Relevant bibliographies by topics / Temporal Hebbian learning

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

Academic literature on the topic 'Temporal Hebbian learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Temporal Hebbian learning"

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Rao, Rajesh P. N., and Terrence J. Sejnowski. "Spike-Timing-Dependent Hebbian Plasticity as Temporal Difference Learning." Neural Computation 13, no. 10 (2001): 2221–37. http://dx.doi.org/10.1162/089976601750541787.

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A spike-timing-dependent Hebbian mechanism governs the plasticity of recurrent excitatory synapses in the neocortex: synapses that are activated a few milliseconds before a postsynaptic spike are potentiated, while those that are activated a few milliseconds after are depressed. We show that such a mechanism can implement a form of temporal difference learning for prediction of input sequences. Using a biophysical model of a cortical neuron, we show that a temporal difference rule used in conjunction with dendritic backpropagating action potentials reproduces the temporally asymmetric window o
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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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Tully, Philip J., Henrik Lindén, Matthias H. Hennig, and Anders Lansner. "Spike-Based Bayesian-Hebbian Learning of Temporal Sequences." PLOS Computational Biology 12, no. 5 (2016): e1004954. http://dx.doi.org/10.1371/journal.pcbi.1004954.

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Girolami, Mark, and Colin Fyfe. "A temporal model of linear anti-Hebbian learning." Neural Processing Letters 4, no. 3 (1996): 139–48. http://dx.doi.org/10.1007/bf00426022.

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Zenke, Friedemann, Wulfram Gerstner, and Surya Ganguli. "The temporal paradox of Hebbian learning and homeostatic plasticity." Current Opinion in Neurobiology 43 (April 2017): 166–76. http://dx.doi.org/10.1016/j.conb.2017.03.015.

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Lee, Yun-Parn. "Multidimensional Hebbian Learning With Temporal Coding in Neocognitron Visual Recognition." IEEE Transactions on Systems, Man, and Cybernetics: Systems 47, no. 12 (2017): 3386–96. http://dx.doi.org/10.1109/tsmc.2016.2599200.

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Kolodziejski, Christoph, Bernd Porr, and Florentin Wörgötter. "On the Asymptotic Equivalence Between Differential Hebbian and Temporal Difference Learning." Neural Computation 21, no. 4 (2009): 1173–202. http://dx.doi.org/10.1162/neco.2008.04-08-750.

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In this theoretical contribution, we provide mathematical proof that two of the most important classes of network learning—correlation-based differential Hebbian learning and reward-based temporal difference learning—are asymptotically equivalent when timing the learning with a modulatory signal. This opens the opportunity to consistently reformulate most of the abstract reinforcement learning framework from a correlation-based perspective more closely related to the biophysics of neurons.
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El-Laithy, Karim, and Martin Bogdan. "A Reinforcement Learning Framework for Spiking Networks with Dynamic Synapses." Computational Intelligence and Neuroscience 2011 (2011): 1–12. http://dx.doi.org/10.1155/2011/869348.

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An integration of both the Hebbian-based and reinforcement learning (RL) rules is presented for dynamic synapses. The proposed framework permits the Hebbian rule to update the hidden synaptic model parameters regulating the synaptic response rather than the synaptic weights. This is performed using both the value and the sign of the temporal difference in the reward signal after each trial. Applying this framework, a spiking network with spike-timing-dependent synapses is tested to learn the exclusive-OR computation on a temporally coded basis. Reward values are calculated with the distance be
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Mitchison, Graeme. "Removing Time Variation with the Anti-Hebbian Differential Synapse." Neural Computation 3, no. 3 (1991): 312–20. http://dx.doi.org/10.1162/neco.1991.3.3.312.

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I describe a local synaptic learning rule that can be used to remove the effects of certain types of systematic temporal variation in the inputs to a unit. According to this rule, changes in synaptic weight result from a conjunction of short-term temporal changes in the inputs and the output. Formally, This is like the differential rule proposed by Klopf (1986) and Kosko (1986), except for a change of sign, which gives it an anti-Hebbian character. By itself this rule is insufficient. A weight conservation condition is needed to prevent the weights from collapsing to zero, and some further con
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Kempter, Richard, Wulfram Gerstner, and J. Leo van Hemmen. "Intrinsic Stabilization of Output Rates by Spike-Based Hebbian Learning." Neural Computation 13, no. 12 (2001): 2709–41. http://dx.doi.org/10.1162/089976601317098501.

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We study analytically a model of long-term synaptic plasticity where synaptic changes are triggered by presynaptic spikes, postsynaptic spikes, and the time differences between presynaptic and postsynaptic spikes. The changes due to correlated input and output spikes are quantified by means of a learning window. We show that plasticity can lead to an intrinsic stabilization of the mean firing rate of the postsynaptic neuron. Subtractive normalization of the synaptic weights (summed over all presynaptic inputs converging on a postsynaptic neuron) follows if, in addition, the mean input rates an
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Dissertations / Theses on the topic "Temporal Hebbian learning"

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Bouchacourt, Flora. "Hebbian mechanisms and temporal contiguity for unsupervised task-set learning." Thesis, Paris 6, 2016. http://www.theses.fr/2016PA066379/document.

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L'homme est capable d'utiliser des stratégies ou règles concurrentes selon les contraintes environnementales. Nous étudions un modèle plausible pour une tâche nécessitant l'apprentissage de plusieurs règles associant des stimuli visuels à des réponses motrices. Deux réseaux de populations neurales à sélectivité mixte interagissent. Le réseau décisionnel apprend les associations stimulus-réponse une à une, mais ne peut gérer qu'une règle à la fois. Son activité modifie la plasticité synaptique du second réseau qui apprend les statistiques d'évènements sur une échelle de temps plus longue. Lorsq
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Tully, Philip. "Spike-Based Bayesian-Hebbian Learning in Cortical and Subcortical Microcircuits." Doctoral thesis, KTH, Beräkningsvetenskap och beräkningsteknik (CST), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-205568.

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Cortical and subcortical microcircuits are continuously modified throughout life. Despite ongoing changes these networks stubbornly maintain their functions, which persist although destabilizing synaptic and nonsynaptic mechanisms should ostensibly propel them towards runaway excitation or quiescence. What dynamical phenomena exist to act together to balance such learning with information processing? What types of activity patterns do they underpin, and how do these patterns relate to our perceptual experiences? What enables learning and memory operations to occur despite such massive and cons
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Barreto, Guilherme de Alencar. "Redes neurais não-supervisionadas para processamento de sequências temporais." Universidade de São Paulo, 1998. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-25112015-111953/.

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Em muitos domínios de aplicação, a variável tempo é uma dimensão essencial. Este é o caso da robótica, na qual trajetórias de robôs podem ser interpretadas como seqüências temporais cuja ordem de ocorrência de suas componentes precisa ser considerada. Nesta dissertação, desenvolve-se um modelo de rede neural não-supervisionada para aprendizagem e reprodução de trajetórias do Robô PUMA 560. Estas trajetórias podem ter estados em comum, o que torna o processo de reprodução susceptível a ambigüidades. O modelo proposto consiste em uma rede competitiva composta por dois conjuntos de pesos sináptic
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Bofill, Petit Adria. "An analogue VLSI study of temporally-asymmetric Hebbian learning." Thesis, University of Edinburgh, 2005. http://hdl.handle.net/1842/15136.

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The primary aim of this thesis is to examine whether temporally asymmetric Hebbian learning is analogue VLSI can support temporal correlation learning and spike-synchrony processing. Novel circuits for synapses with spike-timing-dependent plasticity (STDP) are proposed. Results from several learning experiments conducted with a chip containing a small feed-forward network of neurons with STDP synapses are presented. The learning circuits proposed in this thesis can be used to implement weight-independent STDP and learning rules with weight-dependent potentiation. Test results show that the lea
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Kolodziejski, Christoph Markus. "Mathematical Description of Differential Hebbian Plasticity and its Relation to Reinforcement Learning." Doctoral thesis, 2009. http://hdl.handle.net/11858/00-1735-0000-000D-F171-5.

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Book chapters on the topic "Temporal Hebbian learning"

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Girolami, Mark. "Temporal Anti-Hebbian Learning." In Perspectives in Neural Computing. Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0825-2_8.

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Treur, Jan, and Muhammad Umair. "Rationality for Temporal Discounting, Memory Traces and Hebbian Learning." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30732-4_7.

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Ruf, Berthold, and Michael Schmitt. "Hebbian learning in networks of spiking neurons using temporal coding." In Biological and Artificial Computation: From Neuroscience to Technology. Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/bfb0032496.

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van Hemmen, J. L., and R. Kempter. "Hebbian Learning of Temporal Correlations: Sound Localization in the Barn Owl Auditory System." In Dynamical Networks in Physics and Biology. Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/978-3-662-03524-5_24.

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Porr, Bernd, and Florentin Wörgötter. "Temporal Hebbian Learning in Rate-Coded Neural Networks: A Theoretical Approach towards Classical Conditioning." In Artificial Neural Networks — ICANN 2001. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-44668-0_155.

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Tsukada, Minoru. "Interaction Between the Spatio-Temporal Learning Rule (Non Hebbian) and Hebbian in Single Cells: A Cellular Mechanism of Reinforcement Learning." In Reinforcement Learning. I-Tech Education and Publishing, 2008. http://dx.doi.org/10.5772/5277.

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Conference papers on the topic "Temporal Hebbian learning"

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Lo, James Ting-Ho. "Unsupervised Hebbian learning by recurrent multilayer neural networks for temporal hierarchical pattern recognition." In 2010 44th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2010. http://dx.doi.org/10.1109/ciss.2010.5464925.

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Li, Guoshi, Stacy Cheng, Frank Ko, Scott L. Raunch, Gregory Quirk, and Satish S. Nair. "Computational Modeling of Lateral Amygdala Neurons During Acquisition and Extinction of Conditioned Fear, Using Hebbian Learning." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-15078.

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The amygdaloid complex located within the medial temporal lobe plays an important role in the acquisition and expression of learned fear associations (Quirk et al. 2003) and contains three main components: the lateral nucleus (LA), the basal nucleus (BLA), and the central nucleus (CE) (Faber and Sah, 2002). The lateral nucleus of the amygdala (LA) is widely accepted to be a key site of plastic synaptic events that contributes to fear learning (Pare, Quirk, LeDoux, 2004). There are two main types of neurons within the LA and the BLA: principal pyramidal-like cells which form projection neurons
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Journal articles
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