Relevant bibliographies by topics / Neuro inspired

Contents

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

Academic literature on the topic 'Neuro inspired'

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

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Journal articles on the topic "Neuro inspired"

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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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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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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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Blachowicz, Tomasz, Jacek Grzybowski, Pawel Steblinski, and Andrea Ehrmann. "Neuro-Inspired Signal Processing in Ferromagnetic Nanofibers." Biomimetics 6, no. 2 (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 coup
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Yu, Shimeng. "Neuro-Inspired Computing With Emerging Nonvolatile Memorys." Proceedings of the IEEE 106, no. 2 (2018): 260–85. http://dx.doi.org/10.1109/jproc.2018.2790840.

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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
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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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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
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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 le
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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
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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<br>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 p
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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 élect
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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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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 inspired"

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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, 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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Yu, Shimeng. Neuro-Inspired Computing Using Resistive Synaptic Devices. Springer, 2017.

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Book chapters on the topic "Neuro inspired"

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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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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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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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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. 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. 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. 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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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 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), 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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