Relevant bibliographies by topics / Neuro inspired

Academic literature on the topic 'Neuro inspired'

Author: Grafiati
Published: 8 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Neuro inspired":

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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.

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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.

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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).
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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.

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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.
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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.

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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.

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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.

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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.

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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.
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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.

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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.
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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.

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Dumitrache, Ioan, Simona Iuliana Caramihai, Mihnea Alexandru Moisescu, and Ioan Stefan Sacala. "Neuro-inspired Framework for cognitive manufacturing control." IFAC-PapersOnLine 52, no. 13 (2019): 910–15. http://dx.doi.org/10.1016/j.ifacol.2019.11.311.

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You might also be interested in the extended bibliographies on the topic 'Neuro inspired' for particular source types:
Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Neuro inspired":

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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
2

Mokhtar, Maizura. "Bio-Inspired Autonomous Hardware Neuro-controller Device on an FPGA Inspired by the Hippocampus." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490697.

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One method in achieving artificial intelligence is by emulating biological concepts onto an electronic device, specifically how a biological organism governs its behaviour. This research project investigates how the hippocampus works; and attempts to model this region of the brain onto an electronic device. The hippocampus is chosen because this is one of the regions in the brain responsible for learning and memory. This study uses models of the pyramidal neurons in the hippocampus as well as its spatial representation as the design components for a hardware neuro-controller module. The method chosen to model the individual neurons is the two-dimensional bio-inspired Izhikevich algorithm that has the ability to describe a variety of neuron dynamic behaviours observed in the brain. The hippocampus-inspired spiking neural network architecture also includes place cells/place field representation, a rate-based representation that provides spatial representation of the environment to the hippocampus. A biological nervous system is a dynamical system; it is governed by the learning rules that adjust the strength of connectivity between the neurons in the neural network. These learning rules are implemented to the hippocampus-inspired spiking neural network to allow the neural network to perform its task of path navigation. Following successful simulations of the software prototype of the neural network architecture in performing its desired task, this architecture is then synthesized onto a Field Programmable Gate Array (FPGA) device. This is to allow the neural network architecture to be utilized as a neuro-controller device for the purpose of path navigation, creates memories, and thus achieving autonomy.
3

Khan, Gul Muhammad. "Evolution of neuro-inspired Developmental Programs Capable of Learning." Thesis, University of York, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.490693.

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ABSTRACT. In this work, a type of developmental brain-inspired computational network is presented and evaluated. It is based on the idea of evolving programs that build a computational neural structure. This thesis describes an artificial model of the brain based on evolutionary computation and neurodevelopmental techniques. This model is more biologically plausible than earlier techniques and demonstrates that adding more biological plausibility can enhance the computational power of the neural systems. The thesis demonstrates the capabilities of this brain inspired system on two different learning problems.
4

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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PINHO, ANDERSON GUIMARAES DE. "QUANTUM-INSPIRED EVOLUCIONARY ALGORITHM WITH MIXED REPRESENTATION APPLIED TO NEURO-EVOLUTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2010. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=17224@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Esta dissertação objetivará a unificação de duas metodologias de algoritmos evolutivos consagradas para tratamento de problemas ou do tipo combinatórios, ou do tipo numéricos, num único algoritmo com representação mista. Trata-se de um algoritmo evolutivo inspirado na física quântica com representação mista binário-real do espaço de soluções, o AEIQ-BR. Este algoritmo trata-se de uma extensão do modelo com representação binária de Jang, Han e Kin, o AEIQ-B para otimizações combinatoriais, e o de representação real de Abs da Cruz, o AEIQ-R para otimizações numéricas. Com fins de exemplificação do novo algoritmo proposto, o discutiremos no contexto de neuroevolução, com o propósito de configurar completamente uma rede neural com alimentação adiante em termos: seleção de variáveis de entrada; números de neurônios na camada escondida; todos os pesos existentes; e tipos de funções de ativação de cada neurônio. Esta finalidade em se aplicar o algoritmo AEIQ-BR à neuroevolução – e também, numa analogia ao modelo NEIQ-R de Abs da Cruz – receberá a denominação NEIQ-BR. N de neuroevolução, E de evolutivo, IQ de inspiração quântica, e BR de binário-real. Para avaliar o desempenho do NEIQ-BR, utilizarse- á um total de seis casos benchmark de classificação, e outros dois casos reais, em campos da ciência como: finanças, biologia e química. Resultados serão comparados com algoritmos de outros pesquisadores e a modelagem manual de redes neurais, através de medidas de desempenho. Através de testes estatísticos concluiremos que o algoritmo NEIQ-BR apresentará um desempenho significativo na obtenção de previsões de classificação por neuroevolução.
This work aimed to unify two methodologies of evolutionary algorithms to treat problems with or combinatorial characteristics, or numeric, on a unique algorithm with mix representation. It is an evolutionary algorithm inspired in quantum physics with mixed representation of the solutions space, called QIEABR. This algorithm is an extension of the model with binary representation of the chromosome from Jang, Han e Kin, the QIEA-B for combinatorial optimization, and numeric representation from Abs da Cruz, the QIEA-R for numerical optimizations. For purposes of exemplification of the new algorithm, we will introduce the algorithm in the context of neuro-evolution, in order to completely configure a feed forward neural network in terms of: selection of input variables; numbers of neurons in the hidden layer; all existing synaptic weights; and types of activation functions of each neuron. This purpose when applying the algorithm QIEA-BR to neuro-evolution receive the designation of QIEN-BR. QI for quantum-inspired, E for evolutive, N for neuro-evolution, and BR for binary-real representation. To evaluate the performance of QIEN-BR, we will use a total of six benchmark cases of classification, and two real cases in fields of science such as finance, biology and chemistry. Results will be compared with algorithms of other researchers and manual modeling of neural networks through performance measures. Statistical tests will be provided to elucidate the significance of results, and what we can conclude is that the algorithm QIEN-BR better performance others researchers in terms of classification prediction.
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Liu, Yang. "A neuro-immune inspired computational framework and its applications to a machine visual tracking system." Thesis, University of York, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.516625.

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Vincent, Adrien F. "Vers une utilisation synaptique de composants mémoires innovants pour l’électronique neuro-inspirée." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLS034/document.

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Les réseaux de neurones artificiels, dont le concept s'inspire du fonctionnement des cerveaux biologiques et de leurs capacités d'apprentissage, sont une approche prometteuse pour répondre aux nouveaux usages informatiques dits « cognitifs », tels que la reconnaissance d'images ou l'interaction en langage naturel. Néanmoins, leur mise en œuvre par des ordinateurs conventionnels est peu efficace. Une solution à ce problème est le développement de puces d'accélération matérielle spécialisées qui comportent :- des neurones, unités de traitement de l'information, pour lesquelles des circuits électroniques efficaces existent ;- des synapses, reliant les neurones mais aussi support matériel de l'apprentissage, par le biais de la modulation de leur conductance électrique (qualifiée de « plasticité synaptique »). Réaliser des synapses artificielles intégrables densément et capables d'apprendre in situ reste aujourd'hui un défi majeur.Ces travaux de thèse portent sur l'utilisation synaptique de nanocomposants mémoires innovants, dont certains comportements plastiques riches et intrinsèques sont analogues aux fonctionnalités que nous recherchons.Nous nous intéressons tout d'abord aux jonctions tunnel magnétiques à transfert de spin, développées dans l'industrie pour concevoir de nouvelles mémoires informatiques non volatiles. Nous montrons qu'il est aussi possible d'en faire des synapses artificielles binaires. Après la modélisation analytique de leur comportement naturellement stochastique, nous présentons comment exploiter ce dernier pour faciliter la mise en œuvre in situ d'une règle d'apprentissage probabiliste. À l'aide d'outils de simulation développés au laboratoire, nous étudions l'influence du régime de programmation sur la robustesse d'un système à la variabilité de telles synapses et sur leur consommation énergétique.Nous nous tournons ensuite vers des cellules électrochimiques métalliques Ag2S, d'autres nanocomposants mémoires innovants fabriqués et étudiés par des collaborateurs de l'Université de Lille I, qui y ont déjà observé plusieurs comportements plastiques. Nous avons découvert une plasticité supplémentaire, proche d'un comportement observé en neurosciences. Grâce à un modèle analytique simple permettant de comprendre les relations entre les différentes plasticités, nous montrons en simulation une preuve de concept d'apprentissage non supervisé qui repose sur l'interaction de ces multiples comportements.Pour finir, nous soulevons des pistes de réflexion sur les défis posés par les circuits nécessaires au bon fonctionnement d'un système utilisant comme synapses artificielles les nanocomposants étudiés, notamment lors de la lecture ou de l'écriture de ces derniers.Les résultats de cette thèse ouvrent la voie à la conception de systèmes neuro-inspirés capables d'apprendre en s'appuyant sur la richesse de comportements plastiques offerte par les nanocomposants mémoires innovants
Artificial neural networks, which take some inspiration from the behavior of biological brains and their learning capabilities, are promising tools to address emerging computing uses known as “cognitive” tasks like classifying images or natural language interaction. However, implementing them on conventional computers is poorly efficient. A solution to this problem is to develop specialized acceleration chips which feature:• neurons, the information processing units, which can be implemented efficienctly with current electronic technologies;• synapses, the connections between the neurons which also support the learning process by adjusting their electrical conductance (“synaptic plasticity”). Implementing artificial synapses with high integration and on-line learning capabilities is still a challenge.This thesis explores the use of innovative memory nanodevices as artificial synapses: some of their rich plastic behaviors naturally implement features that are difficult to access with other devices.First, we investigate spin-transfer torque magnetic tunnel junctions, that are currently develop in industry as a new non volatile memory technology. We show that they can also be used as binary artificial synapses. After modeling their intrinsic stochastic behavior analytically, we describe how to harness this behavior to facilitate the implementation of an on-line probabilistic learning rule. With simulations tools developped in the laboratory, we detail the impact of the programming regime on the resilience of a system that uses such synapses, as well as on the system's power consumptionWe then investigate Ag2S electrochemical metalization cells, another type of innovative memory nanodevices fabricated and characterized by collaborators from Université de Lille I, who had already observed the existence of several plastic behaviors. We discovered an additional plasticity, close to a behavior known in neurosciences. With a simple analytical model that allows a better understanding of the relationships between theses plasticities, we show by simulations means a proof of concept of an unsupervised learning that relies on the interaction of the plastic behaviors theses nanodevices feature.Finally, we consider the challenges arising from the circuits that are required to read and write such artificial synapses in a neuro-inspired system.The results of this Ph.D. work pave the way for the design of neuro-inspired systems that can learn by harnessing the rich plastic behaviors that are featured by innovative memory nanodevices
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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
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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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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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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
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Books on the topic "Neuro inspired":

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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.

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

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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.

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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.

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

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Book chapters on the topic "Neuro inspired":

1

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.

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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.

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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.

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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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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.

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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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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.

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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, 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.

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Mohanty, Ricky, Sandeep Singh Solanki, Pradeep Kumar Mallick, and Subhendu Kumar Pani. "A Classification Model Based on an Adaptive Neuro-fuzzy Inference System for Disease Prediction." In Bio-inspired Neurocomputing, 131–49. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5495-7_7.

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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.

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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.

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Conference papers on the topic "Neuro inspired":

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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"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.

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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, 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.

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Reports on the topic "Neuro inspired":

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

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