Spike-Timing-Dependent Plasticity and Short-Term Plasticity Jointly Control the Excitation of Hebbian Plasticity without Weight Constraints in Neural Networks
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-12-30
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Fernando, Subha& Yamada, Koichi. 2012. 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،Vol. 2012, no. 2012, pp.1-15.
https://search.emarefa.net/detail/BIM-512192
Modern Language Association (MLA)
Fernando, Subha& Yamada, Koichi. 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 No. 2012 (2012), pp.1-15.
https://search.emarefa.net/detail/BIM-512192
American Medical Association (AMA)
Fernando, Subha& Yamada, Koichi. 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. Vol. 2012, no. 2012, pp.1-15.
https://search.emarefa.net/detail/BIM-512192
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-512192