Relevant bibliographies by topics / Neuro inspiré

Contents

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

Academic literature on the topic 'Neuro inspiré'

Author: Grafiati
Published: 8 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Neuro inspiré"

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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 (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 implemente
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Arfan, Ghani, Dowrick Thomas, and J. McDaid Liam. "OSPEN: an open source platform for emulating neuromorphic hardware." International Journal of Reconfigurable and Embedded Systems (IJRES) 12, no. 1 (2023): 1–8. https://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 implemente
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Zhang, Wenqiang, Bin Gao, Jianshi Tang, et al. "Neuro-inspired computing chips." Nature Electronics 3, no. 7 (2020): 371–82. http://dx.doi.org/10.1038/s41928-020-0435-7.

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Birzhanova, Aigerim, Aliya Nurgaliyeva, Azhar Nurmagambetova, Hasan Dinçer, and Serhat Yüksel. "Neuro quantum-inspired decision-making for investor perception in green and conventional bond investments." Investment Management and Financial Innovations 21, no. 1 (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, liqui
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Harkhoe, Krishan, Guy Verschaffelt, and Guy Van der Sande. "Neuro-Inspired Computing with Spin-VCSELs." Applied Sciences 11, no. 9 (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 interva
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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 (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 (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 (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 b
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Hu, Jinming. "Bridging Neuroscience and AI: A Comprehensive Investigation of Brain-Inspired Computing Models." ITM Web of Conferences 73 (2025): 03001. https://doi.org/10.1051/itmconf/20257303001.

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Artificial Intelligence (AI) has reached new heights, supported by advancements in hardware and algorithm theory. Areas like robotics and autonomous driving have made significant strides, but brain-inspired computing remains a distinctive field. Although there were early hopes of AI closely connecting with brain science, this integration has been minimal. Neuroscience has mostly inspired some early algorithms, while most neural networks only adopted the idea of neuron connections without fully replicating real neural signals. However, brain-inspired algorithms, such as Spiking Neural Networks
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Dissertations / Theses on the topic "Neuro inspiré"

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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
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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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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
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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/mé
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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 neuroins
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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 relativ
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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
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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 e
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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
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Masominia, Amir Hossein. "Neuro-inspired computing with excitable microlasers." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASP053.

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Cette thèse présente des recherches sur des systèmes de calcul alternatifs, en se concentrant spécifiquement sur le calcul analogique et neuromimétique. La quête d'une intelligence artificielle plus générale a mis en évidence les limitations des unités de calcul conventionnelles basées sur les architectures de Von Neumann, en particulier en termes d'efficacité énergétique et de complexité. Les architectures de calcul inspirées du cerveau et les ordinateurs analogiques sont des prétendants de premier plan dans ce domaine. Parmi les différentes possibilités, les systèmes photoniques impulsionnel
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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 co
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Books on the topic "Neuro inspiré"

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Yu, Shimeng, ed. Neuro-inspired Computing Using Resistive Synaptic Devices. 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. Springer, 2008.

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Arena, Paolo, ed. Dynamical Systems, Wave-Based Computation and Neuro-Inspired Robots. 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. 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. Houghton Mifflin, 2003.

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

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Book chapters on the topic "Neuro inspiré"

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Strisciuglio, Nicola, and Nicolai Petkov. "Brain-Inspired Algorithms for Processing of Visual Data." In Lecture Notes in Computer Science. 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 organi
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Lewis, Frank L., and Kyriakos G. Vamvoudakis. "Neuro-Inspired Control." In Encyclopedia of Systems and Control. 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. 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. 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 COLl
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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. 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. 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. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45723-2_24.

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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. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02490-0_12.

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Amudha, J., and D. Radha. "Optimization of Rules in Neuro-Fuzzy Inference Systems." In Computational Vision and Bio Inspired Computing. 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, and Jae-sun Seo. "Peripheral Circuit Design Considerations of Neuro-inspired Architectures." In Neuro-inspired Computing Using Resistive Synaptic Devices. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54313-0_9.

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Conference papers on the topic "Neuro inspiré"

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Swaminathan, Balachandran, and Jack Sampson. "VIBE: Enhancing Unsupervised Continual Learning with Autonomous Novelty Detection." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065777.

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Baracat, Farah, Luca Manneschi, and Elisa Donati. "Heterogeneous Population Encoding for Multi-joint Regression using sEMG Signals." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065840.

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Schmidt, Hartmut, Andreas Grübl, José Montes, Eric Müller, Sebastian Schmitt, and Johannes Schemmel. "Demonstrating the Advantages of Analog Wafer-Scale Neuromorphic Hardware." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065060.

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Selagamsetty, Ranganath, Joshua San Miguel, and Mikko Lipasti. "The Spatial Effect of the Pinna for Neuromorphic Speech Denoising." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065299.

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Leto, Benedetto, Gianvito Urgese, Enrico Macii, and Vittorio Fra. "A LIF-based Legendre Memory Unit as neuromorphic State Space Model benchmarked on a second-long spatio-temporal task." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065250.

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Dietrich, Robin, Tobias Fischer, Nicolai Waniek, et al. "Threshold Adaptation in Spiking Networks Enables Shortest Path Finding and Place Disambiguation." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065806.

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Damberger, Graeme, Kathryn Simone, Chandan Datta, Ram Eshwar Kaundinya, Juan Escareno, and Chris Eliasmith. "Biologically-Inspired Representations for Adaptive Control with Spatial Semantic Pointers." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065084.

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Langer, Tim, Matthias Jobst, Chen Liu, Florian Kelber, Bernhard Vogginger, and Christian Mayr. "OctopuScheduler: On-Chip DNN Scheduling on the SpiNNaker2 Neuromorphic MPSoC." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065866.

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Patel, Karan, Ethan Maness, Tyler Nitzsche, et al. "Evolution at the Edge: Real-Time Evolution for Neuromorphic Engine Control." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065602.

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Spilger, Philipp, Eric Müller, and Johannes Schemmel. "Integrating programmable plasticity in experiment descriptions for analog neuromorphic hardware." In 2025 Neuro Inspired Computational Elements (NICE). IEEE, 2025. https://doi.org/10.1109/nice65350.2025.11065886.

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Reports on the topic "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), 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), 2015. http://dx.doi.org/10.2172/1470994.

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