reactivities in a variety of enzymes in which the lysine is a conserved residue for harboring PLP via Schiff base moiety. This is also known as external aldimine. During the course of reaction, the inbound substrate will form new Schiff base with PLP, and known as external aldimine. The exchange between external and internal aldimine is important for the demonstration of reactions. Base on the previous experimental results, we design a tripeptide involving lysine to modify the surface of PAMAM dendrimer for binding the Pyridoxal 5′-Phosphate. The designed peptides are Phe-Lys-X. The aromatic ring of phenylamine enhances the binding through PLP by?n???{???ninteraction. By the same reason, histidine, tryptophan, or tyrosine are chosen to be the third residue. During the synthesis of peptide, we found the protecting group is crucial to the solubility of those tripeptides. Those with Fmoc protecting group exhibit poor solubility. (G; 4, 5, 7)-dendri-PAMAM-(APO-Phe-Lys)n was selected for the investigation of rasemization. Under basic condition, the racemization was monitered by HPLC analysis. This result proves the ability of those synthetic dendrimers as catalyst of racemization.
Chapitres de livres sur le sujet "Artificial dendrite"
Rouw, Eelco, Jaap Hoekstra et Arthur H. M. van Roermund. « An artificial dendrite using active channels ». Dans Lecture Notes in Computer Science, 176–87. Berlin, Heidelberg : Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100484.
Texte intégralBell, Tony. « Artificial dendritic learning ». Dans Neural Networks, 161–74. Berlin, Heidelberg : Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_37.
Texte intégralJia, Huijue. « Memory in Dendritic Spines ». Dans Neuroscience for Artificial Intelligence, 85–112. New York : Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-5.
Texte intégralHerreras, O., J. M. Ibarz, L. López-Aguado et P. Varona. « Dendrites : The Last-Generation Computers ». Dans Connectionist Models of Neurons, Learning Processes, and Artificial Intelligence, 1–13. Berlin, Heidelberg : Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45720-8_1.
Texte intégralChelly, Zeineb, Abir Smiti et Zied Elouedi. « COID-FDCM : The Fuzzy Maintained Dendritic Cell Classification Method ». Dans Artificial Intelligence and Soft Computing, 233–41. Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29350-4_28.
Texte intégralOhme, M., et A. Schierwagen. « A reduced model for dendritic trees with active membrane ». Dans Artificial Neural Networks — ICANN 96, 691–96. Berlin, Heidelberg : Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_117.
Texte intégralMöller, Ralf, et Horst-Michael Groß. « Possible Functional Roles of the Bipartite Dendrites of Pyramidal Cells ». Dans Neural Networks : Artificial Intelligence and Industrial Applications, 51–54. London : Springer London, 1995. http://dx.doi.org/10.1007/978-1-4471-3087-1_9.
Texte intégralPanchev, Christo, Stefan Wermter et Huixin Chen. « Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites ». Dans Artificial Neural Networks — ICANN 2002, 896–901. Berlin, Heidelberg : Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_145.
Texte intégralVandesompele, Alexander, Francis Wyffels et Joni Dambre. « Dendritic Computation in a Point Neuron Model ». Dans Artificial Neural Networks and Machine Learning – ICANN 2020, 599–609. Cham : Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61616-8_48.
Texte intégralSommerkorn, G., U. Seiffert, D. Surmeli, A. Herzog, B. Michaelis et K. Braun. « Classification of 3-D Dendritic Spines using Self-Organizing Maps ». Dans Artificial Neural Nets and Genetic Algorithms, 129–32. Vienna : Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_28.
Texte intégralActes de conférences sur le sujet "Artificial dendrite"
Nakagawa, K., T. Takaki, Y. Morita et E. Nakamachi. « 2D Phase-Field Analyses of Axonal Extension of Nerve Cell ». Dans ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64281.
Texte intégralHutchinson, Zachary. « Artificial Dendrites : an Algorithm ». Dans 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2020. http://dx.doi.org/10.1109/cogmi50398.2020.00033.
Texte intégralJung, Jin-Young, et Michael M. Chen. « Numerical Simulation of Dendritic Solidification ». Dans ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1481.
Texte intégralKumar, Manoj, et Manan Suri. « Oxide-based Memory Devices as Artificial Dendrites for Neuromorphic Hardware ». Dans 2023 IEEE 23rd International Conference on Nanotechnology (NANO). IEEE, 2023. http://dx.doi.org/10.1109/nano58406.2023.10231171.
Texte intégralLi, Jiayi, Zhipeng Liu, Yaotong Song et Shangce Gao. « Recurrent Dendritic Neural Network ». Dans 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2023. http://dx.doi.org/10.1109/itaic58329.2023.10408923.
Texte intégralHutchinson, Zachary. « An Artificial Dendritic Neuron Model Using Radial Basis Functions ». Dans 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011775400003393.
Texte intégralHuang, R., H. Tawfik et A. K. Nagar. « Artificial Dendritic Cells Algorithm for Online Break-In Fraud Detection ». Dans 2009 Second International Conference on Developments in eSystems Engineering (DESE). IEEE, 2009. http://dx.doi.org/10.1109/dese.2009.59.
Texte intégralvan Ooyen, A. « Influence of dendritic morphology on axonal competition ». Dans 9th International Conference on Artificial Neural Networks : ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991243.
Texte intégralZhou, Wen, Yiwen Liang, Hongbin Dong, Chengyu Tan, Zhenhua Xiao et Weiwei Liu. « A Numerical Differentiation Based Dendritic Cell Model ». Dans 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00167.
Texte intégralHuan Yang, Jun Fu, Shijie Yi, Chengyu Tan et Yiwen Liang. « Dendritic cell algorithm for web server aging detection ». Dans International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.1088.
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