Academic literature on the topic 'Neuro inspiré'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Neuro inspiré.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Neuro inspiré":
Ghani, Arfan, Thomas Dowrick, and Liam J. McDaid. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (March 1, 2023): 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.
Zhang, Wenqiang, Bin Gao, Jianshi Tang, Peng Yao, Shimeng Yu, Meng-Fan Chang, Hoi-Jun Yoo, He Qian, and Huaqiang Wu. "Neuro-inspired computing chips." Nature Electronics 3, no. 7 (July 2020): 371–82. http://dx.doi.org/10.1038/s41928-020-0435-7.
Birzhanova, Aigerim, Aliya Nurgaliyeva, Azhar Nurmagambetova, Hasan Dinçer, and Serhat Yüksel. "Neuro quantum-inspired decision-making for investor perception in green and conventional bond investments." Investment Management and Financial Innovations 21, no. 1 (February 9, 2024): 168–84. http://dx.doi.org/10.21511/imfi.21(1).2024.14.
Harkhoe, Krishan, Guy Verschaffelt, and Guy Van der Sande. "Neuro-Inspired Computing with Spin-VCSELs." Applied Sciences 11, no. 9 (May 7, 2021): 4232. http://dx.doi.org/10.3390/app11094232.
Zhong, Xiaopin, and Lin Ma. "A Neuro-inspired Adaptive Motion Detector." Optics and Photonics Journal 03, no. 02 (2013): 94–98. http://dx.doi.org/10.4236/opj.2013.32b024.
Huang, Ping-Chen, and Jan M. Rabaey. "A Neuro-Inspired Spike Pattern Classifier." IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, no. 3 (September 2018): 555–65. http://dx.doi.org/10.1109/jetcas.2018.2842035.
Kahol, Kanav, and Sethuraman Panchanathan. "Neuro-cognitively inspired haptic user interfaces." Multimedia Tools and Applications 37, no. 1 (September 6, 2007): 15–38. http://dx.doi.org/10.1007/s11042-007-0167-y.
GINGL, ZOLTAN, LASZLO B. KISH, and SUNIL P. KHATRI. "TOWARDS BRAIN-INSPIRED COMPUTING." Fluctuation and Noise Letters 09, no. 04 (December 2010): 403–12. http://dx.doi.org/10.1142/s0219477510000332.
Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski, and Andrea Ehrmann. "Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers." Biomimetics 6, no. 2 (May 26, 2021): 32. http://dx.doi.org/10.3390/biomimetics6020032.
Yu, Shimeng. "Neuro-Inspired Computing With Emerging Nonvolatile Memorys." Proceedings of the IEEE 106, no. 2 (February 2018): 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.
Dissertations / Theses on the topic "Neuro inspiré":
Renaudo, Erwan. "Des comportements flexibles aux comportements habituels : meta-apprentissage neuro-inspiré pour la robotique autonome." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066508/document.
In this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior
Renaudo, Erwan. "Des comportements flexibles aux comportements habituels : meta-apprentissage neuro-inspiré pour la robotique autonome." Electronic Thesis or Diss., Paris 6, 2016. http://www.theses.fr/2016PA066508.
In this work, we study how the notion of behavioral habit, inspired from the study of biology, can benefit to robots. Robot control architectures allow the robot to be able to plan to reach long term goals while staying reactive to events happening in the environment (Kortenkamp et Simmons, 2008). However, these architectures are rarely provided with learning capabilities that would allow them to acquire knowledge from experience. On the other hand, learning has been shown as an essential abiilty for behavioral adaptation in mammals. It permits flexible adaptation to new contexts but also efficient behavior in known contexts (Dickinson, 1985). The learning mechanisms are modeled as model-based (planning) and model-free (habitual) reinforcement learning algorithms (Sutton et Barto, 1998) which are combined into a global model of behavior (Daw et al., 2005). We proposed a robotic control architecture that take inspiration from this model of behavior and embed the two kinds of algorithms, and studied its performance in a robotic simulated task. None of the several methods for combining the algorithm we studied gave satisfying results, however, it allowed to identify some properties required for the planning process in a robotic task. We extended our study to two other tasks (one being on a real robot) and confirmed that combining the algorithms improves learning of the robot's behavior
Cabaret, Théo. "Etude, réalisation et caractérisation de memristors organiques électro-greffés en tant que nanosynapses de circuits neuro-inspirés." Thesis, Paris 11, 2014. http://www.theses.fr/2014PA112168/document.
This PhD project takes place in the context of the study of neuromorphic circuits using memristor devices as synapses. The main objective is to evaluate a new class of organic memories developed at LICSEN (CEA Saclay/IRAMIS) and particularly their compatibility with the learning rules and the implementation strategy proposed by the Nanoarchi group at IEF (Univ. Paris-Sud, Orsay). These new memristors are based on the electro-grafting of organic redox complexes thin films to form robust and scalable metal/molecules/metal junctions. In addition to memristor fabrication, this work includes detailed electrical characterization studies (speed, retention property, scalability, robustness, etc.) aiming at, on the one hand, establishing the commutation mechanism in these new memristors and, on the other hand, evaluating their potential as synapses. This work also proposes a preparatory study of a neural-network type mixed-circuit demonstrator combining nano-memristors and conventional electronic (programmability of devices by spikes, fabrication of assemblies of memristors, variability). Moreover the demonstration of the compatibility of such memristors with the STDP (Spike Timing Dependent Plasticity) property and of the learning of a “conditioned reflex” opens the way to future unsupervised learning studies
Oliverio, Lucas. "Nonlinear dynamics from a laser diode with both optical injection and optical feedback for telecommunication applications." Electronic Thesis or Diss., CentraleSupélec, 2024. http://www.theses.fr/2024CSUP0002.
The current processing of information in large computing clusters is responsible for a strong energetic impact at a global level. The current paradigm needs to be rethought, and a computing architecture based on photonic components (semiconductor laser in particular) is studied in this thesis. The considered structure is a network of artificial neurons for telecommunications data processing. This involves using a laser diode to study the relationship between the dynamics with optical injection and optical feedback and neuroinspired computing capacity with simulations and experimental work
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
Chabi, Djaafar. "Architectures de circuits nanoélectroniques neuro-inspirée." Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00679300.
Vatin, Jeremy. "Photonique neuro-inspirée pour des applications télécoms." Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0004.
We are producing everyday thousands of gigabits of data, exchanged over the internet network. These data are processed thanks to computation clusters, which are responsible of the large amount of energy consumed by the internet network. In this work, we study an architecture made of photonic components, to get rid of electronic components that are power consuming. Thanks to components that are currently used in the internet network (laser and optical fiber), we aim at building an artificial neural network that is able to process telecommunication data. The artificial neural network is made of a laser, and an optical fiber that send back the light into the laser. The complex behavior of this system is used to feed the artificial neurons that are distributed along the fiber. We are able to prove that this system is able either to process one signal with a high efficiency, or two signals at the expense of a small loss of accuracy
Causo, Matteo. "Neuro-Inspired Energy-Efficient Computing Platforms." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10004/document.
Big Data highlights all the flaws of the conventional computing paradigm. Neuro-Inspired computing and other data-centric paradigms rather address Big Data to as resources to progress. In this dissertation, we adopt Hierarchical Temporal Memory (HTM) principles and theory as neuroscientific references and we elaborate on how Bayesian Machine Learning (BML) leads apparently totally different Neuro-Inspired approaches to unify and meet our main objectives: (i) simplifying and enhancing BML algorithms and (ii) approaching Neuro-Inspired computing with an Ultra-Low-Power prospective. In this way, we aim to bring intelligence close to data sources and to popularize BML over strictly constrained electronics such as portable, wearable and implantable devices. Nevertheless, BML algorithms demand for optimizations. In fact, their naïve HW implementation results neither effective nor feasible because of the required memory, computing power and overall complexity. We propose a less complex on-line, distributed nonparametric algorithm and show better results with respect to the state-of-the-art solutions. In fact, we gain two orders of magnitude in complexity reduction with only algorithm level considerations and manipulations. A further order of magnitude in complexity reduction results through traditional HW optimization techniques. In particular, we conceive a proof-of-concept on a FPGA platform for real-time stream analytics. Finally, we demonstrate we are able to summarize the ultimate findings in Machine Learning into a generally valid algorithm that can be implemented in HW and optimized for strictly constrained applications
Hirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.
While electronics has prospered inexorably for several decades, its leading source of progress will stop in the next coming years, due to the fundamental technological limits of transistors. Nevertheless, microelectronics is currently offering a major breakthrough: in recent years, memory technologies have undergone incredible progress, opening the way for multiple research venues in embedded systems. Additionally, a major feature for future years will be the ability to integrate different technologies on the same chip. new emerging memory devices that can be embedded in the core of the CMOS, such as Resistive Random Access Memory (RRAM) or Spin Torque Magnetic Tunnel Junction (STMRAM) based on naturally intelligent inmemory-computing architecture. Three braininspired algorithms are carefully examined: Bayesian reasoning binarized neural networks, and an approach that further exploits the intrinsic behavior of components, population coding of neurons. Each of these approaches explores different aspects of in-memory computing
Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information." Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.
Computer vision and machine learning are two hot research topics that have witnessed major breakthroughs in recent years. Much of the advances in these domains have been the fruits of many years of research on the visual cortex and brain function. In this thesis, we focus on designing neuro-inspired architectures for processing information along three different stages of the visual cortex. At the lowest stage, we propose a neural model for the acquisition of visual signals. This model is adapted to emulating eye movements and is closely inspired by the function and the architecture of the retina and early layers of the ventral stream. On the highest stage, we address the memory problem. We focus on an existing neuro-inspired associative memory model called the Sparse Clustered Network. We propose a new information retrieval algorithm that offers more flexibility and a better performance over existing ones. Furthermore, we suggest a generic formulation within which all existing retrieval algorithms can fit. It can also be used to guide the design of new retrieval approaches in a modular fashion. On the intermediate stage, we propose a new way for dealing with the image feature correspondence problem using a neural network model. This model deploys the structure of Sparse Clustered Networks, and offers a gain in matching performance over state-of-the-art, and provides a useful insight on how neuro-inspired architectures can serve as a substrate for implementing various vision tasks
Books on the topic "Neuro inspiré":
Yu, Shimeng, ed. Neuro-inspired Computing Using Resistive Synaptic Devices. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54313-0.
1966-, Arena Paolo, and International Centre for Mechanical Sciences., eds. Dynamical systems, wave-based computation and neuro-inspired robots. Wien: Springer, 2008.
Arena, Paolo, ed. Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-78775-5.
Patanè, Luca, Roland Strauss, and Paolo Arena. Nonlinear Circuits and Systems for Neuro-inspired Robot Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73347-0.
Roberta, Allen. The playful way to knowing yourself: A creative workbook to inspire self-discovery. Boston: Houghton Mifflin, 2003.
Cairo, Jim. Motivation and goal-setting: How to set and achieve goals and inspire others. Franklin Lakes, NJ: Career Press, 1998.
CAPPY. Traitement Neuro-Inspire de Linformation. ISTE Editions Ltd., 2020.
Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.
Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.
Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.
Book chapters on the topic "Neuro inspiré":
Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science, 105–15. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82427-3_8.
Lewis, Frank L., and Kyriakos G. Vamvoudakis. "Neuro-Inspired Control." In Encyclopedia of Systems and Control, 1–7. London: Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-5102-9_224-3.
Lewis, Frank L., and Kyriakos G. Vamvoudakis. "Neuro-inspired Control." In Encyclopedia of Systems and Control, 1441–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_224.
Habekost, Jan-Gerrit, Erik Strahl, Philipp Allgeuer, Matthias Kerzel, and Stefan Wermter. "CycleIK: Neuro-inspired Inverse Kinematics." In Artificial Neural Networks and Machine Learning – ICANN 2023, 457–70. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44207-0_38.
Patanè, Luca, Roland Strauss, and Paolo Arena. "Towards Neural Reusable Neuro-inspired Systems." In Nonlinear Circuits and Systems for Neuro-inspired Robot Control, 87–99. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73347-0_6.
Reyneri, L. M. "Design and Codesign of Neuro-fuzzy Hardware." In Bio-Inspired Applications of Connectionism, 14–30. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_2.
Madani, Kurosh, Ghislain de Trémiolles, and Pascal Tannhof. "ZISC-036 Neuro-processor Based Image Processing." In Bio-Inspired Applications of Connectionism, 200–207. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_24.
Patel, Leena N., and Alan Murray. "A Biologically Inspired Neural CPG for Sea Wave Conditions/Frequencies." In Advances in Neuro-Information Processing, 95–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_12.
Amudha, J., and D. Radha. "Optimization of Rules in Neuro-Fuzzy Inference Systems." In Computational Vision and Bio Inspired Computing, 803–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-71767-8_69.
Kadetotad, Deepak, Pai-Yu Chen, Yu Cao, Shimeng Yu, and Jae-sun Seo. "Peripheral Circuit Design Considerations of Neuro-inspired Architectures." In Neuro-inspired Computing Using Resistive Synaptic Devices, 167–82. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54313-0_9.
Conference papers on the topic "Neuro inspiré":
Krasilenko, Vladimir G., Alexander Lazarev, and Diana Nikitovich. "Design and simulation of optoelectronic neuron equivalentors as hardware accelerators of self-learning equivalent convolutional neural structures (SLECNS)." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2316352.
Kassa, Wosen, Evangelia Dimitriadou, Marc Haelterman, Serge Massar, and Erwin Bente. "Towards integrated parallel photonic reservoir computing based on frequency multiplexing." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306176.
Pauwels, Jaël, Guy Van der Sande, Arno Bouwens, Marc Haelterman, and Serge Massar. "Towards high-performance spatially parallel optical reservoir computing." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306372.
Lugnan, Alessio, Joni Dambre, and Peter Bienstman. "Integrated dielectric scatterers for fast optical classification of biological cells." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306654.
Denis-le Coarer, Florian, Matthias Freiberger, Joni Dambre, Peter Bienstman, Damien Rontani, Andrew Katumba, and Marc Sciamanna. "Toward neuro-inspired computing using a small network of micro-ring resonators on an integrated photonic chip." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306780.
Röhm, André, and Kathy Lüdge. "Reservoir computing with delay in structured networks." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2307159.
Harkhoe, Krishan, and Guy Van der Sande. "Dual-mode semiconductor lasers in reservoir computing." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2307328.
"Front Matter: Volume 10689." In Neuro-inspired Photonic Computing, edited by Marc Sciamanna and Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2502806.
Tee, Benjamin. "Neuro-inspired Skins." In Neural Interfaces and Artificial Senses. València: Fundació Scito, 2021. http://dx.doi.org/10.29363/nanoge.nias.2021.019.
Doutsi, Effrosyni, Lionel Fillatre, Marc Antonini, and Julien Gaulmin. "Neuro-Inspired Quantization." In 2018 25th IEEE International Conference on Image Processing (ICIP). IEEE, 2018. http://dx.doi.org/10.1109/icip.2018.8451793.
Reports on the topic "Neuro inspiré":
Okandan, Murat. 2015 Neuro-Inspired Computational Elements (NICE) Workshop: Information Processing and Computation Systems beyond von Neumann/Turing Architecture and Moore’s Law Limits (Summary Report). Office of Scientific and Technical Information (OSTI), March 2015. http://dx.doi.org/10.2172/1177593.
Grubbs, Daniel. Summary Report from 2015 Neuro-Inspired Computational Elements (NICE) Workshop, February 23-25, 2015. Information Processing and Computation Systems beyond von Neumann/Turing Architecture and Moore’s Law Limits. Office of Scientific and Technical Information (OSTI), December 2015. http://dx.doi.org/10.2172/1470994.