Bibliografie tematiche / Neuro inspiré

Letteratura scientifica selezionata sul tema "Neuro inspiré"

Autore: Grafiati
Pubblicato: 8 giugno 2024

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Articoli di riviste sul tema "Neuro inspiré":

1

Ghani, Arfan, Thomas Dowrick e Liam J. McDaid. "OSPEN: an open source platform for emulating neuromorphic hardware". International Journal of Reconfigurable and Embedded Systems (IJRES) 12, n. 1 (1 marzo 2023): 1. http://dx.doi.org/10.11591/ijres.v12.i1.pp1-8.

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This paper demonstrates a framework that entails a bottom-up approach to accelerate research, development, and verification of neuro-inspired sensing devices for real-life applications. Previous work in neuromorphic engineering mostly considered application-specific designs which is a strong limitation for researchers to develop novel applications and emulate the true behaviour of neuro-inspired systems. Hence to enable the fully parallel brain-like computations, this paper proposes a methodology where a spiking neuron model was emulated in software and electronic circuits were then implemented and characterized. The proposed approach offers a unique perspective whereby experimental measurements taken from a fabricated device allowing empirical models to be developed. This technique acts as a bridge between the theoretical and practical aspects of neuro-inspired devices. It is shown through software simulations and empirical modelling that the proposed technique is capable of replicating neural dynamics and post-synaptic potentials. Retrospectively, the proposed framework offers a first step towards open-source neuro-inspired hardware for a range of applications such as healthcare, applied machine learning and the internet of things (IoT).
2

Zhang, Wenqiang, Bin Gao, Jianshi Tang, Peng Yao, Shimeng Yu, Meng-Fan Chang, Hoi-Jun Yoo, He Qian e Huaqiang Wu. "Neuro-inspired computing chips". Nature Electronics 3, n. 7 (luglio 2020): 371–82. http://dx.doi.org/10.1038/s41928-020-0435-7.

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3

Birzhanova, Aigerim, Aliya Nurgaliyeva, Azhar Nurmagambetova, Hasan Dinçer e Serhat Yüksel. "Neuro quantum-inspired decision-making for investor perception in green and conventional bond investments". Investment Management and Financial Innovations 21, n. 1 (9 febbraio 2024): 168–84. http://dx.doi.org/10.21511/imfi.21(1).2024.14.

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The purpose of this study is to make a comprehensive analysis of investor perceptions in the context of green and conventional bond investments. For this purpose, a new model is presented by considering two steps. First, a criteria set is generated by considering balanced scorecard perspectives that are finance, customer, organizational effectiveness and learning and growth. After that, the neuro Quantum fuzzy M-SWARA method is considered to weight these criteria. Secondly, seven critical determinants for bond investments are identified that are coupon rates, volume, maturity, riskiness, liquidity, volatility, and tax considerations. Neuro Quantum fuzzy TOPSIS approach is employed to rank these factors. The main contribution of the study is that by combining the balanced scorecard framework and quantum-inspired decision-making techniques, this paper offers a novel and sophisticated decision-making model to understanding investor behavior. Similarly, in the proposed model, a new methodology is generated by the name of M-SWARA. In this framework, some enhancements are adopted to the SWARA technique. The weighting results indicate that meeting customer expectations is the most critical factor that affects the investor perception to make investments to the bonds. Moreover, according to the ranking results, it is concluded that coupon rates are the most important item for both conventional and green bond investors. On the other hand, with respect to the conventional bond investor, tax is the second most essential factor. However, regarding the green bond investors, volatility plays a critical role. AcknowledgmentThis research has been/was/is funded by the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan (№ AP 19679105 “Transformation of ESG financial instruments in the context of the development of the green economy of the Republic of Kazakhstan”).
4

Harkhoe, Krishan, Guy Verschaffelt e Guy Van der Sande. "Neuro-Inspired Computing with Spin-VCSELs". Applied Sciences 11, n. 9 (7 maggio 2021): 4232. http://dx.doi.org/10.3390/app11094232.

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Delay-based reservoir computing (RC), a neuromorphic computing technique, has gathered lots of interest, as it promises compact and high-speed RC implementations. To further boost the computing speeds, we introduce and study an RC setup based on spin-VCSELs, thereby exploiting the high polarization modulation speed inherent to these lasers. Based on numerical simulations, we benchmarked this setup against state-of-the-art delay-based RC systems and its parameter space was analyzed for optimal performance. The high modulation speed enabled us to have more virtual nodes in a shorter time interval. However, we found that at these short time scales, the delay time and feedback rate heavily influence the nonlinear dynamics. Therefore, and contrary to other laser-based RC systems, the delay time has to be optimized in order to obtain good RC performances. We achieved state-of-the-art performances on a benchmark timeseries prediction task. This spin-VCSEL-based RC system shows a ten-fold improvement in processing speed, which can further be enhanced in a straightforward way by increasing the birefringence of the VCSEL chip.
5

Zhong, Xiaopin, e Lin Ma. "A Neuro-inspired Adaptive Motion Detector". Optics and Photonics Journal 03, n. 02 (2013): 94–98. http://dx.doi.org/10.4236/opj.2013.32b024.

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6

Huang, Ping-Chen, e Jan M. Rabaey. "A Neuro-Inspired Spike Pattern Classifier". IEEE Journal on Emerging and Selected Topics in Circuits and Systems 8, n. 3 (settembre 2018): 555–65. http://dx.doi.org/10.1109/jetcas.2018.2842035.

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7

Kahol, Kanav, e Sethuraman Panchanathan. "Neuro-cognitively inspired haptic user interfaces". Multimedia Tools and Applications 37, n. 1 (6 settembre 2007): 15–38. http://dx.doi.org/10.1007/s11042-007-0167-y.

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8

GINGL, ZOLTAN, LASZLO B. KISH e SUNIL P. KHATRI. "TOWARDS BRAIN-INSPIRED COMPUTING". Fluctuation and Noise Letters 09, n. 04 (dicembre 2010): 403–12. http://dx.doi.org/10.1142/s0219477510000332.

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We present introductory considerations and analysis toward computing applications based on the recently introduced deterministic logic scheme with random spike (pulse) trains [Phys. Lett. A373 (2009) 2338–2342]. Also, in considering the questions, "why random?" and "why pulses?", we show that the random pulse based scheme provides the advantages of realizing multivalued deterministic logic. Pulse trains are realized by an element called orthogonator. We discuss two different types of orthogonators, parallel (intersection-based) and serial (demultiplexer-based) orthogonators. The last one can be slower but it makes sequential logic design straightforward. We propose generating a multidimensional logic hyperspace [Phys. Lett. A373 (2009) 1928–1934] by using the zero-crossing events of uncorrelated Gaussian electrical noises available in the chips. The spike trains in the hyperspace are non-overlapping, and are referred to as neuro-bits. To demonstrate this idea, we generate three-dimensional hyperspace bases using the zero-crossing events of two uncorrelated Gaussian noise sources. In such a scenario, the detection of different hyperspace basis elements may have vastly differing delays. We show that it is possible to provide an identical speed for the detection of all the hyperspace bases elements using correlated noise sources, and demonstrate this for the two neuro-bits situation. The key impact of this paper is to demonstrate that a logic design approach using such neuro-bits can yield a fast, low power and environmental variation tolerant means of designing computer circuitry. It also enables the realization of multivalued logic, and also significantly increasing the complexity of computer circuits by allowing several neuro-bits to be transmitted on a single wire.
9

Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski e Andrea Ehrmann. "Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers". Biomimetics 6, n. 2 (26 maggio 2021): 32. http://dx.doi.org/10.3390/biomimetics6020032.

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Computers nowadays have different components for data storage and data processing, making data transfer between these units a bottleneck for computing speed. Therefore, so-called cognitive (or neuromorphic) computing approaches try combining both these tasks, as is done in the human brain, to make computing faster and less energy-consuming. One possible method to prepare new hardware solutions for neuromorphic computing is given by nanofiber networks as they can be prepared by diverse methods, from lithography to electrospinning. Here, we show results of micromagnetic simulations of three coupled semicircle fibers in which domain walls are excited by rotating magnetic fields (inputs), leading to different output signals that can be used for stochastic data processing, mimicking biological synaptic activity and thus being suitable as artificial synapses in artificial neural networks.
10

Yu, Shimeng. "Neuro-Inspired Computing With Emerging Nonvolatile Memorys". Proceedings of the IEEE 106, n. 2 (febbraio 2018): 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.

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Ti possono interessare anche gli elenchi estesi di opere sul tema "Neuro inspiré":
Articoli di riviste Tesi Libri

Tesi sul tema "Neuro inspiré":

1

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.

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Dans cette thèse, nous proposons d'intégrer la notion d'habitude comportementale au sein d'une architecture de contrôle robotique, et d'étudier son interaction avec les mécanismes générant le comportement planifié. Les architectures de contrôle robotiques permettent à ce dernier d'être utilisé efficacement dans le monde réel et au robot de rester réactif aux changements dans son environnement, tout en étant capable de prendre des décisions pour accomplir des buts à long terme (Kortenkamp et Simmons, 2008). Or, ces architectures sont rarement dotées de capacités d'apprentissage leur permettant d'intégrer les expériences précédentes du robot. En neurosciences et en psychologie, l'étude des différents types d'apprentissage montre pour que ces derniers sont une capacité essentielle pour adapter le comportement des mammifères à des contextes changeants, mais également pour exploiter au mieux les contextes stables (Dickinson, 1985). Ces apprentissages sont modélisés par des algorithmes d'apprentissage par renforcement direct et indirect (Sutton et Barto, 1998), combinés pour exploiter leurs propriétés au mieux en fonction du contexte (Daw et al., 2005). Nous montrons que l'architecture proposée, qui s'inspire de ces modèles du comportement, améliore la robustesse de la performance lors d'un changement de contexte dans une tâche simulée. Si aucune des méthodes de combinaison évaluées ne se démarque des autres, elles permettent d'identifier les contraintes sur le processus de planification. Enfin, l'extension de l'étude de notre architecture à deux tâches (dont l'une sur robot réel) confirme que la combinaison permet l'amélioration de l'apprentissage du robot
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
2

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.

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Abstract (sommario):
Dans cette thèse, nous proposons d'intégrer la notion d'habitude comportementale au sein d'une architecture de contrôle robotique, et d'étudier son interaction avec les mécanismes générant le comportement planifié. Les architectures de contrôle robotiques permettent à ce dernier d'être utilisé efficacement dans le monde réel et au robot de rester réactif aux changements dans son environnement, tout en étant capable de prendre des décisions pour accomplir des buts à long terme (Kortenkamp et Simmons, 2008). Or, ces architectures sont rarement dotées de capacités d'apprentissage leur permettant d'intégrer les expériences précédentes du robot. En neurosciences et en psychologie, l'étude des différents types d'apprentissage montre pour que ces derniers sont une capacité essentielle pour adapter le comportement des mammifères à des contextes changeants, mais également pour exploiter au mieux les contextes stables (Dickinson, 1985). Ces apprentissages sont modélisés par des algorithmes d'apprentissage par renforcement direct et indirect (Sutton et Barto, 1998), combinés pour exploiter leurs propriétés au mieux en fonction du contexte (Daw et al., 2005). Nous montrons que l'architecture proposée, qui s'inspire de ces modèles du comportement, améliore la robustesse de la performance lors d'un changement de contexte dans une tâche simulée. Si aucune des méthodes de combinaison évaluées ne se démarque des autres, elles permettent d'identifier les contraintes sur le processus de planification. Enfin, l'extension de l'étude de notre architecture à deux tâches (dont l'une sur robot réel) confirme que la combinaison permet l'amélioration de l'apprentissage du robot
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
3

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.

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Cette thèse s'inscrit dans le contexte de l'étude des circuits neuromorphiques utilisant des dispositifs memristifs comme synapses. Son objectif principal est d'évaluer les mérites d'une nouvelle classe de mémoires organiques développées au LICSEN (CEA Saclay/IRAMIS) et, plus particulièrement, leur adéquation avec les propositions d'implémentation et les règles d'apprentissage proposées par l'équipe NanoArchi de l'IEF (Univ. Paris-Sud, Orsay). Les memristors étudiés sont basés sur l'electro-greffage en films minces de complexes organiques redox pour la formation de jonctions métal/molécules/métal robustes et scalables. Outre la fabrication de memristors, le travail inclut d'importants efforts de caractérisation électrique (vitesse, non-volatilité, scalabilité, robustesse, etc.) visant d'une part à étudier les mécanismes de commutation dans ces nouveaux matériaux memristifs organiques, et d'autres part, à évaluer leur potentiel en tant que synapses. Cette thèse présente également une étude préparatoire à la réalisation d'un démonstrateur de circuit mixte de type réseaux de neurones combinant nano-memristors et électronique conventionnelle (programmabilité des dispositifs en mode impulsionnel, réalisation d'assemblées de dispositifs, variabilité). De plus, la démonstration de la compatibilité de ces memristors avec la propriété STDP (Spike Timing Dependent Plasticity) ainsi que de l’apprentissage d’un « réflexe conditionné » ouvrent la voie aux apprentissages non-supervisés
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
4

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.

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Le traitement actuel de l'information dans les grands clusters de calcul, est responsable d'un fort impact énergétique au niveau mondial. Le paradigme actuel est à repenser, et une architecture de calcul basée sur des composants photoniques (laser à semi-conducteur notamment) est étudiée dans cette thèse. La structure envisagée est un réseau de neurones artificiels pour du traitement de données de télécommunications. Nous étudions une diode laser et ses états dynamiques lorsque soumise à une injection optique et à un feedback optiques simultanés et les liens avec sa capacité de calcul neuroinspirée par de la simulation et de l'expérimentation
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
5

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.

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Une boucle de rétroaction à retard a lieu lorsque la sortie d’un système est utilisée pour modifier le signal d’entrée de ce dernier. Ce phénomène apparaît dans des domaines aussi variés que la physique des amplificateurs, la biologie de la régulation de l’insuline ou encore les sciences sociales. Les effets d’une boucle de rétroaction à retard sur un système électronique sont bien connus et ont donné lieu à de nombreuses applications : boucle à verrouillage de phase pour améliorer les propriétés stochastiques, boucle d’amplification ou de régulation, etc. Cependant ces effets ont étés relativement peu étudiés dans le cadre des systèmes nanomagnétiques. Dans ces travaux de thèse j'ai étudié théoriquement les conséquences d'une boucle de rétroaction à retard sur la dynamique de l'aimantation de trois différents systèmes nanométriques avec un objectif distinct pour chaque système. Le premier concerne un oscillateur à transfert de spin dont j’ai étudié les propriétés stochastiques. La rétroaction engendre de fortes variations de la largeur spectrale et fait apparaitre de bandes secondaires à larges retards. Le deuxième système étudié est l'oscillateur macrospin dans lequel des transitions chaotiques entre deux modes de précession (dans le plan de la couche et hors du plan) sont induites par la rétroaction. Je montre qu'il est possible d'exploiter de telle dynamique pour la génération de nombres aléatoires. Enfin le troisième système représente une implémentation d'un oscillateur du type « Mackey-Glass » avec une paroi de domaine piégée dans un ruban. En déformant cette paroi par courant polarisé de spin, et avec un choix judicieux du signal de sortie, je démontre que ce système peut servir comme élément de base pour une architecture temporelle d'un calculateur avec réservoir (« reservoir computer »), qui permet d'effectuer des tâches comme la prédiction des séries temporelles non linéaires
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
6

Chabi, Djaafar. "Architectures de circuits nanoélectroniques neuro-inspirée". Phd thesis, Université Paris Sud - Paris XI, 2012. http://tel.archives-ouvertes.fr/tel-00679300.

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Les nouvelles techniques de fabrication nanométriques comme l'auto-assemblage ou la nanoimpression permettent de réaliser des matrices régulières (crossbars) atteignant des densités extrêmes (jusqu'à 1012 nanocomposants/cm2) tout en limitant leur coût de fabrication. Cependant, il est attendu que ces technologies s'accompagnent d'une augmentation significative du nombre de défauts et de dispersions de caractéristiques. La capacité à exploiter ces crossbars est alors conditionnée par le développement de nouvelles techniques de calcul capables de les spécialiser et de tolérer une grande densité de défauts. Dans ce contexte, l'approche neuromimétique qui permet tout à la fois de configurer les nanodispositifs et de tolérer leurs défauts et dispersions de caractéristiques apparaît spécialement pertinente. L'objectif de cette thèse est de démontrer l'efficacité d'une telle approche et de quantifier la fiabilité obtenue avec une architecture neuromimétique à base de crossbar de memristors, ou neurocrossbar (NC). Tout d'abord la thèse introduit des algorithmes permettant l'apprentissage de fonctions logiques sur un NC. Par la suite, la thèse caractérise la tolérance du modèle NC aux défauts et aux variations de caractéristiques des memristors. Des modèles analytiques probabilistes de prédiction de la convergence de NC ont été proposés et confrontés à des simulations Monte-Carlo. Ils prennent en compte l'impact de chaque type de défaut et de dispersion. Grâce à ces modèles analytiques il devient possible d'extrapoler cette étude à des circuits NC de très grande taille. Finalement, l'efficacité des méthodes proposées est expérimentalement démontrée à travers l'apprentissage de fonctions logiques par un NC composé de transistors à nanotube de carbone à commande optique (OG-CNTFET).
7

Vatin, Jeremy. "Photonique neuro-inspirée pour des applications télécoms". Electronic Thesis or Diss., CentraleSupélec, 2020. http://www.theses.fr/2020CSUP0004.

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Nous produisons chaque jour de grandes quantités de données, que nous échangeons sur le réseau Internet. Ces données sont traitées grâce à des clusters de calcul, responsables de la consommation énergétique d’internet. Dans cette thèse, nous étudions une architecture faite de composants photoniques, pour se débarrasser des composants électroniques consommant de l'énergie. Grâce aux composants actuellement utilisés dans le réseau Internet (laser et fibre optique), nous réalisons un réseau neuronal artificiel capable de traiter les données de télécommunication. Le réseau de neurones artificiel est constitué d'un laser et d'une fibre optique qui renvoie la lumière dans ce laser. Le comportement complexe de ce système est utilisé pour alimenter les neurones artificiels qui sont répartis le long de la fibre. Nous sommes en mesure de prouver que ce système est capable de traiter soit un signal avec une grande efficacité, soit deux signaux au prix d'une petite perte de précision
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
8

Causo, Matteo. "Neuro-Inspired Energy-Efficient Computing Platforms". Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10004/document.

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Les Big Data mettent en évidence tous les défauts du paradigme de l'informatique classique. Au contraire, le Neuro-Inspiré traite les Big Data comme ressources pour progresser. Dans cette thèse, nous adoptons les principes de Hierarchical Temporal Memory (HTM) comme références neuroscientifiques et nous élaborons sur la façon dont le Bayesian Machine Learning (BML) mène les approches dans le Neuro-Inspiré à s’unifier et à atteindre nos objectives: (i) la simplification et l'amélioration des algorithmes de BML et (ii) l'approche au Neuro-Inspiré avec une prospective Ultra-Low-Power. Donc, nous nous efforçons d'apporter le traitement intelligent proche aux sources de données et de populariser le BML sur l'électronique strictement limitées tels que les appareils portables, mettable et implantables. Cependant, les algorithmes de BML ont besoin d’être optimisés. En fait, leur mise en œuvre en HW est ni efficaces, ni réalisables en raison de la mémoire, la puissance de calcul requises. Nous proposons un algorithme moins complexe, en ligne, distribué et non paramétrique et montrons de meilleurs résultats par rapport aux solutions de l’état de l’art. En fait, nous gagnons deux ordres de grandeur de réduction en complexité au niveau algorithmique et un autre ordre de grandeur grâce à des techniques traditionnelles d'optimisation HW. En particulier, nous concevons une preuve de concept sur une plateforme FPGA pour l'analyse en temps réel d’un flux de données. Enfin, nous démontrons d’être en mesure de résumer les ultimes découvertes du domaine du BML sur un algorithme généralement valide qui peut être mis en œuvre en HW et optimisé pour des applications avec des ressources limitées
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
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Hirtzlin, Tifenn. "Digital Implementation of Neuromorphic systems using Emerging Memory devices". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPAST071.

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Abstract (sommario):
Depuis les années soixante-dix l'évolution des performances des circuits électroniques repose exclusivement sur l'amélioration des performances des transistors. Ce composant a des propriétés extraordinaires puisque lorsque ses dimensions sont réduites, toutes ses caractéristiques sont améliorées. Mais, dû à certaines limites physiques fondamentales, la diminution des dimensions des transistors n’est plus possible. Néanmoins, de nouveaux nano-composants mémoire innovants qui peuvent être intégré conjointement avec les transistors voient le jour tant au niveau académique qu'industriel, ce qui constitue une opportunité pour repenser complètement l'architecture des circuits électroniques actuels. L'une des voies de recherche possible est l’inspiration du fonctionnement du cerveau biologique. Ce dernier peut accomplir des tâches complexes et variées en consommant très peu d’énergie. Ces travaux de thèse explorent trois paradigmes neuro-inspirés pour l'utilisation de ces composants mémoire. Chacune de ces approches explore différentes problématiques du calcul en mémoire
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
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Aboudib, Ala. "Neuro-inspired Architectures for the Acquisition and Processing of Visual Information". Thesis, Télécom Bretagne, 2016. http://www.theses.fr/2016TELB0419/document.

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L'apprentissage automatique et la vision par ordinateur sont deux sujets de recherche d'actualité. Des contributions clés à ces domaines ont été les fruits de longues années d'études du cortex visuel et de la fonction des réseaux cérébraux. Dans cette thèse, nous nous intéressons à la conception des architectures neuro-inspirées pour le traitement de l'information sur trois niveaux différents du cortex visuel. Au niveau le plus bas, nous proposons un réseau de neurones pour l'acquisition des signaux visuels. Ce modèle est étroitement inspiré par le fonctionnement et l'architecture de la retine et les premières couches du cortex visuel chez l'humain. Il est également adapté à l'émulation des mouvements oculaires qui jouent un rôle important dans notre vision. Au niveau le plus haut, nous nous intéressons à la mémoire. Nous traitons un modèle de mémoire associative basée sur une architecture neuro-inspirée dite `Sparse Clustered Network (SCN)'. Notre contribution principale à ce niveau est de proposer une amélioration d'un algorithme utilisé pour la récupération des messages partiellement effacés du SCN. Nous suggérons également une formulation générique pour faciliter l'évaluation des algorithmes de récupération, et pour aider au développement des nouveaux algorithmes. Au niveau intermédiaire, nous étendons l'architecture du SCN pour l'adapter au problème de la mise en correspondance des caractéristiques d'images, un problème fondamental en vision par ordinateur. Nous démontrons que la performance de notre réseau atteint l'état de l'art, et offre de nombreuses perspectives sur la façon dont les architectures neuro-inspirées peuvent servir de substrat pour la mise en oeuvre de diverses tâches de vision
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
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Libri sul tema "Neuro inspiré":

1

Yu, Shimeng, a cura di. Neuro-inspired Computing Using Resistive Synaptic Devices. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54313-0.

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1966-, Arena Paolo, e International Centre for Mechanical Sciences., a cura di. Dynamical systems, wave-based computation and neuro-inspired robots. Wien: Springer, 2008.

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Arena, Paolo, a cura di. Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots. Vienna: Springer Vienna, 2008. http://dx.doi.org/10.1007/978-3-211-78775-5.

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Patanè, Luca, Roland Strauss e 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.

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Roberta, Allen. The playful way to knowing yourself: A creative workbook to inspire self-discovery. Boston: Houghton Mifflin, 2003.

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Cairo, Jim. Motivation and goal-setting: How to set and achieve goals and inspire others. Franklin Lakes, NJ: Career Press, 1998.

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CAPPY. Traitement Neuro-Inspire de Linformation. ISTE Editions Ltd., 2020.

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Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.

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Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.

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Cappy, Alain. Neuro-Inspired Information Processing. Wiley & Sons, Incorporated, John, 2020.

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Capitoli di libri sul tema "Neuro inspiré":

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Strisciuglio, Nicola, e 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.

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Abstract (sommario):
AbstractThe study of the visual system of the brain has attracted the attention and interest of many neuro-scientists, that derived computational models of some types of neuron that compose it. These findings inspired researchers in image processing and computer vision to deploy such models to solve problems of visual data processing.In this paper, we review approaches for image processing and computer vision, the design of which is based on neuro-scientific findings about the functions of some neurons in the visual cortex. Furthermore, we analyze the connection between the hierarchical organization of the visual system of the brain and the structure of Convolutional Networks (ConvNets). We pay particular attention to the mechanisms of inhibition of the responses of some neurons, which provide the visual system with improved stability to changing input stimuli, and discuss their implementation in image processing operators and in ConvNets.
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Lewis, Frank L., e 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.

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Lewis, Frank L., e 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.

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Habekost, Jan-Gerrit, Erik Strahl, Philipp Allgeuer, Matthias Kerzel e 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.

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AbstractThe paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task—a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a standalone fashion, but we also show how embedding these into a hybrid neuro-genetic IK pipeline allows for further optimization via sequential least-squares programming (SLSQP) or a genetic algorithm (GA). The models are trained and tested on dense datasets that were collected from random robot configurations of the new Neuro-Inspired COLlaborator (NICOL), a semi-humanoid robot with two redundant 8-DoF manipulators. We utilize the weighted multi-objective function from the state-of-the-art BioIK method to support the training process and our hybrid neuro-genetic architecture. We show that the neural models can compete with state-of-the-art IK approaches, which allows for deployment directly to robotic hardware. Additionally, it is shown that the incorporation of the genetic algorithm improves the precision while simultaneously reducing the overall runtime.
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Patanè, Luca, Roland Strauss e 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.

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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.

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Madani, Kurosh, Ghislain de Trémiolles e 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.

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Patel, Leena N., e 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.

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Amudha, J., e 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.

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Kadetotad, Deepak, Pai-Yu Chen, Yu Cao, Shimeng Yu e 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.

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Atti di convegni sul tema "Neuro inspiré":

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Krasilenko, Vladimir G., Alexander Lazarev e 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, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2316352.

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Kassa, Wosen, Evangelia Dimitriadou, Marc Haelterman, Serge Massar e Erwin Bente. "Towards integrated parallel photonic reservoir computing based on frequency multiplexing". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306176.

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Pauwels, Jaël, Guy Van der Sande, Arno Bouwens, Marc Haelterman e Serge Massar. "Towards high-performance spatially parallel optical reservoir computing". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306372.

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Lugnan, Alessio, Joni Dambre e Peter Bienstman. "Integrated dielectric scatterers for fast optical classification of biological cells". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306654.

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Denis-le Coarer, Florian, Matthias Freiberger, Joni Dambre, Peter Bienstman, Damien Rontani, Andrew Katumba e Marc Sciamanna. "Toward neuro-inspired computing using a small network of micro-ring resonators on an integrated photonic chip". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2306780.

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Röhm, André, e Kathy Lüdge. "Reservoir computing with delay in structured networks". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2307159.

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Harkhoe, Krishan, e Guy Van der Sande. "Dual-mode semiconductor lasers in reservoir computing". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2307328.

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"Front Matter: Volume 10689". In Neuro-inspired Photonic Computing, a cura di Marc Sciamanna e Peter Bienstman. SPIE, 2018. http://dx.doi.org/10.1117/12.2502806.

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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.

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Doutsi, Effrosyni, Lionel Fillatre, Marc Antonini e 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.

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Rapporti di organizzazioni sul tema "Neuro inspiré":

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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), marzo 2015. http://dx.doi.org/10.2172/1177593.

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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), dicembre 2015. http://dx.doi.org/10.2172/1470994.

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