Academic literature on the topic 'Artificial dendrite'
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Journal articles on the topic "Artificial dendrite":
Jia, Dongbao, Weixiang Xu, Dengzhi Liu, Zhongxun Xu, Zhaoman Zhong, and Xinxin Ban. "Verification of Classification Model and Dendritic Neuron Model Based on Machine Learning." Discrete Dynamics in Nature and Society 2022 (July 4, 2022): 1–14. http://dx.doi.org/10.1155/2022/3259222.
Tanaka, Makito, Tetsuro Sasada, Tetsuya Nakamoto, Sascha Ansén, Osamu Imataki, Alla Berezovskaya, Marcus Butler, Lee Nadler, and Naoto Hirano. "Immunogenicity of Artificial Dendritic Cells Is Upregulated by ROCK Inhibition-Mediated Dendrite Formation." Blood 114, no. 22 (November 20, 2009): 3022. http://dx.doi.org/10.1182/blood.v114.22.3022.3022.
Liu, 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 (January 1, 2022): 012060. http://dx.doi.org/10.1088/1742-6596/2152/1/012060.
Mu, Yanlu, Tianyi Zhou, Zhaoyi Zhai, Shuangbin Zhang, Dexing Li, Lan Chen, and 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.
Jing, Zhaokun, Yuchao Yang, and Ru Huang. "Dual-mode dendritic devices enhanced neural network based on electrolyte gated transistors." Semiconductor Science and Technology 37, no. 2 (December 23, 2021): 024002. http://dx.doi.org/10.1088/1361-6641/ac3f21.
Peng, Hong, Tingting Bao, Xiaohui Luo, Jun Wang, Xiaoxiao Song, Agustín Riscos-Núñez, and Mario J. Pérez-Jiménez. "Dendrite P systems." Neural Networks 127 (July 2020): 110–20. http://dx.doi.org/10.1016/j.neunet.2020.04.014.
Berger, Thomas, Matthew E. Larkum, and 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 (February 1, 2001): 855–68. http://dx.doi.org/10.1152/jn.2001.85.2.855.
Zhang, Xiliang, Sichen Tao, Zheng Tang, Shuxin Zheng, and Yoki Todo. "The Mechanism of Orientation Detection Based on Artificial Visual System for Greyscale Images." Mathematics 11, no. 12 (June 15, 2023): 2715. http://dx.doi.org/10.3390/math11122715.
Chakilam, Shashikanth, Dan Ting Li, Zhang Chuan Xi, Rimvydas Gaidys, and Audrone Lupeikiene. "Morphological Study of Insect Mechanoreceptors to Develop Artificial Bio-Inspired Mechanosensors." Engineering Proceedings 2, no. 1 (November 14, 2020): 70. http://dx.doi.org/10.3390/ecsa-7-08199.
Gong, Mingchen. "The growth mechanism and strategies of dendrite in lithium metal anode." Highlights in Science, Engineering and Technology 83 (February 27, 2024): 533–37. http://dx.doi.org/10.54097/0wy2hf86.
Dissertations / Theses on the topic "Artificial dendrite":
Cheng, Long. "Relaxor ferroelectrics for neuromorphic computing." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST073.
To 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.
Takeda, Shigeo. "Functionalization of Glucan Dendrimers and Bio-applications." Kyoto University, 2020. http://hdl.handle.net/2433/253505.
Janzakova, 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.
Neuromorphic 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.
The 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, and 林侑陞. "Synthesis of Peptide Conjugated Poly(amidoamine) Dendrimer as Artifical Racemerase." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/97525607925182174395.
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.
Book chapters on the topic "Artificial dendrite":
Rouw, Eelco, Jaap Hoekstra, and Arthur H. M. van Roermund. "An artificial dendrite using active channels." In Lecture Notes in Computer Science, 176–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/bfb0100484.
Bell, Tony. "Artificial dendritic learning." In Neural Networks, 161–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52255-7_37.
Jia, Huijue. "Memory in Dendritic Spines." In Neuroscience for Artificial Intelligence, 85–112. New York: Jenny Stanford Publishing, 2023. http://dx.doi.org/10.1201/9781003410980-5.
Herreras, O., J. M. Ibarz, L. López-Aguado, and P. Varona. "Dendrites: The Last-Generation Computers." In 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.
Chelly, Zeineb, Abir Smiti, and Zied Elouedi. "COID-FDCM: The Fuzzy Maintained Dendritic Cell Classification Method." In 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.
Ohme, M., and A. Schierwagen. "A reduced model for dendritic trees with active membrane." In Artificial Neural Networks — ICANN 96, 691–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61510-5_117.
Möller, Ralf, and Horst-Michael Groß. "Possible Functional Roles of the Bipartite Dendrites of Pyramidal Cells." In 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.
Panchev, Christo, Stefan Wermter, and Huixin Chen. "Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites." In Artificial Neural Networks — ICANN 2002, 896–901. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46084-5_145.
Vandesompele, Alexander, Francis Wyffels, and Joni Dambre. "Dendritic Computation in a Point Neuron Model." In 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.
Sommerkorn, G., U. Seiffert, D. Surmeli, A. Herzog, B. Michaelis, and K. Braun. "Classification of 3-D Dendritic Spines using Self-Organizing Maps." In Artificial Neural Nets and Genetic Algorithms, 129–32. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6492-1_28.
Conference papers on the topic "Artificial dendrite":
Nakagawa, K., T. Takaki, Y. Morita, and E. Nakamachi. "2D Phase-Field Analyses of Axonal Extension of Nerve Cell." In ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64281.
Hutchinson, Zachary. "Artificial Dendrites: an Algorithm." In 2020 IEEE Second International Conference on Cognitive Machine Intelligence (CogMI). IEEE, 2020. http://dx.doi.org/10.1109/cogmi50398.2020.00033.
Jung, Jin-Young, and Michael M. Chen. "Numerical Simulation of Dendritic Solidification." In ASME 2000 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/imece2000-1481.
Kumar, Manoj, and Manan Suri. "Oxide-based Memory Devices as Artificial Dendrites for Neuromorphic Hardware." In 2023 IEEE 23rd International Conference on Nanotechnology (NANO). IEEE, 2023. http://dx.doi.org/10.1109/nano58406.2023.10231171.
Li, Jiayi, Zhipeng Liu, Yaotong Song, and Shangce Gao. "Recurrent Dendritic Neural Network." In 2023 IEEE 11th Joint International Information Technology and Artificial Intelligence Conference (ITAIC). IEEE, 2023. http://dx.doi.org/10.1109/itaic58329.2023.10408923.
Hutchinson, Zachary. "An Artificial Dendritic Neuron Model Using Radial Basis Functions." In 15th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2023. http://dx.doi.org/10.5220/0011775400003393.
Huang, R., H. Tawfik, and A. K. Nagar. "Artificial Dendritic Cells Algorithm for Online Break-In Fraud Detection." In 2009 Second International Conference on Developments in eSystems Engineering (DESE). IEEE, 2009. http://dx.doi.org/10.1109/dese.2009.59.
van Ooyen, A. "Influence of dendritic morphology on axonal competition." In 9th International Conference on Artificial Neural Networks: ICANN '99. IEE, 1999. http://dx.doi.org/10.1049/cp:19991243.
Zhou, Wen, Yiwen Liang, Hongbin Dong, Chengyu Tan, Zhenhua Xiao, and Weiwei Liu. "A Numerical Differentiation Based Dendritic Cell Model." In 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2017. http://dx.doi.org/10.1109/ictai.2017.00167.
Huan Yang, Jun Fu, Shijie Yi, Chengyu Tan, and Yiwen Liang. "Dendritic cell algorithm for web server aging detection." In 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.