Relevant bibliographies by topics / Plasticità Hebbiana

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

Academic literature on the topic 'Plasticità Hebbiana'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Plasticità Hebbiana"

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Yee, Ada X., Yu-Tien Hsu, and Lu Chen. "A metaplasticity view of the interaction between homeostatic and Hebbian plasticity." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160155. http://dx.doi.org/10.1098/rstb.2016.0155.

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Hebbian and homeostatic plasticity are two major forms of plasticity in the nervous system: Hebbian plasticity provides a synaptic basis for associative learning, whereas homeostatic plasticity serves to stabilize network activity. While achieving seemingly very different goals, these two types of plasticity interact functionally through overlapping elements in their respective mechanisms. Here, we review studies conducted in the mammalian central nervous system, summarize known circuit and molecular mechanisms of homeostatic plasticity, and compare these mechanisms with those that mediate Heb
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Hsu, Yu-Tien, Jie Li, Dick Wu, Thomas C. Südhof, and Lu Chen. "Synaptic retinoic acid receptor signaling mediates mTOR-dependent metaplasticity that controls hippocampal learning." Proceedings of the National Academy of Sciences 116, no. 14 (2019): 7113–22. http://dx.doi.org/10.1073/pnas.1820690116.

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Homeostatic synaptic plasticity is a stabilizing mechanism engaged by neural circuits in response to prolonged perturbation of network activity. The non-Hebbian nature of homeostatic synaptic plasticity is thought to contribute to network stability by preventing “runaway” Hebbian plasticity at individual synapses. However, whether blocking homeostatic synaptic plasticity indeed induces runaway Hebbian plasticity in an intact neural circuit has not been explored. Furthermore, how compromised homeostatic synaptic plasticity impacts animal learning remains unclear. Here, we show in mice that the
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Prosper, Antoine, Thomas Blanchard, and Claudia Lunghi. "The interplay between Hebbian and homeostatic plasticity in the adult visual cortex." Journal of Physiology 603, no. 6 (2025): 1521–40. https://doi.org/10.1113/jp287665.

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AbstractHomeostatic and Hebbian plasticity co‐operate during the critical period, refining neuronal circuits; however, the interaction between these two forms of plasticity is still unclear, especially in adulthood. Here, we directly investigate this issue in adult humans using two consolidated paradigms to elicit each form of plasticity in the visual cortex: the long‐term potentiation‐like change of the visual evoked potential (VEP) induced by high‐frequency stimulation (HFS) and the shift of ocular dominance induced by short‐term monocular deprivation (MD). We tested homeostatic and Hebbian
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Fox, Kevin, and Michael Stryker. "Integrating Hebbian and homeostatic plasticity: introduction." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160413. http://dx.doi.org/10.1098/rstb.2016.0413.

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Hebbian plasticity is widely considered to be the mechanism by which information can be coded and retained in neurons in the brain. Homeostatic plasticity moves the neuron back towards its original state following a perturbation, including perturbations produced by Hebbian plasticity. How then does homeostatic plasticity avoid erasing the Hebbian coded information? To understand how plasticity works in the brain, and therefore to understand learning, memory, sensory adaptation, development and recovery from injury, requires development of a theory of plasticity that integrates both forms of pl
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Turrigiano, Gina G. "The dialectic of Hebb and homeostasis." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160258. http://dx.doi.org/10.1098/rstb.2016.0258.

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It has become widely accepted that homeostatic and Hebbian plasticity mechanisms work hand in glove to refine neural circuit function. Nonetheless, our understanding of how these fundamentally distinct forms of plasticity compliment (and under some circumstances interfere with) each other remains rudimentary. Here, I describe some of the recent progress of the field, as well as some of the deep puzzles that remain. These include unravelling the spatial and temporal scales of different homeostatic and Hebbian mechanisms, determining which aspects of network function are under homeostatic contro
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Costa, Rui Ponte, Beatriz E. P. Mizusaki, P. Jesper Sjöström, and Mark C. W. van Rossum. "Functional consequences of pre- and postsynaptic expression of synaptic plasticity." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160153. http://dx.doi.org/10.1098/rstb.2016.0153.

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Growing experimental evidence shows that both homeostatic and Hebbian synaptic plasticity can be expressed presynaptically as well as postsynaptically. In this review, we start by discussing this evidence and methods used to determine expression loci. Next, we discuss the functional consequences of this diversity in pre- and postsynaptic expression of both homeostatic and Hebbian synaptic plasticity. In particular, we explore the functional consequences of a biologically tuned model of pre- and postsynaptically expressed spike-timing-dependent plasticity complemented with postsynaptic homeosta
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Zenke, Friedemann, and Wulfram Gerstner. "Hebbian plasticity requires compensatory processes on multiple timescales." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160259. http://dx.doi.org/10.1098/rstb.2016.0259.

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We review a body of theoretical and experimental research on Hebbian and homeostatic plasticity, starting from a puzzling observation: while homeostasis of synapses found in experiments is a slow compensatory process, most mathematical models of synaptic plasticity use rapid compensatory processes (RCPs). Even worse, with the slow homeostatic plasticity reported in experiments, simulations of existing plasticity models cannot maintain network stability unless further control mechanisms are implemented. To solve this paradox, we suggest that in addition to slow forms of homeostatic plasticity t
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Magee, Jeffrey C., and Christine Grienberger. "Synaptic Plasticity Forms and Functions." Annual Review of Neuroscience 43, no. 1 (2020): 95–117. http://dx.doi.org/10.1146/annurev-neuro-090919-022842.

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Synaptic plasticity, the activity-dependent change in neuronal connection strength, has long been considered an important component of learning and memory. Computational and engineering work corroborate the power of learning through the directed adjustment of connection weights. Here we review the fundamental elements of four broadly categorized forms of synaptic plasticity and discuss their functional capabilities and limitations. Although standard, correlation-based, Hebbian synaptic plasticity has been the primary focus of neuroscientists for decades, it is inherently limited. Three-factor
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Card, H. C., C. R. Schneider, and W. R. Moore. "Hebbian plasticity in mos synapses." IEE Proceedings F Radar and Signal Processing 138, no. 1 (1991): 13. http://dx.doi.org/10.1049/ip-f-2.1991.0003.

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Miller, Kenneth D. "Derivation of Linear Hebbian Equations from a Nonlinear Hebbian Model of Synaptic Plasticity." Neural Computation 2, no. 3 (1990): 321–33. http://dx.doi.org/10.1162/neco.1990.2.3.321.

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A linear Hebbian equation for synaptic plasticity is derived from a more complex, nonlinear model by considering the initial development of the difference between two equivalent excitatory projections. This provides a justification for the use of such a simple equation to model activity-dependent neural development and plasticity, and allows analysis of the biological origins of the terms in the equation. Connections to previously published models are discussed.
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Dissertations / Theses on the topic "Plasticità Hebbiana"

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GUIDALI, GIACOMO. "Cross-modal plasticity in sensory-motor cortices and non-invasive brain stimulation techniques: new ways to explore and modulate brain plasticity." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2021. http://hdl.handle.net/10281/306484.

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Nella presente tesi di dottorato, ho esplorato se fenomeni di apprendimento Hebbiano possano governare il funzionamento dei sistemi cross-modali e sensorimotori del cervello umano. A tal fine, durante il mio dottorato, ho sviluppato e testato due nuovi protocolli Paired Associative Stimulation (PAS), una classe di tecniche di stimolazione cerebrale non invasiva in cui una stimolazione sensoriale periferica viene ripetutamente accoppiata con un impulso di stimolazione magnetica transcranica (TMS) su un’area bersaglio al fine di indurre plasticità associativa Hebbiana. I due protocolli PAS prese
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Prosper, Antoine. "Hebbian and Homeostatic plasticity in the adult visual cortex." Electronic Thesis or Diss., Université Paris sciences et lettres, 2025. http://www.theses.fr/2025UPSLE005.

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Cette thèse explore comment la plasticité hebbienne et homéostatique coexistent dans le système visuel adulte. Bien que ces deux mécanismes aient été étudiés dans des modèles animaux et durant le développement, leur interaction chez l’adulte humain n’a, à notre connaissance, pas été examinée systématiquement. Nous testons ici directement, pour la première fois, l’interaction de ces deux formes de plasticité chez l’adulte. À cette fin, nous présentons trois expériences combinant diverses techniques expérimentales : psychophysique, électroencéphalographie (EEG) chez l’humain et imagerie fonction
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Soares, Cary. "Mechanisms of Synaptic Homeostasis and their Influence on Hebbian Plasticity at CA1 Hippocampal Synapses." Thesis, Université d'Ottawa / University of Ottawa, 2016. http://hdl.handle.net/10393/35508.

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Information is transferred between neurons in the brain via electrochemical transmission at specialized cell-cell junctions called synapses. These structures are far from being static, but rather are influenced by plasticity mechanisms that alter features of synaptic transmission as means to build routes of information flow in the brain. Hebbian forms of synaptic plasticity – long-term potentiation and long-term depression – have been well studied and are considered to be the cellular basis of learning and memory, although their positive feedback nature is prone to instability. Neurons are als
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Bartsch, Armin P. "Orientation maps in primary visual cortex a Hebbian model of intracortical and geniculocortical plasticity /." [S.l. : s.n.], 2000. http://deposit.ddb.de/cgi-bin/dokserv?idn=962125733.

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Ljaschenko, Dmitrij [Verfasser], Mafred [Gutachter] Heckmann, and Erich [Gutachter] Buchner. "Hebbian plasticity at neuromuscular synapses of Drosophila / Dmitrij Ljaschenko. Gutachter: Mafred Heckmann ; Erich Buchner." Würzburg : Universität Würzburg, 2014. http://d-nb.info/1108780482/34.

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Gasselin, Célia. "Plasticités hebbienne et homéostatique de l'excitabilité intrinsèque des neurones de la région CA1 de l'hippocampe=hebbian and homeostatic plasticity of intrinsic excitability in hippocampal CA1 neurons." Thesis, Aix-Marseille, 2013. http://www.theses.fr/2013AIXM5047.

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Pendant des décennies, la plasticité synaptique a été considérée comme le substrat principal de la plasticité fonctionnelle cérébrale. Récemment, plusieurs études expérimentales indiquent que des régulations à long terme de l’excitabilité intrinsèque participent à la plasticité dépendante de l’activité. En effet, la modulation des canaux ioniques dépendants du potentiel, lesquels régulent fortement l’excitabilité intrinsèque et l’intégration des entrées synaptiques, a été démontrée essentielle dans les processus d’apprentissage. Cependant, la régulation, dépendante de l’activité, du courant io
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Bouchacourt, Flora. "Hebbian mechanisms and temporal contiguity for unsupervised task-set learning." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066379/document.

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L'homme est capable d'utiliser des stratégies ou règles concurrentes selon les contraintes environnementales. Nous étudions un modèle plausible pour une tâche nécessitant l'apprentissage de plusieurs règles associant des stimuli visuels à des réponses motrices. Deux réseaux de populations neurales à sélectivité mixte interagissent. Le réseau décisionnel apprend les associations stimulus-réponse une à une, mais ne peut gérer qu'une règle à la fois. Son activité modifie la plasticité synaptique du second réseau qui apprend les statistiques d'évènements sur une échelle de temps plus longue. Lorsq
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Albers, Christian [Verfasser], Klaus [Akademischer Betreuer] Pawelzik, and Stefan [Akademischer Betreuer] Bornholdt. "Functional Implications of Synaptic Spike Timing Dependent Plasticity and Anti-Hebbian Membrane Potential Dependent Plasticity / Christian Albers. Gutachter: Klaus Pawelzik ; Stefan Bornholdt. Betreuer: Klaus Pawelzik." Bremen : Staats- und Universitätsbibliothek Bremen, 2015. http://d-nb.info/107560947X/34.

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Tully, Philip. "Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205568.

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Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and cons
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Fiorentino, Domenico. "Interazione visuo-acustica e fenomeni di plasticità sinaptica: studio mediante un modello di rete neurale applicato al ventriloquismo." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amslaurea.unibo.it/4863/.

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Book chapters on the topic "Plasticità Hebbiana"

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Hayashi, Yasunori, Ken-ichi Okamoto, Miquel Bosch, and Kensuke Futai. "Roles of Neuronal Activity-Induced Gene Products in Hebbian and Homeostatic Synaptic Plasticity, Tagging, and Capture." In Synaptic Plasticity. Springer Vienna, 2012. http://dx.doi.org/10.1007/978-3-7091-0932-8_15.

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Brown, Thomas H., and Sumantra Chattarji. "Hebbian Synaptic Plasticity: Evolution of the Contemporary Concept." In Models of Neural Networks. Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4612-4320-5_8.

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van der Lee, Tim, Georgios Exarchakos, and Sonia Heemstra de Groot. "In-network Hebbian Plasticity for Wireless Sensor Networks." In Internet and Distributed Computing Systems. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34914-1_8.

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Brown, T. H., Y. Zhao, and V. Leung. "Hebbian Plasticity." In Encyclopedia of Neuroscience. Elsevier, 2009. http://dx.doi.org/10.1016/b978-008045046-9.00796-8.

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"Hebbian Synaptic Plasticity." In Encyclopedia of the Sciences of Learning. Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_2204.

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McClelland, James L. "How far can you go with Hebbian learning, and when does it lead you astray?" In Processes of Change in Brain and Cognitive Development. Oxford University PressOxford, 2006. http://dx.doi.org/10.1093/oso/9780198568742.003.0002.

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Abstract This paper considers the use of Hebbian learning rules to model aspects of development and learning, including the emergence of structure in the visual system in early life. There is considerable physiological evidence that a Hebb-like learning rule applies to the strengthening of synaptic efficacy seen in neurophysiological investigations of synaptic plasticity, and similar learning rules are often used to show how various properties of visual neurons and their organization into ocular dominance stripes and orientation columns could arise without being otherwise preprogrammed. Some o
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Trappenberg, Thomas P. "Associators and synaptic plasticity." In Fundamentals of Computational Neuroscience, 3rd ed. Oxford University PressOxford, 2022. http://dx.doi.org/10.1093/oso/9780192869364.003.0006.

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Abstract This chapter is an important introduction and discussion of synaptic plasticity and learning in networks. Neurons are connected to form networks, and a neural network is not only characterized by the topology of the network, but also by the connection strength between two neurons or two population nodes. This chapter explains how a connection strength can be changed in a usage-dependent way through a biological phenomenon called synaptic plasticity. Synaptic plasticity is the physical basis of learning in neural systems. This is illustrated with a general discussion of associators, wh
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Yuste, Rafael. "The Cortical Microcircuit as a Recurrent Neural Network." In Handbook of Brain Microcircuits, edited by Gordon M. Shepherd and Sten Grillner. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190636111.003.0004.

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The mammalian neocortex has distributed excitatory and inhibitory connectivity that, together with the integrative properties of pyramidal cells and their strong synaptic plasticity, make it ideally suited to implement a neural network design. This chapter summarizes results from the author’s research, consistent with the hypothesis that the neocortical microcircuit is a recurrent neural network that builds dynamical attractors. According to this paradigm, the units of function of the cortex would be groups of neurons forming ensembles or assemblies through Hebbian synaptic plasticity. The can
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"Associators and Synaptic Plasticity." In Fundamentals of Computational Neuroscience, edited by Thomas P. Trappenberg. Oxford University PressOxford, 2009. http://dx.doi.org/10.1093/oso/9780199568413.003.0004.

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Abstract So far, we have neglected some of the most exciting and central mechanisms of brain processing, those of synaptic plasticity and learning in networks. Neurons are connected to form networks, and a neural network is not only characterized by the topology of the network, but also by the connection strength, wij, between two neurons or two population nodes. In this chapter we discuss how connection strengths can be changed in a usage-dependent way through a biological phenomena called synaptic plasticity. Synaptic plasticity is the physical basis of learning in neural systems which we wi
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Song, Sen. "Hebbian Learning and Spike-Timing-Dependent Plasticity." In Computational Neuroscience. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203494462.ch11.

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Conference papers on the topic "Plasticità Hebbiana"

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Stricker, Patrick, Florian Röhrbein, and Andreas Knoblauch. "Weight Perturbation and Competitive Hebbian Plasticity for Training Sparse Excitatory Neural Networks." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650478.

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Thangarasa, Vithursan, Thomas Miconi, and Graham W. Taylor. "Enabling Continual Learning with Differentiable Hebbian Plasticity." In 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020. http://dx.doi.org/10.1109/ijcnn48605.2020.9206764.

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Magotra, Arjun, and Juntae kim. "Transfer Learning for Image Classification Using Hebbian Plasticity Principles." In CSAI2019: 2019 3rd International Conference on Computer Science and Artificial Intelligence. ACM, 2019. http://dx.doi.org/10.1145/3374587.3375880.

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Ravichandran, Naresh Balaji, Anders Lansner, and Pawel Herman. "Spiking neural networks with Hebbian plasticity for unsupervised representation learning." In ESANN 2023 - European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Ciaco - i6doc.com, 2023. http://dx.doi.org/10.14428/esann/2023.es2023-169.

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Antonietti, Alberto, Vasco Orza, Claudia Casellato, Egidio D'Angelo, and Alessandra Pedrocchi. "Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity." In 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2019. http://dx.doi.org/10.1109/embc.2019.8856489.

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Scott, J. Campbell, Thomas F. Hayes, Ahmet S. Ozcan, and Winfried W. Wilcke. "Synaptic plasticity in an artificial Hebbian network exhibiting continuous, unsupervised, rapid learning." In the 7th Annual Neuro-inspired Computational Elements Workshop. ACM Press, 2019. http://dx.doi.org/10.1145/3320288.3320292.

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Dasgupta, Sakyasingha, Florentin Worgotter, Jun Morimoto, and Poramate Manoonpong. "Neural Combinatorial Learning of Goal-Directed Behavior with Reservoir Critic and Reward Modulated Hebbian Plasticity." In 2013 IEEE International Conference on Systems, Man and Cybernetics (SMC 2013). IEEE, 2013. http://dx.doi.org/10.1109/smc.2013.174.

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Fernando, Subha, and Koichi Yamada. "Spike-timing dependent plasticity with release probability supported to eliminate weight boundaries and to balance the excitation of Hebbian neurons." In 2012 Joint 6th Intl. Conference on Soft Computing and Intelligent Systems (SCIS) and 13th Intl. Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2012. http://dx.doi.org/10.1109/scis-isis.2012.6505006.

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Enikov, Eniko T., Juan-Antonio Escareno, and Micky Rakotondrabe. "Image Schema Based Landing and Navigation for Rotorcraft MAV-s." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-51450.

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To date, most autonomous micro air vehicles (MAV-s) operate in a controlled environment, where the location of and attitude of the aircraft are measured with an infrared (IR) tracking systems. If MAV-s are to ever exit the lab, their flight control needs to become autonomous and based on on-board image and attitude sensors. To address this need, several groups are developing monocular and binocular image based navigation systems. One of the challenges of these systems is the need for exact calibration in order to determine the vehicle’s position and attitude through the solution of an inverse
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Reports on the topic "Plasticità Hebbiana"

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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