Добірка наукової літератури з теми "Neuroinspired"
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Статті в журналах з теми "Neuroinspired":
Chen, Pai-Yu, and Shimeng Yu. "Technological Benchmark of Analog Synaptic Devices for Neuroinspired Architectures." IEEE Design & Test 36, no. 3 (June 2019): 31–38. http://dx.doi.org/10.1109/mdat.2018.2890229.
Alibart, Fabien, Stéphane Pleutin, Olivier Bichler, Christian Gamrat, Teresa Serrano-Gotarredona, Bernabe Linares-Barranco, and Dominique Vuillaume. "A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing." Advanced Functional Materials 22, no. 3 (December 13, 2011): 609–16. http://dx.doi.org/10.1002/adfm.201101935.
Bogdanov, Andrey V. "Neuroinspired Architecture for Robust Classifier Fusion of Multisensor Imagery." IEEE Transactions on Geoscience and Remote Sensing 46, no. 5 (May 2008): 1467–87. http://dx.doi.org/10.1109/tgrs.2008.916214.
Yan, Xiaobing, Jianhui Zhao, Sen Liu, Zhenyu Zhou, Qi Liu, Jingsheng Chen, and Xiang Yang Liu. "Memristor with Ag-Cluster-Doped TiO2Films as Artificial Synapse for Neuroinspired Computing." Advanced Functional Materials 28, no. 1 (November 24, 2017): 1705320. http://dx.doi.org/10.1002/adfm.201705320.
Zhao, Mengliu, Yong Sun, Lei Yan, Zhen Zhao, Linxia Wang, Xiaobing Yan, and Kaiyou Wang. "Memristor with BiVO4 nanoparticle as artificial synapse for neuroinspired computing." Applied Physics Letters 120, no. 9 (February 28, 2022): 093501. http://dx.doi.org/10.1063/5.0079418.
Zhao, Jianhui, Zhenyu Zhou, Yuanyuan Zhang, Jingjuan Wang, Lei Zhang, Xiaoyan Li, Mengliu Zhao, et al. "An electronic synapse memristor device with conductance linearity using quantized conduction for neuroinspired computing." Journal of Materials Chemistry C 7, no. 5 (2019): 1298–306. http://dx.doi.org/10.1039/c8tc04395g.
Sun, Lin, Yi Du, Haiyang Yu, Huanhuan Wei, Wenlong Xu, and Wentao Xu. "An Artificial Reflex Arc That Perceives Afferent Visual and Tactile Information and Controls Efferent Muscular Actions." Research 2022 (February 11, 2022): 1–11. http://dx.doi.org/10.34133/2022/9851843.
Mofrad, Asieh Abolpour, Matthew G. Parker, Zahra Ferdosi, and Mohammad H. Tadayon. "Clique-Based Neural Associative Memories with Local Coding and Precoding." Neural Computation 28, no. 8 (August 2016): 1553–73. http://dx.doi.org/10.1162/neco_a_00856.
Yan, Xiaobing, Jianhui Zhao, Sen Liu, Zhenyu Zhou, Qi Liu, Jingsheng Chen, and Xiang Yang Liu. "Memristors: Memristor with Ag-Cluster-Doped TiO2 Films as Artificial Synapse for Neuroinspired Computing (Adv. Funct. Mater. 1/2018)." Advanced Functional Materials 28, no. 1 (January 2018): 1870002. http://dx.doi.org/10.1002/adfm.201870002.
Ellenbogen, JR, V. Narbad, H. Hasegawa, and R. Selway. "P32 Targeting accuracy of the neuromate robot in DBS implantation for paediatric dystonia." Journal of Neurology, Neurosurgery & Psychiatry 90, no. 3 (February 14, 2019): e33.2-e33. http://dx.doi.org/10.1136/jnnp-2019-abn.105.
Дисертації з теми "Neuroinspired":
Henniquau, Dimitri. "Conception d’une interface fonctionnelle permettant la communication de neurones artificiels et biologiques pour des applications dans le domaine des neurosciences." Thesis, Université de Lille (2018-2021), 2021. http://www.theses.fr/2021LILUN032.
Neuromorphic engineering is an exciting emerging new field, which combines skills in electronics, mathematics, computer sciences and biomorphic engineering with the aim of developing artificial neuronal networks capable of reproducing the brain’s data processing. Thus, neuromorphic systems not only offer more effective and energy efficient solutions than current data processing technologies, but also set the bases for developing novel original therapeutic strategies in the context of pathological brain dysfunctions. The research group Circuits Systèmes Applications des Micro-ondes (CSAM) of the Institute for Electronics, Microelectronics and Nanotechnologies (IEMN) in Lille, in which this thesis work was carried out, has contributed to the generation of such neuromorphic systems by developing a toolbox constituted of artificial neurons and synapses. In order to implement neuromorphic engineering in the therapeutic arsenal for treating neurologic disorders, we need to interface living and artificial neurons to ensure real communication between these different components. In this context and using the original tools developed by the CSAM group, the main goal of this thesis work was to design and produce a functional interface allowing a bidirectional communication loop to be established between living and artificial neurons. These artificial neurons have been developed by the CSAM group using CMOS technology and are able to emit biomimetic electrical signals. Living neurons were obtained from differentiated PC-12 cells. A first step in this work consisted in modeling and simulating this interface between artificial and living neurons; a second part of the thesis was dedicated to the fabrication and characterization of neurobiohybrid interfaces, and to the growth and characterization of living neurons before studying their capacities to communicate with artificial neurons. First, a model of neuronal membrane representing a living neuron interfaced with a metallic planar electrode has been developed. We thus showed that it is possible to excite neurons using biomimetic signals produced by artificial neurons while maintaining a low excitation voltage. Low voltage excitation would improve energy efficiency of neurobiohybrid systems integrating artificial neurons and reduce the impact of harmful electrical signals on living neurons. Then, the neurobiohybrid interfacing living and artificial neurons has been designed and produced. The results obtained by experimental characterization of this interface validate the approach consisting in exciting living neurons through a metallic planar electrode. Finally, living neurons from PC-12 cells were grown and differentiated directly onto neurobiohybrids. Then, an experimental proof of the ability of biomimetic electrical signals to excite living neurons was obtained using calcium imaging. To conclude, the work presented in this manuscript clearly establishes a proof of concept for the excitation of living neurons using a biomimetic signal in our experimental conditions and thus substantiates the first part of the bidirectional communication loop between artificial neurons and living neurons
Williame, Jérôme. "Oscillateurs nanomagnétiques soumis à une boucle de rétroaction à retard : Bruit, chaos et applications neuromorphiques." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLS119.
A delay feedback loop occurs when the output of a system is used to modify the input signal of the system. This phenomenon appears in fields as varied as the physics of amplifiers, the biology of insulin regulation or in social interactions. The effects of a delay feedback loop on an electronic system are well known and have given rise to many applications: phase-locked loops to improve stochastic properties, amplification or regulation loops, and so on. However, these feedback effects remain relatively unexplored in the context of nanomagnetic systems. In this thesis I have studied theoretically the consequences of delayed feedback on the magnetization dynamics of three different nanoscale systems with a separate focus for each system. The first involves spin-torque nano-oscillators whose stochastic properties and the impact of a feedback loop on them have been studied. It is found that significant changes can occur to the spectral linewidth, along with the appearance of secondary frequencies at large delays. The second system involves the macrospin oscillator, where I investigated how delayed feedback can induce chaotic transitions between the in-plane and out-ofplane precession states. These complex dynamics can be used to generate random numbers. The third system represents a proposal for implementing a Mackey-Glass oscillator using a domain wall racetrack-like geometry. By deforming this domain wall with spin polarized currents and with a suitable readout function, I show that this oscillator can be used for a time-delay architecture for reservoir computing. Tests of nonlinear time series prediction are conducted to evaluate the performance of this system
Тези доповідей конференцій з теми "Neuroinspired":
Locatelli, Nicolas, Julie Grollier, Damien Querlioz, Adrien F. Vincent, Alice Mizrahi, Joseph S. Friedman, Damir Vodenicarevic, Joo-Von Kim, Jacques-Olivier Klein, and Weisheng Zhao. "Spintronic Devices as Key Elements for Energy-Efficient Neuroinspired Architectures." In Design, Automation and Test in Europe. New Jersey: IEEE Conference Publications, 2015. http://dx.doi.org/10.7873/date.2015.1117.
Beck, Cornelia, Umberto Olcese, Alberto Montagner, Stefan Ringbauer, Heiko Neumann, Antonio Frisoli, Rita Almeida, Massimo Bergamasco, and Gustavo Deco. "A neuroinspired cognitive behavioral control architecture for visually driven mobile robotics." In 2008 IEEE International Conference on Robotics and Biomimetics. IEEE, 2009. http://dx.doi.org/10.1109/robio.2009.4913342.