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Academic literature on the topic 'Hebburn Ltd'
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Journal articles on the topic "Hebburn Ltd"
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Grzywacz, Norberto M., and Pierre-Yves Burgi. "Toward a Biophysically Plausible Bidirectional Hebbian Rule." Neural Computation 10, no. 3 (April 1, 1998): 499–520. http://dx.doi.org/10.1162/089976698300017629.
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Although the commonly used quadratic Hebbian-;anti-Hebbian rules lead to successful models of plasticity and learning, they are inconsistent with neurophysiology. Other rules, more physiologically plausible, fail to specify the biological mechanism of bidirectionality and the biological mechanism that prevents synapses from changing from excitatory to inhibitory, and vice versa. We developed a synaptic bidirectional Hebbian rule that does not suffer from these problems. This rule was compared with physiological homosynaptic conditions in the hippocampus, with the results indicating the consistency of this rule with long-term potentiation (LTP) and long-term depression (LTD) phenomenologies. The phenomenologies considered included the reversible dynamics of LTP and LTD and the effects of N-methyl-D-aspartate blockers and phosphatase inhibitors.
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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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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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Scheiderer, Cary L., Lynn E. Dobrunz, and Lori L. McMahon. "Novel Form of Long-Term Synaptic Depression in Rat Hippocampus Induced By Activation of α1 Adrenergic Receptors." Journal of Neurophysiology 91, no. 2 (February 2004): 1071–77. http://dx.doi.org/10.1152/jn.00420.2003.
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Neurons located in the locus coeruleus project to hippocampus and provide noradrenergic innervation necessary for hippocampal-dependent learning and memory. The mechanisms underlying the function of norepinephrine (NE) in memory processing are unknown but likely reside in the ability of NE to modulate the efficacy of glutamate synaptic transmission via activation of G-protein-coupled adrenergic receptors. Here we show that application of NE to rat hippocampal slices in vitro induces a long-term depression (LTD) of synaptic transmission at excitatory CA3–CA1 synapses that persists for ≥40 min after agonist washout. This LTD, which we refer to as NE LTD, is mediated by activation of α1 adrenergic receptors because the α1 agonist methoxamine can induce LTD at the same magnitude as that induced with the nonselective adrenergic agonist NE. Furthermore, NE LTD induced by either NE or methoxamine is blocked with the α1 receptor antagonist, prazosin, but is unaffected by antagonists of α2 and β receptors. This plasticity persists in the presence of the GABAA receptor antagonist bicuculline, indicating that adrenergic modulation of GABAA receptor-mediated transmission does not underlie NE LTD. Induction of NE LTD requires presynaptic activity during agonist application and postsynaptic activation of N-methyl-d-aspartate receptors, fulfilling Hebbian criteria of coincident pre- and postsynaptic activity. The expression of NE LTD is likely to be postsynaptic because paired-pulse facilitation ratios during NE LTD expression are not different from baseline, similar to LTD induced by low-frequency stimulation. Thus we report the identification and characterization of a novel Hebbian form of LTD in hippocampus that is induced after activation of α1 adrenergic receptors. This plasticity may be a mechanism by which the adrenergic system participates in normal cognitive function.
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Saudargiene, Ausra, Bernd Porr, and Florentin Wörgötter. "How the Shape of Pre- and Postsynaptic Signals Can Influence STDP: A Biophysical Model." Neural Computation 16, no. 3 (March 1, 2004): 595–625. http://dx.doi.org/10.1162/089976604772744929.
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Spike-timing-dependent plasticity (STDP) is described by long-term potentiation (LTP), when a presynaptic event precedes a postsynaptic event, and by long-term depression (LTD), when the temporal order is reversed. In this article, we present a biophysical model of STDP based on a differential Hebbian learning rule (ISO learning). This rule correlates presynaptically the NMDA channel conductance with the derivative of the membrane potential at the synapse as the postsynaptic signal. The model is able to reproduce the generic STDP weight change characteristic. We find that (1) The actual shape of the weight change curve strongly depends on the NMDA channel characteristics and on the shape of the membrane potential at the synapse. (2) The typical antisymmetrical STDP curve (LTD and LTP) can become similar to a standard Hebbian characteristic (LTP only) without having to change the learning rule. This occurs if the membrane depolarization has a shallow onset and is long lasting. (3) It is known that the membrane potential varies along the dendrite as a result of the active or passive backpropagation of somatic spikes or because of local dendritic processes. As a consequence, our model predicts that learning properties will be different at different locations on the dendritic tree. In conclusion, such site-specific synaptic plasticity would provide a neuron with powerful learning capabilities.
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Fiori, Simone. "Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay." Neural Computation 17, no. 4 (April 1, 2005): 779–838. http://dx.doi.org/10.1162/0899766053429381.
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The Hebbian paradigm is perhaps the best-known unsupervised learning theory in connectionism. It has inspired wide research activity in the artificial neural network field because it embodies some interesting properties such as locality and the capability of being applicable to the basic weight-and-sum structure of neuron models. The plain Hebbian principle, however, also presents some inherent theoretical limitations that make it impractical in most cases. Therefore, modifications of the basic Hebbian learning paradigm have been proposed over the past 20 years in order to design profitable signal and data processing algorithms. Such modifications led to the principal component analysis type class of learning rules along with their nonlinear extensions. The aim of this review is primarily to present part of the existing fragmented material in the field of principal component learning within a unified view and contextually to motivate and present extensions of previous works on Hebbian learning to complex-weighted linear neural networks. This work benefits from previous studies on linear signal decomposition by artificial neural networks, nonquadratic component optimization and reconstruction error definition, neural parameters adaptation by constrained optimization of learning criteria of complex-valued arguments, and orthonormality expression via the insertion of topological elements in the networks or by modifying the network learning criterion. In particular, the learning principles considered here and their analysis concern complex-valued principal/minor component/subspace linear/nonlinear rules for complex-weighted neural structures, both feedforward and laterally connected.
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Yong, Adeline J. H., Han L. Tan, Qianwen Zhu, Alexei M. Bygrave, Richard C. Johnson, and Richard L. Huganir. "Tyrosine phosphorylation of the AMPA receptor subunit GluA2 gates homeostatic synaptic plasticity." Proceedings of the National Academy of Sciences 117, no. 9 (February 18, 2020): 4948–58. http://dx.doi.org/10.1073/pnas.1918436117.
Full textAbstract:
Hebbian plasticity, comprised of long-term potentiation (LTP) and depression (LTD), allows neurons to encode and respond to specific stimuli; while homeostatic synaptic scaling is a counterbalancing mechanism that enables the maintenance of stable neural circuits. Both types of synaptic plasticity involve the control of postsynaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor (AMPAR) abundance, which is modulated by AMPAR phosphorylation. To address the necessity of GluA2 phospho-Y876 in synaptic plasticity, we generated phospho-deficient GluA2 Y876F knock-in mice. We show that, while GluA2 phospho-Y876 is not necessary for Hebbian plasticity, it is essential for both in vivo and in vitro homeostatic upscaling. Bidirectional changes in GluA2 phospho-Y876 were observed during homeostatic scaling, with a decrease during downscaling and an increase during upscaling. GluA2 phospho-Y876 is necessary for synaptic accumulation of glutamate receptor interacting protein 1 (GRIP1), a crucial scaffold protein that delivers AMPARs to synapses, during upscaling. Furthermore, increased phosphorylation at GluA2 Y876 increases GluA2 binding to GRIP1. These results demonstrate that AMPAR trafficking during homeostatic upscaling can be gated by a single phosphorylation site on the GluA2 subunit.
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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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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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Beggs, John M. "A Statistical Theory of Long-Term Potentiation and Depression." Neural Computation 13, no. 1 (January 1, 2001): 87–111. http://dx.doi.org/10.1162/089976601300014646.
Full textAbstract:
The synaptic phenomena of long-term potentiation (LTP) and long-term depression (LTD) have been intensively studied for over twenty-five years. Although many diverse aspects of these forms of plasticity have been observed, no single theory has offered a unifying explanation for them. Here, a statistical “bin” model is proposed to account for a variety of features observed in LTP and LTD experiments performed with field potentials in mammalian cortical slices. It is hypothesized that long-term synaptic changes will be induced when statistically unlikely conjunctions of pre- and postsynaptic activity occur. This hypothesis implies that finite changes in synaptic strength will be proportional to information transmitted by conjunctions and that excitatory synapses will obey a Hebbian rule (Hebb, 1949). Using only one set of constants, the bin model offers an explanation as to why synaptic strength decreases in a decelerating manner during LTD induction (Mulkey & Malenka, 1992); why the induction protocols for LTP and LTD are asymmetric (Dudek & Bear, 1992; Mulkey & Malenka, 1992); why stimulation over a range of frequencies produces a frequency-response curve similar to that proposed by the BCM theory (Bienenstock, Cooper, & Munro, 1982; Dudek & Bear, 1992); and why this curve would shift as postsynaptic activity is changed (Kirkwood, Rioult, & Bear, 1996). In addition, the bin model offers an alternative to the BCM theory by predicting that changes in postsynaptic activity will produce vertical shifts in the curve rather than merely horizontal shifts.
Books on the topic "Hebburn Ltd"
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Hanley, Beth. Lockout: Weston and its mines, 1928-30. Charlestown, Newcastle, N.S.W: Loani, 1992.
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Slovenia. Republiška uprava za zaščito in reševanje., ed. Organisational concept of the system for protection and rescue in the Republic of Slovenia. Ljubljana: Republic Administration for Protection and Rescue, Ministry of Defence, 1992.
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