Relevant bibliographies by topics / Plasticità Hebbiana

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

Academic literature on the topic 'Plasticità Hebbiana'

Author: Grafiati
Published: 9 March 2023
Last updated: 10 March 2023

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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 (March 5, 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 Hebbian plasticity. We end with a discussion of ‘local’ homeostatic plasticity and the potential role of local homeostatic plasticity as a form of metaplasticity that modulates a neuron's future capacity for Hebbian plasticity. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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 (February 19, 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 experience of an enriched environment (EE) engaged homeostatic synaptic plasticity in hippocampal circuits, thereby reducing excitatory synaptic transmission. This process required RARα, a nuclear retinoic acid receptor that doubles as a cytoplasmic retinoic acid-induced postsynaptic regulator of protein synthesis. Blocking RARα-dependent homeostatic synaptic plasticity during an EE experience by ablating RARα signaling induced runaway Hebbian plasticity, as evidenced by greatly enhanced long-term potentiation (LTP). As a consequence, RARα deletion in hippocampal circuits during an EE experience resulted in enhanced spatial learning but suppressed learning flexibility. In the absence of RARα, moreover, EE experience superactivated mammalian target of rapamycin (mTOR) signaling, causing a shift in protein translation that enhanced the expression levels of AMPA-type glutamate receptors. Treatment of mice with the mTOR inhibitor rapamycin during an EE experience not only restored normal AMPA-receptor expression levels but also reversed the increases in runaway Hebbian plasticity and learning after hippocampal RARα deletion. Thus, our findings reveal an RARα- and mTOR-dependent mechanism by which homeostatic plasticity controls Hebbian plasticity and learning.
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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 (March 5, 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 plasticity into a whole. In April 2016, a group of computational and experimental neuroscientists met in London at a discussion meeting hosted by the Royal Society to identify the critical questions in the field and to frame the research agenda for the next steps. Here, we provide a brief introduction to the papers arising from the meeting and highlight some of the themes to have emerged from the discussions. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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 (March 5, 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 control, and understanding when and how homeostatic and Hebbian mechanisms must be segregated within neural circuits to prevent interference. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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 (March 5, 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 homeostatic control. The pre- and postsynaptic expression in this model predicts (i) more reliable receptive fields and sensory perception, (ii) rapid recovery of forgotten information (memory savings), and (iii) reduced response latencies, compared with a model with postsynaptic expression only. Finally, we discuss open questions that will require a considerable research effort to better elucidate how the specific locus of expression of homeostatic and Hebbian plasticity alters synaptic and network computations. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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 (March 5, 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 there are RCPs which stabilize synaptic plasticity on short timescales. These rapid processes may include heterosynaptic depression triggered by episodes of high postsynaptic firing rate. While slower forms of homeostatic plasticity are not sufficient to stabilize Hebbian plasticity, they are important for fine-tuning neural circuits. Taken together we suggest that learning and memory rely on an intricate interplay of diverse plasticity mechanisms on different timescales which jointly ensure stability and plasticity of neural circuits. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.
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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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Magee, Jeffrey C., and Christine Grienberger. "Synaptic Plasticity Forms and Functions." Annual Review of Neuroscience 43, no. 1 (July 8, 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 plasticity rules supplement Hebbian forms with neuromodulation and eligibility traces, while true supervised types go even further by adding objectives and instructive signals. Finally, a recently discovered hippocampal form of synaptic plasticity combines the above elements, while leaving behind the primary Hebbian requirement. We suggest that the effort to determine the neural basis of adaptive behavior could benefit from renewed experimental and theoretical investigation of more powerful directed types of synaptic plasticity.
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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 (September 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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Guzman-Karlsson, Mikael C., Jarrod P. Meadows, Cristin F. Gavin, John J. Hablitz, and J. David Sweatt. "Transcriptional and epigenetic regulation of Hebbian and non-Hebbian plasticity." Neuropharmacology 80 (May 2014): 3–17. http://dx.doi.org/10.1016/j.neuropharm.2014.01.001.

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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 presentati nella mia tesi mirano a due sistemi cerebrali sensoriali-motori con funzionamento a specchio (tactile mirror system e action observation network), sfruttando rispettivamente una via cross-corticale visuo-tattile (cross-modal PAS) e una visuo-motoria (mirror PAS). Nel primo capitolo del presente lavoro, dopo una breve introduzione al concetto di plasticità associativa Hebbiana, fornirò una revisione esaustiva dei protocolli PAS che mirano ai sistemi sensorimotori, proponendo una classificazione in tre macro-categorie (within-system, cross-systems e cortico-cortical), a seconda delle caratteristiche delle stimolazioni accoppiate. Nel secondo capitolo descriverò le principali proprietà del sistema dei neuroni specchio (MNS) considerando anche le sue proprietà cross-modali visuo-tattili ed i meccanismi di plasticità neuronale che sono stati ipotizzati alla base dello sviluppo dei neuroni specchio. Nel terzo capitolo, introdurrò il cross-modal PAS (cm-PAS), un nuovo cross-systems PAS sviluppato per sfruttare le proprietà visuo-tattili della corteccia somatosensoriale primaria, al fine di indurre plasticità associativa Hebbiana in tale regione sensoriale. In una serie di tre esperimenti, ho testo la dipendenza temporale (Esperimento 1), la specificità corticale (Esperimento 2) e visiva (Esperimento 3) del protocollo, misurando possibili cambiamenti nell'acuità tattile dei partecipanti. Nell'esperimento 3, ho valutato anche possibili cambiamenti neurofisiologici all'interno di S1, registrando i potenziali evocati somatosensoriali. Infine, in un quarto esperimento, la dipendenza temporale del cm-PAS è stata ulteriormente studiata, testando l'ipotesi che meccanismi anticipatori di tipo predittivo possano svolgere un ruolo centrale nell'efficacia del protocollo. Nel quarto capitolo introdurrò un secondo cross-systems PAS: il mirror PAS (m-PAS) che sfrutta le proprietà ‘mirror’ visuo-motorie del cervello umano. A differenza del cm-PAS, questo secondo protocollo sfrutta la natura associativa dell'integrazione visuo-motoria all'interno del MNS, mirando a indurre un nuovo, atipico, fenomeno di risonanza motoria attraverso apprendimento Hebbiano. In tre esperimenti ho testato la dipendenza temporale (Esperimento 1), la specificità visiva (Esperimento 2) e corticale (Esperimento 3) del protocollo registrando i potenziali evocati motori durante la visione di semplici movimenti (i.e., risonanza motoria). Inoltre, nel terzo esperimento, ho esplorato anche possibili effetti comportamentali dell’m-PAS, utilizzando un compito di compatibilità imitativa che sfrutta il fenomeno dell'imitazione automatica. Infine, nel capitolo conclusivo, discuterò i risultati teorici, metodologici e clinici e le prospettive future che derivano da questi due protocolli.
In the present doctoral thesis, I have explored whether Hebbian learning may rule the functioning of cross-modal and sensory-motor networks of the human brain. To this aim, during my doctorate, I have developed and tested two novel Paired Associative Stimulation (PAS) protocols, a class of non-invasive brain stimulation techniques in which a peripheral, sensory, stimulation is repeatedly paired with a Transcranial Magnetic Stimulation (TMS) pulse to induce Hebbian associative plasticity. The two PAS protocols presented in my thesis target sensory-motor networks with mirror functioning, exploiting a visuo-tactile (cross-modal PAS), and a visuo-motor pathway (mirror PAS), respectively. In the first chapter of the present work, after a brief introduction to the concept of Hebbian associative plasticity, I will provide an exhaustive review of PAS protocols targeting sensory-motor systems, proposing a classification in three macro-categories: within-system, cross-systems, and cortico-cortical protocols, according to the characteristics of the paired stimulations. In the second chapter, I will describe the principal properties of the Mirror Neuron System (MNS) also considering its cross-modal (i.e., visuo-tactile) characteristics and the plastic mechanisms that are been hypothesize at the ground of the development of mirror neurons’ matching properties. In the third chapter, I will introduce the cross-modal PAS (cm-PAS), a novel cross-systems PAS developed to exploit the visuo-tactile mirroring properties of the primary somatosensory cortex (S1) to induce Hebbian associative plasticity in such primary sensory region. In a series of three experiments, timing dependency (Experiment 1), cortical (Experiment 2), and visual specificity (Experiment 3) of the protocol have been tested, by measuring changes in participants’ tactile acuity. In Experiment 3, also possible neurophysiological changes within S1 has been assessed, recording somatosensory-evoked potentials (SEP). Then, in a fourth experiment, cm-PAS timing dependency has been further investigated, testing the hypothesis that anticipatory, predictive-like, mechanisms within S1 may play a central role in the effectiveness of the protocol. In the fourth chapter, a second cross-systems PAS will be introduced: the mirror PAS (m-PAS) which exploits visuo-motor mirroring properties of the human brain. Differently from the cm-PAS, this second protocol targets visuo-motor integration within the MNS and aims at induce a novel, atypical, motor resonance phenomena (assessed recording motor-evoked potentials – MEPs) following Hebbian learning. In three experiments, timing dependency (Experiment 1), visual (Experiment 2), and cortical specificity (Experiment 3) of the protocol have been tested. Furthermore, in the third experiment, the behavioral effects of the m-PAS are explored, using an imitative compatibility task exploiting automatic imitation phenomenon. Finally, in the conclusive chapter, I will discuss theoretical, methodological, and clinical outcomes and future perspectives that arise from these two protocols and the related results.
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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 also endowed with homeostatic mechanisms of synaptic plasticity that act to stabilize neural network functions by globally tuning synaptic drive. Precisely how neurons orchestrate this adaptive homeostatic response and how it influences Hebbian forms of synaptic plasticity, however, remains only partially understood. Using a combination of whole-cell electrophysiology, two-photon imaging and glutamate uncaging in organotypic hippocampal slices, I have expanded upon the known repertoire of homeostatic mechanisms that increase excitatory synaptic drive when CA1 hippocampal neurons experience a prolonged period of diminished activity. I found that the subunit composition of AMPA and NMDA receptors, the two major glutamate receptor subtypes at excitatory synapses, are altered which, in addition to increasing synaptic strength, are predicted to change the signaling and integrative properties of synaptic transmission. Moreover, I found that the amount of glutamate released from presynaptic terminals during evoked-transmission is enhanced and that this mechanism might, in part, underlie the uniform cell-wide homeostatic increase in synaptic strengths. Lastly, I found that homeostatic strengthening of synaptic transmission reduced the potential for CA1 synapses to exhibit long-term potentiation, and that this was caused by altered presynaptic release dynamics that impeded plasticity induction. Together, this work highlights several mechanistic strategies employed by neurons to increase excitatory synaptic drive during periods of activity deprivation which, in addition to balancing cellular excitability, alters the metaplastic state of synapses.
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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 ionique activé par l’hyperpolarisation (Ih) et ses conséquences sur l’induction de futures plasticités reste à éclaircir, tout comme la présence d’une régulation de conductances dépendantes du potentiel dans les neurones inhibiteurs. Dans la première partie de ma thèse, nous caractérisons les mécanismes d’induction et d’expression de la plasticité à long terme de l’excitabilité (LTP-IE) dans les interneurons en panier de la région CA1 exprimant la parvalbumine. Dans une seconde partie, le rôle de Ih dans la régulation homéostatique de l’excitabilité neuronale induite par des manipulations de l’activité neuronale dans sa globalité a été étudié. Dans la troisième étude, nous montrons que la magnitude de la Dépression à Long Terme (LTD) détermine le sens de la régulation de Ih dans les neurones pyramidaux de CA1. En conclusion, cette thèse montre qu’à la fois dans les neurones excitateurs et inhibiteurs, les régulations des conductances dépendantes du potentiel aident à maintenir une relative stabilité dans l’activité du réseau
Synaptic plasticity has been considered for decades as the main substrate of functional plasticity in the brain. Recently, experimental evidences suggest that long-lasting regulation of intrinsic neuronal excitability may also account for activity-dependent plasticity. Indeed, voltage-dependent ionic channels strongly regulate intrinsic excitability and inputs integration and their regulation was found to be essential in learning process. However, activity-dependent regulation of the hyperpolarization-activated ionic current (Ih) and its consequences for future plasticity remain unclear, so as the presence of any voltage-dependent conductances regulation in inhibitory neurons. In the first part of this thesis, we report the characterization of the induction and expression mechanisms of Long-Term Potentiation of Intrinsic Excitability (LTP-IE) in CA1 parvalbumin-positive basket interneurons. In a second part, the role of Ih in the homeostatic regulation of intrinsic neuronal excitability induced by global manipulations of neuronal activity was reported. In the third experimental study, we showed that the magnitude of Long-term Depression (LTD) determines the sign of Ih regulation in CA1 pyramidal neurons. In conclusion, this thesis shows that in both excitatory and inhibitory neurons, activity-dependent regulations of voltage-dependent conductances help to maintain a relative stability in the network activity
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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. Lorsque des motifs entre les associations stimulus-réponse sont détectés, un biais d'inférence vers le réseau décisionnel guide le comportement futur. Nous montrons que le mécanisme de Hebb non-supervisé dans le second réseau est suffisant pour l'implémentation des règles. Leur récupération dans le réseau de décision améliore la performance. Le modèle prédit des changements comportementaux en fonction de la séquence des réponses précédentes, dont les effets sur la performance peuvent être positifs ou négatifs. Les prédictions sont confirmées par les données, et permettent d'identifier les sujets ayant appris la structure de la tâche. Le signal d'inférence corrèle avec l'activité BOLD dans le réseau fronto-pariétal. Au sein de ce réseau, les n¿uds préfrontaux dorsomédial et dorsolatéral sont préférentiellement recrutés lorsque les règles sont récurrentes: l'activité dans ces régions pourrait biaiser les circuits de décision lorsqu'une règle est récupérée. Ces résultats montrent que le mécanisme de Hebb peut expliquer l'apprentissage de comportements complexes en contrôle cognitif
Depending on environmental demands, humans performing in a given task are able to exploit multiple concurrent strategies, for which the mental representations are called task-sets. We examine a candidate model for a specific human experiment, where several stimulus-response mappings, or task-sets, need to be learned and monitored. The model is composed of two interacting networks of mixed-selective neural populations. The decision network learns stimulus-response associations, but cannot learn more than one task-set. Its activity drives synaptic plasticity in a second network that learns event statistics on a longer timescale. When patterns in stimulus-response associations are detected, an inference bias to the decision network guides successive behavior. We show that a simple unsupervised Hebbian mechanism in the second network is sufficient to learn an implementation of task-sets. Their retrieval in the decision network improves performance. The model predicts abrupt changes in behavior depending on the precise statistics of previous responses, corresponding to positive (task-set retrieval) or negative effects on performance. The predictions are borne out by the data, and enable to identify subjects who have learned the task structure. The inference signal correlates with BOLD activity in the fronto-parietal network. Within this network, dorsomedial and dorsolateral prefrontal nodes are preferentially recruited when task-sets are recurrent: activity in these regions may provide a bias to decision circuits when a task-set is retrieved. These results show that Hebbian mechanisms and temporal contiguity may parsimoniously explain the learning of rule-guided behavior
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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 constant neural reorganization? Progress towards answering many of these questions can be pursued through large-scale neuronal simulations.    In this thesis, a Hebbian learning rule for spiking neurons inspired by statistical inference is introduced. The spike-based version of the Bayesian Confidence Propagation Neural Network (BCPNN) learning rule involves changes in both synaptic strengths and intrinsic neuronal currents. The model is motivated by molecular cascades whose functional outcomes are mapped onto biological mechanisms such as Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability. Temporally interacting memory traces enable spike-timing dependence, a stable learning regime that remains competitive, postsynaptic activity regulation, spike-based reinforcement learning and intrinsic graded persistent firing levels.    The thesis seeks to demonstrate how multiple interacting plasticity mechanisms can coordinate reinforcement, auto- and hetero-associative learning within large-scale, spiking, plastic neuronal networks. Spiking neural networks can represent information in the form of probability distributions, and a biophysical realization of Bayesian computation can help reconcile disparate experimental observations.

QC 20170421

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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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Cappelli, Simona. "Modello di rete neurale per lo studio di fenomeni di integrazione visuoacustica in soggetti sani e patologici." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/3275/.

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L’integrazione multisensoriale è la capacità del sistema nervoso di utilizzare molteplici sorgenti sensoriali. Una tra le più studiate forme di integrazione è quella tra informazioni visive ed acustiche. La capacità di localizzare uno stimolo acustico nello spazio è un processo meno accurato ed affidabile della localizzazione visiva, di conseguenza, un segnale visivo è spesso in grado di “catturare” (ventriloquismo) o di incrementare (enhancement multisensoriale) la performance di localizzazione acustica. Numerose evidenze sperimentali hanno contribuito ad individuare i processi neurali e le aree cerebrali alla base dei fenomeni integrativi; in particolare, un importante contributo viene dallo studio su soggetti con lesioni cerebrali. Tuttavia molti aspetti sui possibili meccanismi coinvolti restano ancora da chiarire. Obiettivo di questa tesi è stato lo sviluppo di un modello matematico di rete neurale per fare luce sui meccanismi alla base dell’interazione visuo-acustica e dei suoi fenomeni di plasticità. In particolare, il modello sviluppato è in grado di riprodurre condizioni che si verificano in-vivo, replicando i fenomeni di ventriloquismo ed enhancement in diversi stati fisiopatologici e interpretandoli in termini di risposte neurali e reciproche interazione tra i neuroni. Oltre ad essere utile a migliorare la comprensione dei meccanismi e dei circuiti neurali coinvolti nell’integrazione multisensoriale, il modello può anche essere utile per simulare scenari nuovi, con la possibilità di effettuare predizioni da testare in successivi esperimenti.
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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, 335–54. Vienna: 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, 287–314. New York, NY: 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, 79–88. Cham: 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, 1049–56. 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, 1419. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_2204.

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Trappenberg, Thomas P. "Associators and synaptic plasticity." In Fundamentals of Computational Neuroscience, 133–66. 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, which summarize the essence of plasticity mechanisms and their importance for cognitive brain processing. The chapter then presents the neurophysiological basis of plasticity and some specific models of plasticity. The final part of this chapter discusses some consequences of plasticity rules, in particular weight distributions, and explains some strategies for weight scaling and weight decay. The chapter ends with an application of a basic Hebbian rule with weight decay to principal component analysis.
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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, 47–58. 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 canonical cortical microcircuit would thus be a general-purpose neural network, fine-tuned by experience to solve any optimization problem.
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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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"Hebbian Learning and Spike-Timing-Dependent Plasticity." In Computational Neuroscience, 320–56. Chapman and Hall/CRC, 2003. http://dx.doi.org/10.1201/9780203494462-18.

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Boraud, Thomas. "The Winner Takes It All." In How the Brain Makes Decisions, 31–34. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198824367.003.0004.

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This chapter reviews the general principles that are necessary for a neural system to make decisions. A glance at the literature shows that the simplest system to obtain an imbalance between two populations of neurons subjected to the same activation consists of two interconnected populations of inhibitory neurons. These two populations exert lateral inhibition on each other. In order for a differential response to emerge, noise is necessary. Synaptic noise is considered the main source of noise in the nervous system. The chapter then goes on to look at positive feedback. It also studies the learning processes in the nervous system and explores neural plasticity rules, particularly the Hebbian learning rule.
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Conference papers on the topic "Plasticità Hebbiana"

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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. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3374587.3375880.

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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. New York, New York, USA: 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 problem. Body schemas are a biologically-inspired approach, emulating the plasticity of the animal brain, which allows it to learn non-linear mappings between the body configurations, i.e. its generalized coordinates and the resulting sensory outputs. The advantages of body schemas has long been recognized in the cognitive robotic literature and resulting studies on human-robot interactions based on artificial neural networks, however little effort has been made so far to develop avian-inspired flight control strategies utilizing body and image schemas. This paper presents a numerical experiment of controlling the trajectory of a miniature rotorcraft during landing maneuvers suing the notion of body and image schemas. More specifically, we demonstrate how trajectory planning can be executed in the image space using gradient-based maximum seeking algorithm of a pseudo-potential. It is demonstrated that a neural-gas type artificial neural network (ANN), trained through Hebbian-type learning algorithm, can be effective in learning a mapping between the rotorcraft’s position/attitude and the output of its vision sensors. Numerical simulation of the landing performance, including resulting landing errors are presented using an experimentally validated rotorcraft model.
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