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Journal articles on the topic 'Plasticità Hebbiana'

Author: Grafiati

Published: 9 March 2023

Last updated: 31 July 2025

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1

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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3

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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8

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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10

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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11

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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12

Félix-Oliveira, A., R. B. Dias, M. Colino-Oliveira, D. M. Rombo, and A. M. Sebastião. "Homeostatic plasticity induced by brief activity deprivation enhances long-term potentiation in the mature rat hippocampus." Journal of Neurophysiology 112, no. 11 (2014): 3012–22. http://dx.doi.org/10.1152/jn.00058.2014.

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Different forms of plasticity occur concomitantly in the nervous system. Whereas homeostatic plasticity monitors and maintains neuronal activity within a functional range, Hebbian changes such as long-term potentiation (LTP) modify the relative strength of specific synapses after discrete changes in activity and are thought to provide the cellular basis for learning and memory. Here, we assessed whether homeostatic plasticity could influence subsequent LTP in acute hippocampal slices that had been briefly deprived of activity by blocking action potential generation and N-methyl-d-aspartate (NM

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13

Stefanescu, Roxana A., and Susan E. Shore. "Muscarinic acetylcholine receptors control baseline activity and Hebbian stimulus timing-dependent plasticity in fusiform cells of the dorsal cochlear nucleus." Journal of Neurophysiology 117, no. 3 (2017): 1229–38. http://dx.doi.org/10.1152/jn.00270.2016.

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Cholinergic modulation contributes to adaptive sensory processing by controlling spontaneous and stimulus-evoked neural activity and long-term synaptic plasticity. In the dorsal cochlear nucleus (DCN), in vitro activation of muscarinic acetylcholine receptors (mAChRs) alters the spontaneous activity of DCN neurons and interacts with N-methyl-d-aspartate (NMDA) and endocannabinoid receptors to modulate the plasticity of parallel fiber synapses onto fusiform cells by converting Hebbian long-term potentiation to anti-Hebbian long-term depression. Because noise exposure and tinnitus are known to i

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14

Keck, Tara, Taro Toyoizumi, Lu Chen, et al. "Integrating Hebbian and homeostatic plasticity: the current state of the field and future research directions." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160158. http://dx.doi.org/10.1098/rstb.2016.0158.

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We summarize here the results presented and subsequent discussion from the meeting on Integrating Hebbian and Homeostatic Plasticity at the Royal Society in April 2016. We first outline the major themes and results presented at the meeting. We next provide a synopsis of the outstanding questions that emerged from the discussion at the end of the meeting and finally suggest potential directions of research that we believe are most promising to develop an understanding of how these two forms of plasticity interact to facilitate functional changes in the brain. This article is part of the themed

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15

Card, H. C., and W. R. Moore. "EEPROM synapses exhibiting pseudo-Hebbian plasticity." Electronics Letters 25, no. 12 (1989): 805. http://dx.doi.org/10.1049/el:19890543.

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16

Martens, Marijn B., Tansu Celikel, and Paul H. E. Tiesinga. "A Developmental Switch for Hebbian Plasticity." PLOS Computational Biology 11, no. 7 (2015): e1004386. http://dx.doi.org/10.1371/journal.pcbi.1004386.

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17

Lansner, Anders, Florian Fiebig, and Pawel Herman. "Fast Hebbian plasticity and working memory." Current Opinion in Neurobiology 83 (December 2023): 102809. http://dx.doi.org/10.1016/j.conb.2023.102809.

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18

Ziegler, Martin, Christoph Riggert, Mirko Hansen, Thorsten Bartsch, and Hermann Kohlstedt. "Memristive Hebbian Plasticity Model: Device Requirements for the Emulation of Hebbian Plasticity Based on Memristive Devices." IEEE Transactions on Biomedical Circuits and Systems 9, no. 2 (2015): 197–206. http://dx.doi.org/10.1109/tbcas.2015.2410811.

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19

Elliott, Terry. "An Analysis of Synaptic Normalization in a General Class of Hebbian Models." Neural Computation 15, no. 4 (2003): 937–63. http://dx.doi.org/10.1162/08997660360581967.

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In standard Hebbian models of developmental synaptic plasticity, synaptic normalization must be introduced in order to constrain synaptic growth and ensure the presence of activity-dependent, competitive dynamics. In such models, multiplicative normalization cannot segregate afferents whose patterns of electrical activity are positively correlated, while subtractive normalization can. It is now widely believed that multiplicative normalization cannot segregate positively correlated afferents in any Hebbian model. However, we recently provided a counterexample to this belief by demonstrating th

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Gainey, Melanie A., and Daniel E. Feldman. "Multiple shared mechanisms for homeostatic plasticity in rodent somatosensory and visual cortex." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (2017): 20160157. http://dx.doi.org/10.1098/rstb.2016.0157.

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We compare the circuit and cellular mechanisms for homeostatic plasticity that have been discovered in rodent somatosensory (S1) and visual (V1) cortex. Both areas use similar mechanisms to restore mean firing rate after sensory deprivation. Two time scales of homeostasis are evident, with distinct mechanisms. Slow homeostasis occurs over several days, and is mediated by homeostatic synaptic scaling in excitatory networks and, in some cases, homeostatic adjustment of pyramidal cell intrinsic excitability. Fast homeostasis occurs within less than 1 day, and is mediated by rapid disinhibition, i

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21

Rauschecker, Josef P. "Reverberations of Hebbian thinking." Behavioral and Brain Sciences 18, no. 4 (1995): 642–43. http://dx.doi.org/10.1017/s0140525x00040358.

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AbstractCortical reverberations may induce synaptic changes that underlie developmental plasticity as well as long-term memory. They may be especially important for the consolidation of synaptic changes. Reverberations in cortical networks should have particular significance during development, when large numbers of new representations are formed. This includes the formation of representations across different sensory modalities.

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22

Acker, Daniel, Suzanne Paradis, and Paul Miller. "Stable memory and computation in randomly rewiring neural networks." Journal of Neurophysiology 122, no. 1 (2019): 66–80. http://dx.doi.org/10.1152/jn.00534.2018.

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Our brains must maintain a representation of the world over a period of time much longer than the typical lifetime of the biological components producing that representation. For example, recent research suggests that dendritic spines in the adult mouse hippocampus are transient with an average lifetime of ~10 days. If this is true, and if turnover is equally likely for all spines, ~95% of excitatory synapses onto a particular neuron will turn over within 30 days; however, a neuron’s receptive field can be relatively stable over this period. Here, we use computational modeling to ask how memor

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23

Vitureira, Nathalia, and Yukiko Goda. "The interplay between Hebbian and homeostatic synaptic plasticity." Journal of Cell Biology 203, no. 2 (2013): 175–86. http://dx.doi.org/10.1083/jcb.201306030.

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Synaptic plasticity, a change in the efficacy of synaptic signaling, is a key property of synaptic communication that is vital to many brain functions. Hebbian forms of long-lasting synaptic plasticity—long-term potentiation (LTP) and long-term depression (LTD)—have been well studied and are considered to be the cellular basis for particular types of memory. Recently, homeostatic synaptic plasticity, a compensatory form of synaptic strength change, has attracted attention as a cellular mechanism that counteracts changes brought about by LTP and LTD to help stabilize neuronal network activity.

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24

Jackson, Meyer B. "Hebbian and non‐Hebbian timing‐dependent plasticity in the hippocampal CA3 region." Hippocampus 30, no. 12 (2020): 1241–56. http://dx.doi.org/10.1002/hipo.23252.

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25

Witten, Ilana B., Eric I. Knudsen, and Haim Sompolinsky. "A Hebbian Learning Rule Mediates Asymmetric Plasticity in Aligning Sensory Representations." Journal of Neurophysiology 100, no. 2 (2008): 1067–79. http://dx.doi.org/10.1152/jn.00013.2008.

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In the brain, mutual spatial alignment across different sensory representations can be shaped and maintained through plasticity. Here, we use a Hebbian model to account for the synaptic plasticity that results from a displacement of the space representation for one input channel relative to that of another, when the synapses from both channels are equally plastic. Surprisingly, although the synaptic weights for the two channels obeyed the same Hebbian learning rule, the amount of plasticity exhibited by the respective channels was highly asymmetric and depended on the relative strength and wid

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26

Wang, Daliang, and Leonard Maler. "In Vitro Plasticity of the Direct Feedback Pathway in the Electrosensory System of Apteronotus leptorhynchus." Journal of Neurophysiology 78, no. 4 (1997): 1882–89. http://dx.doi.org/10.1152/jn.1997.78.4.1882.

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Wang, Daliang and Leonard Maler. In vitro plasticity of the direct feedback pathway in the electrosensory system of Apteronotus leptorhynchus. J. Neurophysiol. 78: 1882–1889, 1997. We have used field and intracellular recording from pyramidal cells in an in vitro preparation of the electrosensory lateral line lobe (ELL) of Apteronotus leptorhynchus to investigate synaptic plasticity of a direct feedback pathway: the (StF). Tetanic stimulation of the StF enhanced the StF-evoked synaptic response by 145% in field and the excitatory postsynaptic potential (EPSP) 190% in intracellular recordings.

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Mendes, Alexandre, Gaetan Vignoud, Sylvie Perez, Elodie Perrin, Jonathan Touboul, and Laurent Venance. "Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map." Cerebral Cortex 30, no. 8 (2020): 4381–401. http://dx.doi.org/10.1093/cercor/bhaa024.

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Abstract The striatum integrates inputs from the cortex and thalamus, which display concomitant or sequential activity. The striatum assists in forming memory, with acquisition of the behavioral repertoire being associated with corticostriatal (CS) plasticity. The literature has mainly focused on that CS plasticity, and little remains known about thalamostriatal (TS) plasticity rules or CS and TS plasticity interactions. We undertook here the study of these plasticity rules. We found bidirectional Hebbian and anti-Hebbian spike-timing-dependent plasticity (STDP) at the thalamic and cortical in

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Lazari, Alberto, Piergiorgio Salvan, Michiel Cottaar, Daniel Papp, Matthew F. S. Rushworth, and Heidi Johansen-Berg. "Hebbian activity-dependent plasticity in white matter." Cell Reports 39, no. 11 (2022): 110951. http://dx.doi.org/10.1016/j.celrep.2022.110951.

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29

Rubin, Jonathan, Daniel D. Lee, and H. Sompolinsky. "Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity." Physical Review Letters 86, no. 2 (2001): 364–67. http://dx.doi.org/10.1103/physrevlett.86.364.

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30

Andersen, Niels, Nathalie Krauth, and Sadegh Nabavi. "Hebbian plasticity in vivo: relevance and induction." Current Opinion in Neurobiology 45 (August 2017): 188–92. http://dx.doi.org/10.1016/j.conb.2017.06.001.

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31

Bains, Amarpreet Singh, and Nicolas Schweighofer. "Time-sensitive reorganization of the somatosensory cortex poststroke depends on interaction between Hebbian and homeoplasticity: a simulation study." Journal of Neurophysiology 112, no. 12 (2014): 3240–50. http://dx.doi.org/10.1152/jn.00433.2013.

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Together with Hebbian plasticity, homeoplasticity presumably plays a significant, yet unclear, role in recovery postlesion. Here, we undertake a simulation study addressing the role of homeoplasticity and rehabilitation timing poststroke. We first hypothesize that homeoplasticity is essential for recovery and second that rehabilitation training delivered too early, before homeoplasticity has compensated for activity disturbances postlesion, is less effective for recovery than training delivered after a delay. We developed a neural network model of the sensory cortex driven by muscle spindle in

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Rizzo, Francesca Romana, Alessandra Musella, Francesca De Vito та ін. "Tumor Necrosis Factor and Interleukin-1β Modulate Synaptic Plasticity during Neuroinflammation". Neural Plasticity 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/8430123.

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Cytokines are constitutively released in the healthy brain by resident myeloid cells to keep proper synaptic plasticity, either in the form of Hebbian synaptic plasticity or of homeostatic plasticity. However, when cytokines dramatically increase, establishing a status of neuroinflammation, the synaptic action of such molecules remarkably interferes with brain circuits of learning and cognition and contributes to excitotoxicity and neurodegeneration. Among others, interleukin-1β (IL-1β) and tumor necrosis factor (TNF) are the best studied proinflammatory cytokines in both physiological and pat

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33

Stefanescu, Roxana A., Seth D. Koehler, and Susan E. Shore. "Stimulus-timing-dependent modifications of rate-level functions in animals with and without tinnitus." Journal of Neurophysiology 113, no. 3 (2015): 956–70. http://dx.doi.org/10.1152/jn.00457.2014.

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Tinnitus has been associated with enhanced central gain manifested by increased spontaneous activity and sound-evoked firing rates of principal neurons at various stations of the auditory pathway. Yet, the mechanisms leading to these modifications are not well understood. In a recent in vivo study, we demonstrated that stimulus-timing-dependent bimodal plasticity mediates modifications of spontaneous and tone-evoked responses of fusiform cells in the dorsal cochlear nucleus (DCN) of the guinea pig. Fusiform cells from sham animals showed primarily Hebbian learning rules while noise-exposed ani

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Basura, Gregory J., Seth D. Koehler, and Susan E. Shore. "Bimodal stimulus timing-dependent plasticity in primary auditory cortex is altered after noise exposure with and without tinnitus." Journal of Neurophysiology 114, no. 6 (2015): 3064–75. http://dx.doi.org/10.1152/jn.00319.2015.

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Central auditory circuits are influenced by the somatosensory system, a relationship that may underlie tinnitus generation. In the guinea pig dorsal cochlear nucleus (DCN), pairing spinal trigeminal nucleus (Sp5) stimulation with tones at specific intervals and orders facilitated or suppressed subsequent tone-evoked neural responses, reflecting spike timing-dependent plasticity (STDP). Furthermore, after noise-induced tinnitus, bimodal responses in DCN were shifted from Hebbian to anti-Hebbian timing rules with less discrete temporal windows, suggesting a role for bimodal plasticity in tinnitu

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Koch, G., V. Ponzo, F. Di Lorenzo, C. Caltagirone, and D. Veniero. "Hebbian and Anti-Hebbian Spike-Timing-Dependent Plasticity of Human Cortico-Cortical Connections." Journal of Neuroscience 33, no. 23 (2013): 9725–33. http://dx.doi.org/10.1523/jneurosci.4988-12.2013.

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36

Frank, Marcos Gabriel. "Erasing Synapses in Sleep: Is It Time to Be SHY?" Neural Plasticity 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/264378.

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Converging lines of evidence strongly support a role for sleep in brain plasticity. An elegant idea that may explain how sleep accomplishes this role is the “synaptic homeostasis hypothesis (SHY).” According to SHY, sleep promotes net synaptic weakening which offsets net synaptic strengthening that occurs during wakefulness. SHY is intuitively appealing because it relates the homeostatic regulation of sleep to an important function (synaptic plasticity). SHY has also received important experimental support from recent studies inDrosophila melanogaster. There remain, however, a number of unansw

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Huupponen, J., T. Atanasova, T. Taira, and S. E. Lauri. "GluA4 subunit of AMPA receptors mediates the early synaptic response to altered network activity in the developing hippocampus." Journal of Neurophysiology 115, no. 6 (2016): 2989–96. http://dx.doi.org/10.1152/jn.00435.2015.

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Development of the neuronal circuitry involves both Hebbian and homeostatic plasticity mechanisms that orchestrate activity-dependent refinement of the synaptic connectivity. AMPA receptor subunit GluA4 is expressed in hippocampal pyramidal neurons during early postnatal period and is critical for neonatal long-term potentiation; however, its role in homeostatic plasticity is unknown. Here we show that GluA4-dependent plasticity mechanisms allow immature synapses to promptly respond to alterations in network activity. In the neonatal CA3, the threshold for homeostatic plasticity is low, and a

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Uleru, George-Iulian, Mircea Hulea, and Alexandru Barleanu. "The Influence of the Number of Spiking Neurons on Synaptic Plasticity." Biomimetics 8, no. 1 (2023): 28. http://dx.doi.org/10.3390/biomimetics8010028.

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The main advantages of spiking neural networks are the high biological plausibility and their fast response due to spiking behaviour. The response time decreases significantly in the hardware implementation of SNN because the neurons operate in parallel. Compared with the traditional computational neural network, the SNN use a lower number of neurons, which also reduces their cost. Another critical characteristic of SNN is their ability to learn by event association that is determined mainly by postsynaptic mechanisms such as long-term potentiation. However, in some conditions, presynaptic pla

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Bergoin, Raphaël, Alessandro Torcini, Gustavo Deco, Mathias Quoy, and Gorka Zamora-López. "Emergence and maintenance of modularity in neural networks with Hebbian and anti-Hebbian inhibitory STDP." PLOS Computational Biology 21, no. 4 (2025): e1012973. https://doi.org/10.1371/journal.pcbi.1012973.

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The modular and hierarchical organization of the brain is believed to support the coexistence of segregated (specialization) and integrated (binding) information processes. A relevant question is yet to understand how such architecture naturally emerges and is sustained over time, given the plastic nature of the brain’s wiring. Following evidences that the sensory cortices organize into assemblies under selective stimuli, it has been shown that stable neuronal assemblies can emerge due to targeted stimulation, embedding various forms of synaptic plasticity in presence of homeostatic and/or con

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Magotra, Arjun, and Juntae Kim. "Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle." Symmetry 13, no. 8 (2021): 1344. http://dx.doi.org/10.3390/sym13081344.

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The plastic modifications in synaptic connectivity is primarily from changes triggered by neuromodulated dopamine signals. These activities are controlled by neuromodulation, which is itself under the control of the brain. The subjective brain’s self-modifying abilities play an essential role in learning and adaptation. The artificial neural networks with neuromodulated plasticity are used to implement transfer learning in the image classification domain. In particular, this has application in image detection, image segmentation, and transfer of learning parameters with significant results. Th

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Matsumoto, Narihisa, and Masato Okada. "Self-Regulation Mechanism of Temporally Asymmetric Hebbian Plasticity." Neural Computation 14, no. 12 (2002): 2883–902. http://dx.doi.org/10.1162/089976602760805322.

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Recent biological experimental findings have shown that synaptic plasticity depends on the relative timing of the pre- and postsynaptic spikes. This determines whether long-term potentiation (LTP) or long-term depression (LTD) is induced. This synaptic plasticity has been called temporally asymmetric Hebbian plasticity (TAH). Many authors have numerically demonstrated that neural networks are capable of storing spatiotemporal patterns. However, the mathematical mechanism of the storage of spatiotemporal patterns is still unknown, and the effect of LTD is particularly unknown. In this article,

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Scarpetta, Silvia, L. Zhaoping, and John Hertz. "Hebbian Imprinting and Retrieval in Oscillatory Neural Networks." Neural Computation 14, no. 10 (2002): 2371–96. http://dx.doi.org/10.1162/08997660260293265.

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We introduce a model of generalized Hebbian learning and retrieval in oscillatory neural networks modeling cortical areas such as hippocampus and olfactory cortex. Recent experiments have shown that synaptic plasticity depends on spike timing, especially on synapses from excitatory pyramidal cells, in hippocampus, and in sensory and cerebellar cortex. Here we study how such plasticity can be used to form memories and input representations when the neural dynamics are oscillatory, as is common in the brain (particularly in the hippocampus and olfactory cortex). Learning is assumed to occur in a

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Fernando, Subha, and Koichi Yamada. "Spike-Timing-Dependent Plasticity and Short-Term Plasticity Jointly Control the Excitation of Hebbian Plasticity without Weight Constraints in Neural Networks." Computational Intelligence and Neuroscience 2012 (2012): 1–15. http://dx.doi.org/10.1155/2012/968272.

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Hebbian plasticity precisely describes how synapses increase their synaptic strengths according to the correlated activities between two neurons; however, it fails to explain how these activities dilute the strength of the same synapses. Recent literature has proposed spike-timing-dependent plasticity and short-term plasticity on multiple dynamic stochastic synapses that can control synaptic excitation and remove many user-defined constraints. Under this hypothesis, a network model was implemented giving more computational power to receptors, and the behavior at a synapse was defined by the co

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Cooke, Sam F., and Mark F. Bear. "How the mechanisms of long-term synaptic potentiation and depression serve experience-dependent plasticity in primary visual cortex." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1633 (2014): 20130284. http://dx.doi.org/10.1098/rstb.2013.0284.

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Donald Hebb chose visual learning in primary visual cortex (V1) of the rodent to exemplify his theories of how the brain stores information through long-lasting homosynaptic plasticity. Here, we revisit V1 to consider roles for bidirectional ‘Hebbian’ plasticity in the modification of vision through experience. First, we discuss the consequences of monocular deprivation (MD) in the mouse, which have been studied by many laboratories over many years, and the evidence that synaptic depression of excitatory input from the thalamus is a primary contributor to the loss of visual cortical responsive

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Cho, Myoung Won. "Temporal Hebbian plasticity designed for efficient competitive learning." Journal of the Korean Physical Society 64, no. 8 (2014): 1213–19. http://dx.doi.org/10.3938/jkps.64.1213.

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Moore, Jason J., Jesse D. Cushman, Lavanya Acharya, Briana Popeney, and Mayank R. Mehta. "Linking hippocampal multiplexed tuning, Hebbian plasticity and navigation." Nature 599, no. 7885 (2021): 442–48. http://dx.doi.org/10.1038/s41586-021-03989-z.

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van Rossum, M. C. W., G. Q. Bi, and G. G. Turrigiano. "Stable Hebbian Learning from Spike Timing-Dependent Plasticity." Journal of Neuroscience 20, no. 23 (2000): 8812–21. http://dx.doi.org/10.1523/jneurosci.20-23-08812.2000.

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Caporale, Natalia, and Yang Dan. "Spike Timing–Dependent Plasticity: A Hebbian Learning Rule." Annual Review of Neuroscience 31, no. 1 (2008): 25–46. http://dx.doi.org/10.1146/annurev.neuro.31.060407.125639.

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Huang, S., C. Rozas, M. Trevino, et al. "Associative Hebbian Synaptic Plasticity in Primate Visual Cortex." Journal of Neuroscience 34, no. 22 (2014): 7575–79. http://dx.doi.org/10.1523/jneurosci.0983-14.2014.

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Koch, G. "T017 Hebbian plasticity in the parieto-frontal network." Clinical Neurophysiology 128, no. 3 (2017): e5-e6. http://dx.doi.org/10.1016/j.clinph.2016.10.116.

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