Relevant bibliographies by topics / Synaptic time dependent plasticity

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Synaptic time dependent plasticity'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Synaptic time dependent plasticity"

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Akil, Alan Eric, Robert Rosenbaum, and Krešimir Josić. "Balanced networks under spike-time dependent plasticity." PLOS Computational Biology 17, no. 5 (2021): e1008958. http://dx.doi.org/10.1371/journal.pcbi.1008958.

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The dynamics of local cortical networks are irregular, but correlated. Dynamic excitatory–inhibitory balance is a plausible mechanism that generates such irregular activity, but it remains unclear how balance is achieved and maintained in plastic neural networks. In particular, it is not fully understood how plasticity induced changes in the network affect balance, and in turn, how correlated, balanced activity impacts learning. How do the dynamics of balanced networks change under different plasticity rules? How does correlated spiking activity in recurrent networks change the evolution of we
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Elliott, Terry. "The Mean Time to Express Synaptic Plasticity in Integrate-and-Express, Stochastic Models of Synaptic Plasticity Induction." Neural Computation 23, no. 1 (2011): 124–59. http://dx.doi.org/10.1162/neco_a_00061.

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Stochastic models of synaptic plasticity propose that single synapses perform a directed random walk of fixed step sizes in synaptic strength, thereby embracing the view that the mechanisms of synaptic plasticity constitute a stochastic dynamical system. However, fluctuations in synaptic strength present a formidable challenge to such an approach. We have previously proposed that single synapses must interpose an integration and filtering mechanism between the induction of synaptic plasticity and the expression of synaptic plasticity in order to control fluctuations. We analyze a class of thre
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Elliott, Terry. "Temporal Dynamics of Rate-Based Synaptic Plasticity Rules in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 20, no. 9 (2008): 2253–307. http://dx.doi.org/10.1162/neco.2008.06-07-555.

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In a recently proposed, stochastic model of spike-timing-dependent plasticity, we derived general expressions for the expected change in synaptic strength, ΔSn, induced by a typical sequence of precisely n spikes. We found that the rules ΔSn, n ≥ 3, exhibit regions of parameter space in which stable, competitive interactions between afferents are present, leading to the activity-dependent segregation of afferents on their targets. The rules ΔSn, however, allow an indefinite period of time to elapse for the occurrence of precisely n spikes, while most measurements of changes in synaptic strengt
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Chechik, Gal. "Spike-Timing-Dependent Plasticity and Relevant Mutual Information Maximization." Neural Computation 15, no. 7 (2003): 1481–510. http://dx.doi.org/10.1162/089976603321891774.

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Synaptic plasticity was recently shown to depend on the relative timing of the pre- and postsynaptic spikes. This article analytically derives a spike-dependent learning rule based on the principle of information maximization for a single neuron with spiking inputs. This rule is then transformed into a biologically feasible rule, which is compared to the experimentally observed plasticity. This comparison reveals that the biological rule increases information to a near-optimal level and provides insights into the structure of biological plasticity. It shows that the time dependency of synaptic
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Billings, Guy, and Mark C. W. van Rossum. "Memory Retention and Spike-Timing-Dependent Plasticity." Journal of Neurophysiology 101, no. 6 (2009): 2775–88. http://dx.doi.org/10.1152/jn.91007.2008.

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Memory systems should be plastic to allow for learning; however, they should also retain earlier memories. Here we explore how synaptic weights and memories are retained in models of single neurons and networks equipped with spike-timing-dependent plasticity. We show that for single neuron models, the precise learning rule has a strong effect on the memory retention time. In particular, a soft-bound, weight-dependent learning rule has a very short retention time as compared with a learning rule that is independent of the synaptic weights. Next, we explore how the retention time is reflected in
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Lobov, Sergey A., Ekaterina S. Berdnikova, Alexey I. Zharinov, Dmitry P. Kurganov, and Victor B. Kazantsev. "STDP-Driven Rewiring in Spiking Neural Networks under Stimulus-Induced and Spontaneous Activity." Biomimetics 8, no. 3 (2023): 320. http://dx.doi.org/10.3390/biomimetics8030320.

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Mathematical and computer simulation of learning in living neural networks have typically focused on changes in the efficiency of synaptic connections represented by synaptic weights in the models. Synaptic plasticity is believed to be the cellular basis for learning and memory. In spiking neural networks composed of dynamical spiking units, a biologically relevant learning rule is based on the so-called spike-timing-dependent plasticity or STDP. However, experimental data suggest that synaptic plasticity is only a part of brain circuit plasticity, which also includes homeostatic and structura
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Shouval, Harel Z., and Georgios Kalantzis. "Stochastic Properties of Synaptic Transmission Affect the Shape of Spike Time–Dependent Plasticity Curves." Journal of Neurophysiology 93, no. 2 (2005): 1069–73. http://dx.doi.org/10.1152/jn.00504.2004.

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Theoretical studies have shown that calcium influx through N-methyl-d-aspartate (NMDA) receptors is a sufficient signal to account for various induction protocols of bidirectional synaptic plasticity, including spike time–dependent plasticity (STDP). The STDP curves obtained by these different models exhibits a form of spike time–dependent long-term depression that occurs when a presynaptic spike precedes the postsynaptic spike (pre-post LTD). We have previously proposed that this novel form of LTD can serve as an experimental test for the validity of these models. These calcium based theoreti
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Chauhan, Kanishk, Alexander B. Neiman, and Peter A. Tass. "Synaptic reorganization of synchronized neuronal networks with synaptic weight and structural plasticity." PLOS Computational Biology 20, no. 7 (2024): e1012261. http://dx.doi.org/10.1371/journal.pcbi.1012261.

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Abnormally strong neural synchronization may impair brain function, as observed in several brain disorders. We computationally study how neuronal dynamics, synaptic weights, and network structure co-emerge, in particular, during (de)synchronization processes and how they are affected by external perturbation. To investigate the impact of different types of plasticity mechanisms, we combine a network of excitatory integrate-and-fire neurons with different synaptic weight and/or structural plasticity mechanisms: (i) only spike-timing-dependent plasticity (STDP), (ii) only homeostatic structural
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Muñoz, Pablo, Carolina Estay, Paula Díaz, Claudio Elgueta, Álvaro O. Ardiles, and Pablo A. Lizana. "Inhibition of DNA Methylation Impairs Synaptic Plasticity during an Early Time Window in Rats." Neural Plasticity 2016 (2016): 1–13. http://dx.doi.org/10.1155/2016/4783836.

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Although the importance of DNA methylation-dependent gene expression to neuronal plasticity is well established, the dynamics of methylation and demethylation during the induction and expression of synaptic plasticity have not been explored. Here, we combined electrophysiological, pharmacological, molecular, and immunohistochemical approaches to examine the contribution of DNA methylation and the phosphorylation of Methyl-CpG-binding protein 2 (MeCP2) to synaptic plasticity. We found that, at twenty minutes after theta burst stimulation (TBS), the DNA methylation inhibitor 5-aza-2-deoxycytidin
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Masuda, Naoki, and Kazuyuki Aihara. "Self-Organizing Dual Coding Based on Spike-Time-Dependent Plasticity." Neural Computation 16, no. 3 (2004): 627–63. http://dx.doi.org/10.1162/089976604772744938.

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It has been a matter of debate how firing rates or spatiotemporal spike patterns carry information in the brain. Recent experimental and theoretical work in part showed that these codes, especially a population rate code and a synchronous code, can be dually used in a single architecture. However, we are not yet able to relate the role of firing rates and synchrony to the spatiotemporal structure of inputs and the architecture of neural networks. In this article, we examine how feedforward neural networks encode multiple input sources in the firing patterns. We apply spike-time-dependent plast
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Dissertations / Theses on the topic "Synaptic time dependent plasticity"

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Enge, Sören, Monika Fleischhauer, Anne Gärtner, et al. "Brain-Derived Neurotrophic Factor (Val66Met) and Serotonin Transporter (5-HTTLPR) Polymorphisms Modulate Plasticity in Inhibitory Control Performance Over Time but Independent of Inhibitory Control Training." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-221795.

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Several studies reported training-induced improvements in executive function tasks and also observed transfer to untrained tasks. However, the results are mixed and there is a large interindividual variability within and across studies. Given that training-related performance changes would require modification, growth or differentiation at the cellular and synaptic level in the brain, research on critical moderators of brain plasticity potentially explaining such changes is needed. In the present study, a pre-post-follow-up design (N = 122) and a 3-weeks training of two response inhibition tas
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Enge, Sören, Monika Fleischhauer, Anne Gärtner, et al. "Brain-Derived Neurotrophic Factor (Val66Met) and Serotonin Transporter (5-HTTLPR) Polymorphisms Modulate Plasticity in Inhibitory Control Performance Over Time but Independent of Inhibitory Control Training." Frontiers Research Foundation, 2016. https://tud.qucosa.de/id/qucosa%3A30228.

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Several studies reported training-induced improvements in executive function tasks and also observed transfer to untrained tasks. However, the results are mixed and there is a large interindividual variability within and across studies. Given that training-related performance changes would require modification, growth or differentiation at the cellular and synaptic level in the brain, research on critical moderators of brain plasticity potentially explaining such changes is needed. In the present study, a pre-post-follow-up design (N = 122) and a 3-weeks training of two response inhibition tas
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Coquinco, Ainsley. "Activity dependent synaptic plasticity and microfluidics." Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/40663.

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The visual cortex of the brain is one of the fundamental preparations to study critical periods and activity dependent changes in the brain. During development, when sensory input from one eye is prevented, visual acuity and brain connectivity is lost in favour of inputs from the active eye. Because of the brain’s complexity, it is difficult to perform thorough analyses of synaptic mechanisms that exist during development. Therefore, the development of simpler in vitro models would be advantageous. In our studies, we used a 3-compartment microfluidic device and created a new model for dual inp
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Billings, Guy. "Memory stability and synaptic plasticity." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3853.

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Numerous experiments have demonstrated that the activity of neurons can alter the strength of excitatory synapses. This synaptic plasticity is bidirectional and synapses can be strengthened (potentiation) or weakened (depression). Synaptic plasticity offers a mechanism that links the ongoing activity of the brain with persistent physical changes to its structure. For this reason it is widely believed that synaptic plasticity mediates learning and memory. The hypothesis that synapses store memories by modifying their strengths raises an important issue. There should be a balance between the nec
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Yeung, Luk Chong. "A mechanistic model of calcium-dependent synaptic plasticity /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174704.

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Arnold, F. J. L. "An experimental model of transcription-dependent synaptic plasticity." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.596164.

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A cell culture system was developed to study synaptic plasticity. Electrical activity across cultured hippocampal networks was monitored using an array of 60 microelectrodes (MEA). These networks demonstrated spontaneous low rate asynchronous electrical activity. the GABA-A receptor antagonist bicuculline (50μM) transformed this activity into synchronous repetitive bursts of action potentials across the network, accompanied by NMDA receptor-dependent sustained global calcium transients. A 15 minute bicuculline exposure induced a long-lasting increase in synaptic efficacy, and a dramatic change
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Lee, Bo Young. "Signaling events in activity dependent neuroprotection, neurodegeneration, and synaptic plasticity." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180458484.

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Monzon, Joshua Jen C. "Analog VLSI circuit design of spike-timing-dependent synaptic plasticity." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/54636.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (p. 61-63).<br>Synaptic plasticity is the ability of a synaptic connection to change in strength and is believed to be the basis for learning and memory. Currently, two types of synaptic plasticity exist. First is the spike-timing-dependent-plasticity (STDP), a timing-based protocol that suggests that the efficacy of synaptic connections is modulated by the relative timing between presynaptic and pos
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Birtoli, Barbara. "Firing mode dependent synaptic plasticity in rat neocortical pyramidal neurons /." Bern : [s.n.], 2004. http://www.ub.unibe.ch/content/bibliotheken_sammlungen/sondersammlungen/dissen_bestellformular/index_ger.html.

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Heidari, Mohajerani Majid. "Activity-dependent synaptic plasticity processes in the immature rat hippocampus." Doctoral thesis, SISSA, 2007. http://hdl.handle.net/20.500.11767/4747.

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Books on the topic "Synaptic time dependent plasticity"

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Myoga, Michael Hideki. Calcium-Dependent Synaptic Plasticity in the Cerebellum. 2011.

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Nakic, Marina. Extracellular matrix glycoprotein tenascin-C in activity-dependent synaptic plasticity. 1997.

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Tropea, Daniela, Abhishek Banerjee, and Annalisa Scimemi, eds. Biology of Brain Disorders – Cellular substrates for disrupted synaptic function and experience-dependent plasticity. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-83250-072-9.

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Studies on synaptic plasticity and memory in calcium/calmodulin-dependent protein kinase II mutant mice. 1996.

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Morris, R. G. M. Elements of a Neurobiological Theory of the Hippocampus: The Role of Activity-dependent Synaptic Plasticity in Memory. Amsterdam University Press, 2005.

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Experimental evaluation criteria for constitutive models of time dependent cyclic plasticity: Final report. College of Engineering, Michigan State University, 1986.

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Vorel, Stanislav R., and Sarah H. Lisanby. Therapeutic potential of TMS-induced plasticity in the prefrontal cortex. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0038.

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This article discusses synaptic plasticity as a potential mechanism of enduring changes in function observed after relatively brief periods of repetitive (r)TMS. Plasticity is a use dependent enduring change in neural structure and function. The characteristics of plasticity are described in this article. Taking into account, the interactions between rTMS and pharmacological manipulations, this article explores how principles of synaptic plasticity may be exploited in the rational design of future rTMS paradigms in psychiatric disorders like major depressive disorder, obsessive-compulsive diso
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Buetefisch, Cathrin M., and Leonardo G. Cohen. Use-dependent changes in TMS measures. Edited by Charles M. Epstein, Eric M. Wassermann, and Ulf Ziemann. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780198568926.013.0018.

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Adult brains maintain the ability to reorganize throughout life. Cortical reorganization or plasticity includes modification of synaptic efficacy as well as neuronal networks that carry behavioural implications. Transcranial magnetic stimulation (TMS) allows for the study of primary motor cortex reorganization in humans. Motor-evoked potential (MEP) amplitudes change in response to practice. This article gives information about the effect of practice on TMS measures such as motor-evoked potential amplitudes, motor maps, paired-pulse measures, and behavioural measures. These changes may be acco
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Nutt, David J., and Liam J. Nestor. The glutamate system and addiction. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198797746.003.0009.

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Glutamate is the primary excitatory neurotransmitter in the brain. Glutamate is involved in synaptic plasticity, particularly within dopamine systems of the brain that are involved in reward. Glutamate-dependent plasticity is involved in the development of substance addiction through its actions at NMDA receptors during long-term potentiation (LTP) related learning and memory processes. This plasticity within brain circuitry involved in learning and memory is sustained during substance abstinence and may provide a neural substrate for a vulnerability to addiction relapse. Medications that poss
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Koch, Christof. Biophysics of Computation. Oxford University Press, 1998. http://dx.doi.org/10.1093/oso/9780195104912.001.0001.

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Neural network research often builds on the fiction that neurons are simple linear threshold units, completely neglecting the highly dynamic and complex nature of synapses, dendrites, and voltage-dependent ionic currents. Biophysics of Computation: Information Processing in Single Neurons challenges this notion, using richly detailed experimental and theoretical findings from cellular biophysics to explain the repertoire of computational functions available to single neurons. The author shows how individual nerve cells can multiply, integrate, or delay synaptic inputs and how information can b
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Book chapters on the topic "Synaptic time dependent plasticity"

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Balena, Trevor, Brooke A. Acton, and Melanie A. Woodin. "Activity-Dependent Inhibitory Synaptic Plasticity Mediated by Chloride Regulation." In Inhibitory Synaptic Plasticity. Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-6978-1_10.

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Hanse, Eric, and Arthur Konnerth. "Calcium and Activity-Dependent Synaptic Plasticity." In Integrative Aspects of Calcium Signalling. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4899-1901-4_16.

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Atwood, Brady K., and David M. Lovinger. "Endocannabinoid-Dependent Synaptic Plasticity in the Striatum." In Endocannabinoids and Lipid Mediators in Brain Functions. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57371-7_5.

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Gerstner, Wulfram. "Biological Learning: Synaptic Plasticity, Hebb Rule and Spike Timing Dependent Plasticity." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_80.

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Plattner, Florian, K. Peter Giese, and Marco Angelo. "Involvement of Cdk5 in Synaptic Plasticity, and Learning and Memory." In Cyclin Dependent Kinase 5 (Cdk5). Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78887-6_16.

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Fischer, Andre, and Li-Huei Tsai. "Cyclin-Dependent Kinase 5 (Cdk5): Linking Synaptic Plasticity and Neurodegeneration." In Cyclin Dependent Kinase 5 (Cdk5). Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-78887-6_17.

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Johnson, Melissa, and Sylvain Chartier. "Model Derived Spike Time Dependent Plasticity." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68600-4_40.

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Saggau, P., and G. ten Bruggencate. "Topology Related Real-Time Monitoring of Neural Activity in Hippocampal Brain Slices by Noninvasive Optical Recording — A Step Towards Functional Aspects of Long-Term Potentiation (LTP)." In Synaptic Plasticity in the Hippocampus. Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73202-7_46.

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Baranyi, Attila, and Magdolna B. Szente. "Postsynaptic Activity-Dependent Facilitation of Excitatory Synaptic Transmission in the Neocortex." In Cellular Mechanisms of Conditioning and Behavioral Plasticity. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4757-9610-0_36.

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Holtmaat, Antony, V. De Paola, L. Wilbrecht, and G. Knott. "Imaging of Experience-Dependent Structural Plasticity in the Mouse Neocortex in vivo." In Synaptic Plasticity and the Mechanism of Alzheimer's Disease. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-76330-7_3.

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Conference papers on the topic "Synaptic time dependent plasticity"

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Qiu, Zhen, Bowen Gu, Chenxu Wei, Yang Gu, Qinan Wang, and Chun Zhao. "CMOS Based Spiking-Time Dependent Plasticity Circuit and Simple Image Classification." In 2024 IEEE International Conference on IC Design and Technology (ICICDT). IEEE, 2024. http://dx.doi.org/10.1109/icicdt63592.2024.10717854.

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Maraj, Joshua J., Jessie D. Ringley, and Stephen A. Sarles. "Voltage-Dependent Medium-Term Synaptic Plasticity in Biomolecular Synapses." In ASME 2021 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/smasis2021-67304.

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Abstract Biomolecular assemblies of phospholipids and the pore forming species monazomycin were constructed using the droplet interface bilayer technique to mimic synaptic properties. Electrical characterization of the interface confirms the existence of multiple forms of short-term synaptic plasticity in response to constant stimuli. Memory of prior stimulation can last up to 20 minutes suggesting an unaccounted mechanism of longer term memory retention, termed “medium-term synaptic plasticity.” Monazomycin doped biomembranes were stimulated by a series of step voltage inputs with varying off
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Lima, Renan Araújo de, José Wanderson Oliveira Silva, and Luiz Marcos Garcia Gonçalves. "Towards a Computer Vision System for Monitoring and Stimulation of Rodents." In Anais Estendidos da Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2022. http://dx.doi.org/10.5753/sibgrapi.est.2022.23265.

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The Behavioral Neurophysiology research area investigates the electrophysiological correlates of behaviors, normally using animals such as rodents as subjects. Examples of studies in the field include the investigation of neural processing dysfunctions and synaptic plasticity in animal models of autism and changes in synaptic plasticity in animal models of epilepsy. This area is in constant need of new equipment to aid research. With this goal, this work aims to develop a system capable of receiving video information in real-time using computer vision algorithms to define the positioning of th
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Yicong Meng, Kuan Zhou, J. J. C. Monzon, and Chi-Sang Poon. "Iono-neuromorphic implementation of spike-timing-dependent synaptic plasticity." In 2011 33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2011. http://dx.doi.org/10.1109/iembs.2011.6091838.

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Rush, Andrew J., and Rashmi Jha. "NbOx synaptic devices for spike frequency dependent plasticity learning." In 2017 75th Device Research Conference (DRC). IEEE, 2017. http://dx.doi.org/10.1109/drc.2017.7999430.

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"PULSE-TYPE NEURO DEVICES WITH SPIKE TIMING DEPENDENT SYNAPTIC PLASTICITY." In International Conference on Biomedical Electronics and Devices. SciTePress - Science and and Technology Publications, 2008. http://dx.doi.org/10.5220/0001051702640268.

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Daddinounou, Salah, and Elena Ioana Vatajelu. "Synaptic Control for Hardware Implementation of Spike Timing Dependent Plasticity." In 2022 25th International Symposium on Design and Diagnostics of Electronic Circuits and Systems (DDECS). IEEE, 2022. http://dx.doi.org/10.1109/ddecs54261.2022.9770171.

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Ikeda, Narumitsu. "A Synaptically Local Spike-Rate-Dependent Plasticity toward Neuromorphic Computing." In 31st International Conference on Neural Information Processing. Tuwhera, 2025. https://doi.org/10.24135/iconip8.

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A synaptically local spike-rate-dependent learning rule is proposed in this work. Using mathematical analysis and a numerical simulation, we demonstrate that the synaptic weights weaken/strengthen with the proposed method when the presynaptic firing rate is low/high. Such changes correspond to the rate-dependent synaptic plasticity observed in actual neural circuits. We also present that the proposed method enables learning in a classical conditioning manner. As synaptically local spike-rate-dependent plasticity, our proposed method is expected to contribute to training spiking neural networks
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Belhadj, Bilel, Jean Tomas, Olivia Malot, Gilles N'Kaoua, Yannick Bornat, and Sylvie Renaud. "FPGA-based architecture for real-time synaptic plasticity computation." In 2008 15th IEEE International Conference on Electronics, Circuits and Systems - (ICECS 2008). IEEE, 2008. http://dx.doi.org/10.1109/icecs.2008.4674799.

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"Dynamic Data-based Modelling of Synaptic Plasticity: mGluR-dependent Long-term Depression." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004231100480053.

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Reports on the topic "Synaptic time dependent plasticity"

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Pasupuleti, Murali Krishna. Neural Computation and Learning Theory: Expressivity, Dynamics, and Biologically Inspired AI. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv425.

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Abstract: Neural computation and learning theory provide the foundational principles for understanding how artificial and biological neural networks encode, process, and learn from data. This research explores expressivity, computational dynamics, and biologically inspired AI, focusing on theoretical expressivity limits, infinite-width neural networks, recurrent and spiking neural networks, attractor models, and synaptic plasticity. The study investigates mathematical models of function approximation, kernel methods, dynamical systems, and stability properties to assess the generalization capa
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Wang, Felix, Nick Alonso, and Corinne Teeter. Combining Spike Time Dependent Plasticity (STDP) and Backpropagation (BP) for Robust and Data Efficient Spiking Neural Networks (SNN). Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1902866.

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Oliynyk, Kateryna, and Matteo Ciantia. Application of a finite deformation multiplicative plasticity model with non-local hardening to the simulation of CPTu tests in a structured soil. University of Dundee, 2021. http://dx.doi.org/10.20933/100001230.

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In this paper an isotropic hardening elastoplastic constitutive model for structured soils is applied to the simulation of a standard CPTu test in a saturated soft structured clay. To allow for the extreme deformations experienced by the soil during the penetration process, the model is formulated in a fully geometric non-linear setting, based on: i) the multiplicative decomposition of the deformation gradient into an elastic and a plastic part; and, ii) on the existence of a free energy function to define the elastic behaviour of the soil. The model is equipped with two bonding-related intern
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