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Добірка наукової літератури з теми "Artificial dendrite"

Автор: Grafiati
Опубліковано: 7 липня 2024

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Статті в журналах з теми "Artificial dendrite":

1

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.

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Artificial neural networks have achieved a great success in simulating the information processing mechanism and process of neuron supervised learning, such as classification. However, traditional artificial neurons still have many problems such as slow and difficult training. This paper proposes a new dendrite neuron model (DNM), which combines metaheuristic algorithm and dendrite neuron model effectively. Eight learning algorithms including traditional backpropagation, classic evolutionary algorithms such as biogeography-based optimization, particle swarm optimization, genetic algorithm, population-based incremental learning, competitive swarm optimization, differential evolution, and state-of-the-art jSO algorithm are used for training of dendritic neuron model. The optimal combination of user-defined parameters of model has been systemically investigated, and four different datasets involving classification problem are investigated using proposed DNM. Compared with common machine learning methods such as decision tree, support vector machine, k-nearest neighbor, and artificial neural networks, dendritic neuron model trained by biogeography-based optimization has significant advantages. It has the characteristics of simple structure and low cost and can be used as a neuron model to solve practical problems with a high precision.
2

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.

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Abstract Abstract 3022 Poster Board II-998 Dendritic cells (DC) are “professional” antigen-presenting cells (APC) that can prime T cells. Their characteristic morphology and phenotype segregate them from other APC. Many studies suggest that mature DC are able to induce potent antitumor T cell immunity that can reject tumors. Based on this, numerous cancer vaccine trials using ex vivo generated DC have been conducted in humans. However, the observed objective response rates in these studies have been disappointing. This could partially be attributed to difficulties in generating large numbers of clinical grade, optimally matured DC. Also, it is widely accepted that the quality and quantity of DC generated ex vivo vary substantially among individuals. We have hypothesized that the generation of standardized artificial DC (aDC) will overcome the time, expense, and suboptimal reproducibility of DC cultures and prompt the development of DC-based immunotherapy for cancer. Previously, we developed a renewable and standardized artificial APC (aAPC) by transducing HLA-A2, CD80, and CD83 to the human erythroleukemic suspension cell line, K562. This aAPC can naturally process and present HLA-A2-restricted peptides and uniquely support the priming and prolonged expansion of large numbers of antigen-specific CD8+ CTL. Generated antigen-specific CTL display a central ∼ effector memory phenotype consistent with in vivo persistence, possess potent effector function, and specifically recognize tumor cells. Furthermore, CTL can be maintained in vitro for a prolonged period of time up to >1 year without any feeder cells or cloning. Recent clinical trials have demonstrated that adoptive transfer of anti-tumor CTL with a memory phenotype generated ex vivo using this aAPC, IL-2, and IL-15 can persist in cancer patients as memory T cells for >6 months without any lymphodepletion, adjuvants, or cytokine administration. Clinical responses have also been observed in some patients. To develop a standardized aDC, we have undertaken an approach to differentiate our K562-based aAPC into aDC. In neurogenesis, it has been well established that a family of Rho GTPases (Rho, Rac, and Cdc42) critically regulates the outgrowth of neurites, i.e. dendrites and axon. We have found that the inhibition of Rho kinase (ROCK), which is a key effector molecule of Rho, can promote the differentiation of monocyte-derived immature DC into mature DC both morphologically and phenotypically. Intriguingly, when aAPC were forced to attach via a newly identified surface molecule, PladX, and ROCK activity was subsequently blocked, K562-derived aAPC “differentiated” into DC-like cells by acquiring dendrite extensions and growth cone-like structures at the end of the extensions (see picture). PladX-mediated strong attachment was critical for differentiation, since ROCK inhibition without attachment or following attachment via conventional adhesion molecules such as poly-L lysine, fibronectin, or collagen was not sufficient to induce dendrites. Confocal microscopy analysis revealed that dendrites were composed of F-actin rich filopodia and lamellipodia. Furthermore, F-actin and microtubules were differentially localized in the “growth cones” and “dendrite shafts” of aDC, respectively. While treatment with actin inhibitors blocked the generation of “growth cones” but not dendritic shafts, exposure to microtubule inhibitors abrogated the extension of dendritic shafts. Finally, we were able to demonstrate that aDC were more potent than aAPC in CD8+ T cell stimulatory activity. This was the case despite the fact that differentiation of aAPC into aDC does not alter the expression level of molecularly engineered immunoaccessory molecules MHC class I, CD80, and CD83. The effects of the differentiation on processing and presentation of antigenic peptides were negligible since CD8+ T cell antigen was exogenously pulsed as a fully processed synthetic peptide. Taken together, this result indicates that the dendrite formation and the resultant enlarged surface area are critical determinants of DC's enhanced immunogenicity. We have succeeded in producing infinite number of aDC with enhanced immunogenicity by differentiating our renewable and standardized K562-based aAPC, which has been already tested in the clinic. This novel aDC may overcome the cumbersome issues inherent to conventional DC and widen the applicability of DC-based immunotherapy for cancer. Disclosures No relevant conflicts of interest to declare.
3

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.

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Abstract The lithium metal has been considered as a competitive material for anode on the high-energy storage battery because of its various advantages, such as high capacity, low density, and the lowest electrochemical potential. However, the uncontrolled dendritic growth on the anode surface could cause the short circuit, even explosion of the battery. Therefore, strategies about how to effectively inhibit the formation of dendrites is of great importance. This paper will first give a brief introduction on the growth of dendrites. The attention is then focused on the recent advancements to suppress the dendrite growth of lithium metal, such as the optimization of electrolyte, application of artificial solid electrolyte interphase (SEI), and the modification of lithium anode. The future research directions will be presented at the end.
4

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.

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The schematic diagram for the plating/stripping process of Zn. (a) Corrosion, by-products, and Zn dendrites are observed on a bare Zn electrode. (b) The Zn–THBA protective layer endows a dense and dendrite-free plating/stripping morphology.
5

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.

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Abstract As a fundamental component of biological neurons, dendrites have been proven to have crucial effects in neuronal activities. Single neurons with dendrite structures show high signal processing capability that is analogous to a multilayer perceptron (MLP), whereas oversimplified point neuron models are still prevalent in artificial intelligence algorithms and neuromorphic systems and fundamentally limit their efficiency and functionality of the systems constructed. In this study, we propose a dual-mode dendritic device based on electrolyte gated transistor, which can be operated to generate both supralinear and sublinear current–voltage responses when receiving input voltage pulses. We propose and demonstrate that the dual-mode dendritic devices can be used as a dendritic processing block between weight matrices and output neurons so as to dramatically enhance the expression ability of the neural networks. A dual-mode dendrites-enhanced neural network is therefore constructed with only two trainable parameters in the second layer, thus achieving 1000× reduction in the amount of second layer parameter compared to MLP. After training by back propagation, the network reaches 90.1% accuracy in MNIST handwritten digits classification, showing advantage of the present dual-mode dendritic devices in building highly efficient neuromorphic computing.
6

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.

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7

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.

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Despite the wealth of recent research on active signal propagation along the dendrites of layer V neocortical pyramidal neurons, there is still little known regarding the traffic of subthreshold synaptic signals. We present a study using three simultaneous whole cell recordings on the apical dendrites of these cells in acute rat brain slices to examine the spread and attenuation of spontaneous excitatory postsynaptic potentials (sEPSPs). Equal current injections at each of a pair of sites separated by ∼500 μm on the apical dendrite resulted in equal voltage transients at the other site (“reciprocity”), thus disclosing linear behavior of the neuron. The mean apparent “length constants” of the apical dendrite were 273 and 446 μm for somatopetal and somatofugal sEPSPs, respectively. Trains of artificial EPSPs did not show temporal summation. Blockade of the hyperpolarization-activated cation current ( I h) resulted in less attenuation by 17% for somatopetal and by 47% for somatofugal sEPSPs. A pronounced location-dependent temporal summation of EPSP trains was seen. The subcellular distribution and biophysical properties of I h were studied in cell-attached patches. Within less than ∼400 μm of the soma, a low density of ∼3 pA/μm2 was found, which increased to ∼40 pA/μm2 in the apical distal dendrite. I h showed activation and deactivation kinetics with time constants faster than 40 ms and half-maximal activation at −95 mV. These findings suggest that integration of synaptic input to the apical tuft and the basal dendrites occurs spatially independently. This is due to a high I h channel density in the apical tuft that increases the electrotonic distance between these two compartments in comparison to a passive dendrite.
8

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.

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Human visual system is a crucial component of the nervous system, enabling us to perceive and understand the surrounding world. Advancements in research on the visual system have profound implications for our understanding of both biological and computer vision. Orientation detection, a fundamental process in the visual cortex where neurons respond to linear stimuli in specific orientations, plays a pivotal role in both fields. In this study, we propose a novel orientation detection mechanism for local neurons based on dendrite computation, specifically designed for grayscale images. Our model comprises eight neurons capable of detecting local orientation information, with inter-neuronal interactions facilitated through nonlinear dendrites. Through the extraction of local orientation information, this mechanism effectively derives global orientation information, as confirmed by successful computer simulations. Experimental results demonstrate that our mechanism exhibits remarkable orientation detection capabilities irrespective of variations in size, shape, or position, which aligns with previous physiological research findings. These findings contribute to our understanding of the human visual system and provide valuable insights into both biological and computer vision. The proposed orientation detection mechanism, with its nonlinear dendritic computations, offers a promising approach for improving orientation detection in grayscale images.
9

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.

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Mechanoreceptors of the insect play a vital role for the insect to sense and monitor the environmental parameters, like flow, tactile pressure, etc. This paper presents the studies made on the morphology of the mechanoreceptor of the insect Blattella asahinai (scientific name of cockroach) that is a hair-like structure known as trichoid sensilla, by scanning electron microscope and confocal laser microscope. The scanned images show the details of sensilla components in which the hair is embedded in the sockets, which are connected with the cuticle and joint membrane, where the dendrite touches at the base of the hair passing through the cuticle layers. The images also show that the tubular bodies and microtubules are tightly compacted inside the dendrite. This paper presents the details of how the sensilla work when an external stimulus act on them. The hair deflects with the disturbance of the cuticle and joint membrane, and this deformed hair leans on the dendrite, which is attached at the base of the hair that in turn presses the tubular bodies and microtubules, which develop negative ions passing down through the dendrite to the neuron, which provides information as an electric signal to the brain of the insect so that it responds for necessary action. Based on the morphological studies, sensing mechanism, material properties of the components, and design principles will be evolved for the development of an artificial bio-inspired sensor. A solid works model of the sensilla is also presented.
10

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.

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Lithium metal batteries offer an incredibly high potential energy density when compared to the present large-scale commercial lithium-ion batteries. In recent years, with the development of technology, the energy density of lithium-ion batteries has rapidly reached its theoretical energy density. People are gradually pursuing higher energy density batteries. The negative electrode of batteries, made of lithium metal, has the lowest reduction potential and the highest theoretical specific capacity, which has great research value and a number of possible uses for the creation of secondary batteries with large capacities. However, actual uses may present safety risks. Lithium dendrites, which can result in short circuits, fires, or explosions, and decreased battery efficiency are caused by the ease with which Li+ can deposit unevenly on the anode's uneven surface. As a result, this study focuses on the development process of lithium dendrite and analyzes three elements of electrolyte regulation: artificial SEI layer, solid electrolyte, and strategies to restrict the growth of lithium dendrite.
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Статті в журналах

Дисертації з теми "Artificial dendrite":

1

Cheng, Long. "Relaxor ferroelectrics for neuromorphic computing." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPAST073.

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Pour surmonter les défis posés par les architectures traditionnelles de von Neumann, l'informatique neuromorphique s'inspire des sciences du cerveau pour créer du matérielécoénergétique adaptable à des tâches complexes. Les memristors, bien que novateurs,rencontrent des problèmes tels que la chaleur de Joule entravant le calcul neuronal à trèsbasse puissance.Pour remédier à cela, nous proposons un mécanisme de memcapacitor -la transition de phase induite par champ électrique. Les memcapacitors, qui expriment les signaux en tension, offrent une consommation d'énergie inférieure aux memristors (basés surle courant). Notre étude sur les matériaux ferroélectriques relaxeur (PMN-28PT, PZN-4.5PT) et le ferroélectrique conventionnel BTO (001) démontre la nature universelle des transitions de phase induites par champ électrique. Des impulsions personnalisées permettent la reproduction de la potentialisation à long terme (LTP), de la dépression à long terme (LTD) et de la plasticité dépendante du temps d'impulsion (STDP).De plus, les ferroélectriques relaxeur présentent un effet dendritique absent dans les contreparties conventionnelles. La mise en œuvre de dendrites PZN-4.5PT dans les réseaux neuronaux améliore la précision (83.44 %), surpassant les réseaux de memristors avec dendrites linéaires (81.84 %) et surpassant de manière significative les réseaux sans dendrites (80.1 %).En fin de compte, nous mettons en œuvre avec succès un memcapacitor relaxeur enutilisant un film mince PMN. Cette structure métal/ferroélectrique/métal/isolant atteint desétats capacitifs de 3 bits par le biais de transitions de phase induites par champ. 8 états memcapacitifs robustes présentent une maintenance cohérente sur plus de 100 secondes et une endurance exceptionnelle dépassant 5×10^5 cycles. Des impulsions sur mesure émulent efficacement LTP, LTD, et permettent l'exploration des fonctionnalités synaptiques dépendantes de la température
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
2

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.

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The use of metals like titanium (Ti) and vanadium (V) are common in many medical implants for orthopaedic and orthodontic purposes. The most frequent cause of implant failure is aseptic loosening, resulting from an inflammatory reaction and increased osteolysis at the bone-metal interface. Currently, the pathophysiological mechanism of aseptic loosening remains poorly understood. One hypothesis suggests the reactivity of immune cells (metal hypersensitivity) towards metal ions released through the biocorrosion of metal implants. This thesis examines the effects of titanium and vanadium ions on various immune cells like monocytes, dendritic cells (DCs) and T-lymphocytes. Thereby investigating the role and mechanism which titanium and vanadium plays in aseptic loosening. Through energy filtered transmission electron microscopy, the accumulation of titanium ions was visualized in human monocyte-derived DCs and T-lymphocytes after 24 hours exposure. Titanium was seen to co-localise with phosphorous-rich regions, like the cell membrane, organelles and nucleus of these cells. Flow cytometry measured changes in the cell surface marker expression of monocytes, osteoclasts, DCs and T-lymphocytes treated with the metals. Monocytes exposed to titanium (IV) showed an increase of Tartate-Resistant Acid Phosphatase (TRAP), important for osteolysis and indicative of differentiation towards an osteoclast-like phenotype. DCs treated with Ti(IV) and vanadium (III) had reduced antigen presenting MHC class II expression, but not a reduced capacity to proliferate non-adherent peripheral blood monocytic cells (naPBMCs). Under the influence of Ti(IV), T-lymphocytes, DCs and monocytes expressed elevated levels of the chemokine receptor, CCR4. This would allow for the migration of CCR4+ cells towards the bone and skin regions. Functional changes were measured with BrdU incorporation proliferation assays, cytokine assays (CBA Kits) and the successful generation of titanium-specific T-lymphocytes from Ti(IV) treated DCs. Ti(IV) specific T-lymphocytes conceptually shows the possible formation of an antigenic titanium-protein complex, which can be recognized by the immune system. DCs treated with Ti(IV) and V(III) were able to cause the proliferation of naPBMCs, even with a reduced antigen presenting capability. However, there was no additional influence of V(III) on the immune response through DCs. Cytokines released by DCs and T-lymphocytes after Ti(IV) treatments showed a skew towards an inflammatory Th1-type response through the release of TGF-! and IL-12p70. Activated T-lymphocytes exposed to Ti(IV) also released RANK-L, which drives osteoclastogenesis and subsequently increased osteolysis. The research supports and suggests an interaction between immune and bone cells where titanium-induced inflammation drives an osteolytic cycle that prevents the integration of metal implants into the bone. Hence, suggesting a mechanism for implant failure through aseptic loosening in patients with titanium-vanadium implants.
3

Takeda, Shigeo. "Functionalization of Glucan Dendrimers and Bio-applications." Kyoto University, 2020. http://hdl.handle.net/2433/253505.

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4

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.

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Les technologies neuromorphiques constituent une voie prometteuse pour le développement d'une informatique plus avancée et plus économe en énergie. Elles visent à reproduire les caractéristiques attrayantes du cerveau, telles qu'une grande efficacité de calcul et une faible consommation d'énergie au niveau des logiciels et du matériel. À l'heure actuelle, les implémentations logicielles inspirées du cerveau (telles que ANN et SNN) ont déjà démontré leur efficacité dans différents types de tâches (reconnaissance d'images et de la parole). Toutefois, pour tirer un meilleur parti des algorithmes inspirés du cerveau, il est possible de les combiner avec une implémentation materielle appropriée qui s'appuierait également sur une architecture et des processus inspirés du cerveau. L'ingénierie neuromorphique s'est principalement appuyée sur les technologies conventionnelles (CMOS circuits, memristor) pour le développement de circuits inspirés du cerveau. Néanmoins, ces implémentations sont fabriquées suivant une approche top-down. En revanche, l'informatique cérébrale repose sur des processus bottom-up tels que l'interconnectivité entre les cellules et la formation de voies de communication neuronales.À la lumière de ce qui précède, ce travail de thèse porte sur le développement de dispositifs neuromorphiques organiques programmables en 3D qui, contrairement à la plupart des technologies neuromorphiques actuelles, peuvent être créés de manière bottom-up. Cela permet de rapprocher les technologies neuromorphiques du niveau de programmation du cerveau, où les chemins neuronaux nécessaires sont établis uniquement en fonction des besoins.Tout d'abord, nous avons découvert que les interconnexions 3D à base de PEDOT:PSS peuvent être formées au moyen d'électropolymérisation bipolaire en courant alternatif, permettant d'imiter la croissance des cellules neuronales. En réglant individuellement les paramètres de la forme d'onde (tension d'amplitude de crête - VP, fréquence - f, duty cycle- dc et tension de décalage - Voff), une large gamme de structures semblables à des dendrites a été observée avec différents degrés de ramification, volumes, surfaces, asymétries et dynamiques de croissance.Ensuite, nous avons montré que les morphologies dendritiques obtenues à différentes fréquences sont conductrices. De plus, chaque structure présente une valeur de conductance qui peut être interprétée comme un poids synaptique. Plus important encore, la capacité des dendrites à fonctionner comme OECT a été révélée. Différentes morphologies de dendrites ont présenté des performances différentes en tant qu'OECT. De plus, la capacité des dendrites en PEDOT:PSS à modifier leur conductivité en réponse à la tension de grille a été utilisée pour imiter les fonctions de mémoire du cerveau (plasticité à court terme -STP et plasticité à long terme -LTP). Les réponses à la STP varient en fonction de la structure dendritique. En outre, l'émulation de la LTP a été démontrée non seulement au moyen d'un fil de grille Ag/AgCl, mais aussi au moyen d'une grille dendritique en polymère développée par électropolymérisation.Enfin, la plasticité structurelle a été démontrée par la croissance dendritique, où le poids de la connexion finale est régi par les règles d'apprentissage de type Hebbien (plasticité dépendante du moment de l'impulsion - STDP et plasticité dépendante du rythme de l'impulsion - SRDP). En utilisant les deux approches, une variété de topologies dendritiques avec des états de conductance programmables (c'est-à-dire le poids synaptique) et diverses dynamiques de croissance ont été observées. Finalement, en utilisant la même plasticité structurelle dendritique, des caractéristiques cérébrales plus complexes telles que l'apprentissage associatif et les tâches de classification ont été émulées.En outre, les perspectives futures de ces technologies basées sur des objets dendritiques polymères ont été discutées
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
5

Almeida, Fernando Mendonça de. "Autoproteção para a internet das coisas." Universidade Federal de Sergipe, 2016. https://ri.ufs.br/handle/riufs/3361.

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Fundação de Apoio a Pesquisa e à Inovação Tecnológica do Estado de Sergipe - FAPITEC/SE
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.
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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.

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碩士
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.

Частини книг з теми "Artificial dendrite":

1

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.

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2

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.

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3

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.

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4

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.

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5

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.

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6

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.

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7

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.

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8

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.

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9

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.

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10

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.

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Тези доповідей конференцій з теми "Artificial dendrite":

1

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.

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In this study, we aimed to develop a computer-aided simulation technique to predict the axonal extension in the neuronal network evolution processes for design new scaffolds to activate the nerve cell and promote the nerve regeneration. We developed a mathematical model of axonal extension by using phase-field method and evaluated the validity of the mathematical model by comparison with the experiments. In the previous experimental studies, the peripheral nerve scaffold has been introduced to guide the axonal extension. Damaged part of nerve was replaced by the artificial tube as the scaffold to induce the axonal growth through the artificial tube and regenerate the nerve network. However, the scaffold made of biodegradable materials has a problem that it is degraded and absorbed before the nerve regenerate, and then the nerve cannot regenerate. Therefore, there is a need for the design and development of a scaffold for nerve regeneration to promote nerve regeneration. For that purpose, it is necessary to understand the difference between the axonal extensions by the surrounding environment, such as the shape or materials of the scaffold for nerve regeneration. In particular, the numerical technique to analyze the remodeling process of the nerve in the scaffold is strongly required to be established because the in-vivo experimental observation technology at the micro scale, bioethical issues in the animal experiment and requires time and money are also remained as unresolved problems. In this study, we developed a new simulation code which employed the phase-field method to predict the two-dimensional dendritic and axonal growth processes of nerve cells on cultivation scaffolds. We curried out the phase-field analyses to make clear how the parameters of Kobayashi–Warren–Carter (KWC) phase-field model affected on the morphologic growths of dendrite and axon. Simultaneously, we had observed the axonal extension process by using the PC-12D cells with nerve growth factor (NGF) on two-dimensional cultivation dish. Based on these axonal extension observation results, we approximated the morphological changes and establish the phenomenological model for phase-field analysis. Finally, we confirmed the validity of our newly developed phase-field simulation scheme in two dimensions by comparison with the experiments.
2

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.

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3

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.

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Abstract It is well known that the dendritic microstructure of alloys is a consequence of morphological instability of the solidification process, which is a result of the coupling of heat and mass transfer with the composition-dependent phase equilibrium condition mediated by the surface energy. There have been many numerical simulations of dendritic solidification. However, many successful simulations of dendritic growth have used non-discrete front tracking method such as artificial source method or phase field method, with demonstrably first order accuracy. Many also found it necessary to continuously inject random noise during simulation. The continuous injection of random noise raises the suspicion that the numerical schemes used may be overly dissipative. The noise is apparently capable of creating nonuniform solidification, but not sufficient to ensure growth with a clear dendritic pattern. In the present study, to rule out the numerical diffusivity as a cause of the damping of dendritic perturbations, artificial perturbations are either not used, or injected only as initial conditions. Under the unstable solidification mode, the initial perturbation triggers the onset of interface instability. Computations were performed for both sub-cooled pure material as well as directional solidification of alloys. The successful simulation of dendritic solidification without the intentional injection of random noise provided evidence that the present method has less numerical diffusion than many existing front tracking methods.
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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.

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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.

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6

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.

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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.

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8

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.

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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.

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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.

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