Journal articles: '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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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 (December 1, 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 (NMDA) receptor activation for 3 h. Activity deprivation enhanced the frequency and the amplitude of spontaneous miniature excitatory postsynaptic currents and enhanced basal synaptic transmission in the absence of significant changes in intrinsic excitability. Changes in the threshold for Hebbian plasticity were evaluated by inducing LTP with stimulation protocols of increasing strength. We found that activity-deprived slices consistently showed higher LTP magnitude compared with control conditions even when using subthreshold theta-burst stimulation. Enhanced LTP in activity-deprived slices was also observed when picrotoxin was used to prevent the modulation of GABAergic transmission. Finally, we observed that consecutive LTP inductions attained a higher magnitude of facilitation in activity-deprived slices, suggesting that the homeostatic plasticity mechanisms triggered by a brief period of neuronal silencing can both lower the threshold and raise the ceiling for Hebbian modifications. We conclude that even brief periods of altered activity are able to shape subsequent synaptic transmission and Hebbian plasticity in fully developed hippocampal circuits.

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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 (March 1, 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 increase spontaneous activity in fusiform cells as well as alter stimulus timing-dependent plasticity (StTDP), it is important to understand the contribution of mAChRs to in vivo spontaneous activity and plasticity in fusiform cells. In the present study, we blocked mAChRs actions by infusing atropine, a mAChR antagonist, into the DCN fusiform cell layer in normal hearing guinea pigs. Atropine delivery leads to decreased spontaneous firing rates and increased synchronization of fusiform cell spiking activity. Consistent with StTDP alterations observed in tinnitus animals, atropine infusion induced a dominant pattern of inversion of StTDP mean population learning rule from a Hebbian to an anti-Hebbian profile. Units preserving their initial Hebbian learning rules shifted toward more excitatory changes in StTDP, whereas units with initial suppressive learning rules transitioned toward a Hebbian profile. Together, these results implicate muscarinic cholinergic modulation as a factor in controlling in vivo fusiform cell baseline activity and plasticity, suggesting a central role in the maladaptive plasticity associated with tinnitus pathology. NEW & NOTEWORTHY This study is the first to use a novel method of atropine infusion directly into the fusiform cell layer of the dorsal cochlear nucleus coupled with simultaneous recordings of neural activity to clarify the contribution of muscarinic acetylcholine receptors (mAChRs) to in vivo fusiform cell baseline activity and auditory-somatosensory plasticity. We have determined that blocking the mAChRs increases the synchronization of spiking activity across the fusiform cell population and induces a dominant pattern of inversion in their stimulus timing-dependent plasticity. These modifications are consistent with similar changes established in previous tinnitus studies, suggesting that mAChRs might have a critical contribution in mediating the maladaptive alterations associated with tinnitus pathology. Blocking mAChRs also resulted in decreased fusiform cell spontaneous firing rates, which is in contrast with their tinnitus hyperactivity, suggesting that changes in the interactions between the cholinergic and GABAergic systems might also be an underlying factor in tinnitus pathology.

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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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Martens, Marijn B., Tansu Celikel, and Paul H. E. Tiesinga. "A Developmental Switch for Hebbian Plasticity." PLOS Computational Biology 11, no. 7 (July 14, 2015): e1004386. http://dx.doi.org/10.1371/journal.pcbi.1004386.

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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 (April 2015): 197–206. http://dx.doi.org/10.1109/tbcas.2015.2410811.

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Keck, Tara, Taro Toyoizumi, Lu Chen, Brent Doiron, Daniel E. Feldman, Kevin Fox, Wulfram Gerstner, 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 (March 5, 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 issue ‘Integrating Hebbian and homeostatic plasticity’.

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Elliott, Terry. "An Analysis of Synaptic Normalization in a General Class of Hebbian Models." Neural Computation 15, no. 4 (April 1, 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 that our own neurotrophic model of synaptic plasticity, which can segregate positively correlated afferents, can be reformulated as a nonlinear Hebbian model with competition implemented through multiplicative normalization. We now perform an analysis of a general class of Hebbian models under general forms of synaptic normalization. In particular, we extract conditions on the forms of these rules that guarantee that such models possess a fixed-point structure permitting the segregation of all but perfectly correlated afferents. We find that the failure of multiplicative normalization to segregate positively correlated afferents in a standard Hebbian model is quite atypical.

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Rauschecker, Josef P. "Reverberations of Hebbian thinking." Behavioral and Brain Sciences 18, no. 4 (December 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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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 (March 5, 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, implemented by activity-dependent plasticity in parvalbumin interneuron circuits. These processes interact with Hebbian synaptic plasticity to maintain cortical firing rates during learned adjustments in sensory representations. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

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Vitureira, Nathalia, and Yukiko Goda. "The interplay between Hebbian and homeostatic synaptic plasticity." Journal of Cell Biology 203, no. 2 (October 28, 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. New findings on the cellular mechanisms and molecular players of the two forms of plasticity are uncovering the interplay between them in individual neurons.

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Acker, Daniel, Suzanne Paradis, and Paul Miller. "Stable memory and computation in randomly rewiring neural networks." Journal of Neurophysiology 122, no. 1 (July 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 memories can persist in neural circuits such as the hippocampus and visual cortex in the face of synapse turnover. We demonstrate that Hebbian plasticity during replay of presynaptic activity patterns can integrate newly formed synapses into pre-existing memories. Furthermore, we find that Hebbian plasticity during replay is sufficient to stabilize the receptive fields of hippocampal place cells in a model of the grid-cell-to-place-cell transformation in CA1 and of orientation-selective cells in a model of the center-surround-to-simple-cell transformation in V1. Together, these data suggest that a simple plasticity rule, correlative Hebbian plasticity of synaptic strengths, is sufficient to preserve neural representations in the face of synapse turnover, even in the absence of activity-dependent structural plasticity. NEW & NOTEWORTHY Recent research suggests that synapses turn over rapidly in some brain structures; however, memories seem to persist for much longer. We show that Hebbian plasticity of synaptic strengths during reactivation events can preserve memory in computational models of hippocampal and cortical networks despite turnover of all synapses. Our results suggest that memory can be stored in the correlation structure of a network undergoing rapid synaptic remodeling.

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Jackson, Meyer B. "Hebbian and non‐Hebbian timing‐dependent plasticity in the hippocampal CA3 region." Hippocampus 30, no. 12 (August 20, 2020): 1241–56. http://dx.doi.org/10.1002/hipo.23252.

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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 (August 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 width of the receptive fields (RFs): the channel with the weaker or broader RFs always exhibited most or all of the plasticity. A fundamental difference between our Hebbian model and most previous models is that in our model synaptic weights were normalized separately for each input channel, ensuring that the circuit would respond to both sensory inputs. The model produced three distinct regimes of plasticity dynamics (winner-take-all, mixed-shift, and no-shift), with the transition between the regimes depending on the size of the spatial displacement and the degree of correlation between the sensory channels. In agreement with experimental observations, plasticity was enhanced by the accumulation of incremental adaptive adjustments to a sequence of small displacements. These same principles would apply not only to the maintenance of spatial registry across input channels, but also to the experience-dependent emergence of aligned representations in developing circuits.

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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 (June 2022): 110951. http://dx.doi.org/10.1016/j.celrep.2022.110951.

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Rubin, Jonathan, Daniel D. Lee, and H. Sompolinsky. "Equilibrium Properties of Temporally Asymmetric Hebbian Plasticity." Physical Review Letters 86, no. 2 (January 8, 2001): 364–67. http://dx.doi.org/10.1103/physrevlett.86.364.

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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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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 (October 1, 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. Maximal enhancement occurred at 5 s and lasted for ∼120 s. Tetanic frequencies of 100–300 Hz produced enhancement; lower or higher frequencies failed to produce statistically significant changes in EPSP amplitude. Rates of 100–200 Hz occur in vivo in the cells of origin of the StF, suggesting that this form of plasticity may be operative under natural conditions. We could not elicit either long-term potentiation or depression by any stimulation protocol of the StF; in the case of long-term potentiation, this held even when excitatory transmission was enhanced by application of bicuculline, a γ-aminobutyric acid-A antagonist. When tetanic stimulation of the StF was paired with hyperpolarization of pyramidal cells, subsequent StF-evoked EPSPs were increased by 146% (5 min posttetanus); this anti-Hebbian synaptic enhancement lasted for ∼10 min. Neither tetanic stimulation alone, hyperpolarization alone, nor tetanic stimulation paired with pyramidal cell depolarization altered StF-evoked EPSP amplitudes on this time scale. Anti-Hebbian synaptic enhancement was not blocked by the N-methyl-d-aspartate–receptor antagonist D.L-aminophosphovalerate. The in vitro demonstration of anti-Hebbian plasticity at StF synapses replicates similar in vivo results. Anti-Hebbian synaptic plasticity of the StF may be responsible in part for the ability of gymnotiform fish to reject redundant electrosensory signals.

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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 (March 7, 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 inputs, respectively, which were driving concurrent changes at the striatal synapses. Moreover, TS- and CS-STDP induced heterosynaptic plasticity. We developed a calcium-based mathematical model of the coupled TS and CS plasticity, and simulations predict complex changes in the CS and TS plasticity maps depending on the precise cortex–thalamus–striatum engram. These predictions were experimentally validated using triplet-based STDP stimulations, which revealed the significant remodeling of the CS-STDP map upon TS activity, which is notably the induction of the LTD areas in the CS-STDP for specific timing regimes. TS-STDP exerts a greater influence on CS plasticity than CS-STDP on TS plasticity. These findings highlight the major impact of precise timing in cortical and thalamic activity for the memory engram of striatal synapses.

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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 (June 5, 2013): 9725–33. http://dx.doi.org/10.1523/jneurosci.4988-12.2013.

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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 (December 15, 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 inputs arising from a six-muscle arm. All synapses underwent Hebbian plasticity, while homeoplasticity adjusted cell excitability to maintain a desired firing distribution. After initial training, the network was lesioned, leading to areas of hyper- and hypoactivity due to the loss of lateral synaptic connections. The network was then retrained through rehabilitative arm movements. We found that network recovery was unsuccessful in the absence of homeoplasticity, as measured by reestablishment of lesion-affected inputs. We also found that a delay preceding rehabilitation led to faster network recovery during the rehabilitation training than no delay. Our simulation results thus suggest that homeoplastic restoration of prelesion activity patterns is essential to functional network recovery via Hebbian plasticity.

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Rizzo, Francesca Romana, Alessandra Musella, Francesca De Vito, Diego Fresegna, Silvia Bullitta, Valentina Vanni, Livia Guadalupi, et al. "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 pathological conditions and have been invariably associated with long-term potentiation (LTP) (Hebbian synaptic plasticity) and synaptic scaling (homeostatic plasticity), respectively. Multiple sclerosis (MS) is the prototypical neuroinflammatory disease, in which inflammation triggers excitotoxic mechanisms contributing to neurodegeneration. IL-β and TNF are increased in the brain of MS patients and contribute to induce the changes in synaptic plasticity occurring in MS patients and its animal model, the experimental autoimmune encephalomyelitis (EAE). This review will introduce and discuss current evidence of the role of IL-1β and TNF in the regulation of synaptic strength at both physiological and pathological levels, in particular speculating on their involvement in the synaptic plasticity changes observed in the EAE brain.

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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 (February 1, 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 animals showed primarily anti-Hebbian rules, with broadened profiles for the animals with behaviorally verified tinnitus (Koehler SD, Shore SE. J Neurosci 33: 19647–19656, 2013a). In the present study we show that well-timed bimodal stimulation induces alterations in the rate-level functions (RLFs) of fusiform cells. The RLF gains and maximum amplitudes show Hebbian modifications in sham and no-tinnitus animals but anti-Hebbian modifications in noise-exposed animals with evidence for tinnitus. These findings suggest that stimulus-timing bimodal plasticity produced by the DCN circuitry is a contributing mechanism to enhanced central gain associated with tinnitus.

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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 (December 1, 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 tinnitus. Here, we aimed to determine if multisensory STDP principles like those in DCN also exist in primary auditory cortex (A1), and whether they change following noise-induced tinnitus. Tone-evoked and spontaneous neural responses were recorded before and 15 min after bimodal stimulation in which the intervals and orders of auditory-somatosensory stimuli were randomized. Tone-evoked and spontaneous firing rates were influenced by the interval and order of the bimodal stimuli, and in sham-controls Hebbian-like timing rules predominated as was seen in DCN. In noise-exposed animals with and without tinnitus, timing rules shifted away from those found in sham-controls to more anti-Hebbian rules. Only those animals with evidence of tinnitus showed increased spontaneous firing rates, a purported neurophysiological correlate of tinnitus in A1. Together, these findings suggest that bimodal plasticity is also evident in A1 following noise damage and may have implications for tinnitus generation and therapeutic intervention across the central auditory circuit.

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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 unanswered questions about SHY. What is the cellular mechanism governing SHY? How does it fit with what we know about plasticity mechanisms in the brain? In this review, I discuss the evidence and theory of SHY in the context of what is known about Hebbian and non-Hebbian synaptic plasticity. I conclude that while SHY remains an elegant idea, the underlying mechanisms are mysterious and its functional significance unknown.

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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 (June 1, 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 15-h activity blockage with tetrodotoxin triggers homeostatic upregulation of glutamatergic transmission. On the other hand, attenuation of the correlated high-frequency bursting in the CA3-CA1 circuitry leads to weakening of AMPA transmission in CA1, thus reflecting a critical role for Hebbian synapse induction in the developing CA3-CA1. Both of these developmentally restricted forms of plasticity were absent in GluA4 −/− mice. These data suggest that GluA4 enables efficient homeostatic upscaling and responsiveness to temporal activity patterns during the critical period of activity-dependent refinement of the circuitry.

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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 (January 11, 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 plasticity determined by post-tetanic potentiation occurs due to the fast activation of presynaptic neurons. This violates the Hebbian learning rules that are specific to postsynaptic plasticity. Hebbian learning improves the SNN ability to discriminate the neural paths trained by the temporal association of events, which is the key element of learning in the brain. This paper quantifies the efficiency of Hebbian learning as the ratio between the LTP and PTP effects on the synaptic weights. On the basis of this new idea, this work evaluates for the first time the influence of the number of neurons on the PTP/LTP ratio and consequently on the Hebbian learning efficiency. The evaluation was performed by simulating a neuron model that was successfully tested in control applications. The results show that the firing rate of postsynaptic neurons post depends on the number of presynaptic neurons pre, which increases the effect of LTP on the synaptic potentiation. When post activates at a requested rate, the learning efficiency varies in the opposite direction with the number of pres, reaching its maximum when fewer than two pres are used. In addition, Hebbian learning is more efficient at lower presynaptic firing rates that are divisors of the target frequency of post. This study concluded that, when the electronic neurons additionally model presynaptic plasticity to LTP, the efficiency of Hebbian learning is higher when fewer neurons are used. This result strengthens the observations of our previous research where the SNN with a reduced number of neurons could successfully learn to control the motion of robotic fingers.

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Matsumoto, Narihisa, and Masato Okada. "Self-Regulation Mechanism of Temporally Asymmetric Hebbian Plasticity." Neural Computation 14, no. 12 (December 1, 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, we employ a simple neural network model and show that interference between LTP and LTD disappears in a sparse coding scheme. On the other hand, the covariance learning rule is known to be indispensable for the storage of sparse patterns. We also show that TAH has the same qualitative effect as the covariance rule when spatiotemporal patterns are embedded in the network.

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Scarpetta, Silvia, L. Zhaoping, and John Hertz. "Hebbian Imprinting and Retrieval in Oscillatory Neural Networks." Neural Computation 14, no. 10 (October 1, 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 phase of neural plasticity, in which the network is clamped to external teaching signals. By suitable manipulation of the nonlinearity of the neurons or the oscillation frequencies during learning, the model can be made, in a retrieval phase, either to categorize new inputs or to map them, in a continuous fashion, onto the space spanned by the imprinted patterns. We identify the first of these possibilities with the function of olfactory cortex and the second with the observed response characteristics of place cells in hippocampus. We investigate both kinds of networks analytically and by computer simulations, and we link the models with experimental findings, exploring, in particular, how the spike timing dependence of the synaptic plasticity constrains the computational function of the network and vice versa.

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Magotra, Arjun, and Juntae Kim. "Neuromodulated Dopamine Plastic Networks for Heterogeneous Transfer Learning with Hebbian Principle." Symmetry 13, no. 8 (July 26, 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. This paper proposes a novel approach to enhance transfer learning accuracy in a heterogeneous source and target, using the neuromodulation of the Hebbian learning principle, called NDHTL (Neuromodulated Dopamine Hebbian Transfer Learning). Neuromodulation of plasticity offers a powerful new technique with applications in training neural networks implementing asymmetric backpropagation using Hebbian principles in transfer learning motivated CNNs (Convolutional neural networks). Biologically motivated concomitant learning, where connected brain cells activate positively, enhances the synaptic connection strength between the network neurons. Using the NDHTL algorithm, the percentage of change of the plasticity between the neurons of the CNN layer is directly managed by the dopamine signal’s value. The discriminative nature of transfer learning fits well with the technique. The learned model’s connection weights must adapt to unseen target datasets with the least cost and effort in transfer learning. Using distinctive learning principles such as dopamine Hebbian learning in transfer learning for asymmetric gradient weights update is a novel approach. The paper emphasizes the NDHTL algorithmic technique as synaptic plasticity controlled by dopamine signals in transfer learning to classify images using source-target datasets. The standard transfer learning using gradient backpropagation is a symmetric framework. Experimental results using CIFAR-10 and CIFAR-100 datasets show that the proposed NDHTL algorithm can enhance transfer learning efficiency compared to existing methods.

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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 collective dynamic activities of stochastic receptors. An experiment was conducted to analyze can spike-timing-dependent plasticity interplay with short-term plasticity to balance the excitation of the Hebbian neurons without weight constraints? If so what underline mechanisms help neurons to maintain such excitation in computational environment? According to our results both plasticity mechanisms work together to balance the excitation of the neural network as our neurons stabilized its weights for Poisson inputs with mean firing rates from 10 Hz to 40 Hz. The behavior generated by the two neurons was similar to the behavior discussed under synaptic redistribution, so that synaptic weights were stabilized while there was a continuous increase of presynaptic probability of release and higher turnover rate of postsynaptic receptors.

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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 (January 5, 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 responsiveness to stimuli viewed through the deprived eye. Second, we describe a less studied, but no less interesting form of plasticity in the visual cortex known as stimulus-selective response potentiation (SRP). SRP results in increases in the response of V1 to a visual stimulus through repeated viewing and bears all the hallmarks of perceptual learning. We describe evidence implicating an important role for potentiation of thalamo-cortical synapses in SRP. In addition, we present new data indicating that there are some features of this form of plasticity that cannot be fully accounted for by such feed-forward Hebbian plasticity, suggesting contributions from intra-cortical circuit components.

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Cho, Myoung Won. "Temporal Hebbian plasticity designed for efficient competitive learning." Journal of the Korean Physical Society 64, no. 8 (April 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 (October 20, 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 (December 1, 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 (July 2008): 25–46. http://dx.doi.org/10.1146/annurev.neuro.31.060407.125639.

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Huang, S., C. Rozas, M. Trevino, J. Contreras, S. Yang, L. Song, T. Yoshioka, H. K. Lee, and A. Kirkwood. "Associative Hebbian Synaptic Plasticity in Primate Visual Cortex." Journal of Neuroscience 34, no. 22 (May 28, 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 (March 2017): e5-e6. http://dx.doi.org/10.1016/j.clinph.2016.10.116.

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Kronberg, Greg, Asif Rahman, Mahima Sharma, Marom Bikson, and Lucas C. Parra. "Direct current stimulation boosts hebbian plasticity in vitro." Brain Stimulation 13, no. 2 (March 2020): 287–301. http://dx.doi.org/10.1016/j.brs.2019.10.014.

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Pehlevan, Cengiz, Anirvan M. Sengupta, and Dmitri B. Chklovskii. "Why Do Similarity Matching Objectives Lead to Hebbian/Anti-Hebbian Networks?" Neural Computation 30, no. 1 (January 2018): 84–124. http://dx.doi.org/10.1162/neco_a_01018.

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Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience. Yet derivations of single-layer networks with such local learning rules from principled optimization objectives became possible only recently, with the introduction of similarity matching objectives. What explains the success of similarity matching objectives in deriving neural networks with local learning rules? Here, using dimensionality reduction as an example, we introduce several variable substitutions that illuminate the success of similarity matching. We show that the full network objective may be optimized separately for each synapse using local learning rules in both the offline and online settings. We formalize the long-standing intuition of the rivalry between Hebbian and anti-Hebbian rules by formulating a min-max optimization problem. We introduce a novel dimensionality reduction objective using fractional matrix exponents. To illustrate the generality of our approach, we apply it to a novel formulation of dimensionality reduction combined with whitening. We confirm numerically that the networks with learning rules derived from principled objectives perform better than those with heuristic learning rules.

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Edeline, JM. "Does Hebbian synaptic plasticity explain learning-induced sensory plasticity in adult mammals?" Journal of Physiology-Paris 90, no. 3-4 (January 1996): 271–76. http://dx.doi.org/10.1016/s0928-4257(97)81437-4.

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Journal articles on the topic 'Plasticità Hebbiana'

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