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Journal articles on the topic 'Synaptic time dependent plasticity'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

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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2

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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9

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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11

Lynch, Sebastian Thomas, and Stephen Lynch. "Hysteresis in Neuron Models with Adapting Feedback Synapses." AppliedMath 5, no. 2 (2025): 70. https://doi.org/10.3390/appliedmath5020070.

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Despite its significance, hysteresis remains underrepresented in mainstream models of plasticity. In this work, we propose a novel framework that explicitly models hysteresis in simple one- and two-neuron models. Our models capture key feedback-dependent phenomena such as bistability, multistability, periodicity, quasi-periodicity, and chaos, offering a more accurate and general representation of neural adaptation. This opens the door to new insights in computational neuroscience and neuromorphic system design. Synaptic weights change in several contexts or mechanisms including, Bienenstock–Co

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Haas, Julie S., Thomas Nowotny, and H. D. I. Abarbanel. "Spike-Timing-Dependent Plasticity of Inhibitory Synapses in the Entorhinal Cortex." Journal of Neurophysiology 96, no. 6 (2006): 3305–13. http://dx.doi.org/10.1152/jn.00551.2006.

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Actions of inhibitory interneurons organize and modulate many neuronal processes, yet the mechanisms and consequences of plasticity of inhibitory synapses remain poorly understood. We report on spike-timing-dependent plasticity of inhibitory synapses in the entorhinal cortex. After pairing presynaptic stimulations at time tpre with evoked postsynaptic spikes at time tpost under pharmacological blockade of excitation we found, via whole cell recordings, an asymmetrical timing rule for plasticity of the remaining inhibitory responses. Strength of response varied as a function of the time interva

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Tambuyzer, Tim, Tariq Ahmed, C. James Taylor, Daniel Berckmans, Detlef Balschun, and Jean-Marie Aerts. "System Identification of mGluR-Dependent Long-Term Depression." Neural Computation 25, no. 3 (2013): 650–70. http://dx.doi.org/10.1162/neco_a_00408.

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Recent advances have started to uncover the underlying mechanisms of metabotropic glutamate receptor (mGluR)–dependent long-term depression (LTD). However, it is not completely clear how these mechanisms are linked, and it is believed that several crucial mechanisms remain to be revealed. In this study, we investigated whether system identification (SI) methods can be used to gain insight into the mechanisms of synaptic plasticity. SI methods have been shown to be an objective and powerful approach for describing how sensory neurons encode information about stimuli. However, to our knowledge,

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Tian, Qiaoling, Xiaoting Chen, Xiaoning Zhao, et al. "Temperature-modulated switching behaviors of diffusive memristor for biorealistic emulation of synaptic plasticity." Applied Physics Letters 122, no. 15 (2023): 153502. http://dx.doi.org/10.1063/5.0142742.

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Temperature is known as an important factor in biological synaptic transmission. In this study, temperature-modulated switching behaviors are reported in an amorphous carbon (a-C) diffusive memristor device to emulate biorealistic synaptic plasticity. The devices exhibit memory switching and threshold switching behaviors depending on the compliance current and ambient temperature. As confirmed by conducting atomic force microscopy, the thermal effect can promote the electrochemical formation of a stable metallic conductive filament. A series of timing-controlled pulse experiments are carried o

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15

Baras, Dorit, and Ron Meir. "Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule." Neural Computation 19, no. 8 (2007): 2245–79. http://dx.doi.org/10.1162/neco.2007.19.8.2245.

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Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is influenced by an environmental signal, termed a reward, that directs the changes in appropriate directions. We apply a recently introduced policy learning algorithm from machine learning to networks of spiking neurons and derive a spike-time-dependent plasticity rule that ensures convergence to a local optimum of the expected average reward. The approach is applicable to a broad class of neuronal models, including the

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16

Di Paolo, Ezequiel. "Spike-Timing Dependent Plasticity for Evolved Robots." Adaptive Behavior 10, no. 3-4 (2002): 243–63. http://dx.doi.org/10.1177/1059712302919993006.

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Plastic spiking neural networks are synthesized for phototactic robots using evolutionary techniques. Synaptic plasticity asymmetrically depends on the precise relative timing between presynaptic and postsynaptic spikes at the millisecond range and on longer-term activity-dependent regulatory scaling. Comparative studies have been carried out for different kinds of plastic neural networks with low and high levels of neural noise. In all cases, the evolved controllers are highly robust against internal synaptic decay and other perturbations. The importance of the precise timing of spikes is dem

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17

Lu, Hui, Hyungju Park, and Mu-Ming Poo. "Spike-timing-dependent BDNF secretion and synaptic plasticity." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1633 (2014): 20130132. http://dx.doi.org/10.1098/rstb.2013.0132.

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In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimic

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18

Wang, Lu, Jiachu Xie, Wantao Su, Zhenjie Du, and Mingzhu Zhang. "Scindapsus Aureus Resistive Random-Access Memory with Synaptic Plasticity and Sound Localization Function." Nanomaterials 15, no. 9 (2025): 659. https://doi.org/10.3390/nano15090659.

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This work presents a memristive device based on a composite of Scindapsus aureus (SA) and gold nanoparticles (Au NPs), which exhibits excellent resistive switching characteristics and supports multiple forms of synaptic plasticity such as paired-pulse facilitation (PPF), spike-rate-dependent plasticity (SRDP), and spike-timing-dependent plasticity (STDP). The device demonstrates reliable retention, reproducibility, and switching stability. The SA:Au NP composite originates from a natural plant source and possesses green, biodegradable, and biocompatible features, highlighting its potential as

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Echeveste, Rodrigo, and Claudius Gros. "Two-Trace Model for Spike-Timing-Dependent Synaptic Plasticity." Neural Computation 27, no. 3 (2015): 672–98. http://dx.doi.org/10.1162/neco_a_00707.

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We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the [Formula: see text] concentration in the dendritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, thus constituting a practical tool for the study of the interplay of neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre- and postsynaptic spikes, the standard pairwise S

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Friend, Lindsey, Ryan Williamson, Collin Merrill, et al. "Hippocampal Stratum Oriens Somatostatin-Positive Cells Undergo CB1-Dependent Long-Term Potentiation and Express Endocannabinoid Biosynthetic Enzymes." Molecules 24, no. 7 (2019): 1306. http://dx.doi.org/10.3390/molecules24071306.

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The hippocampus is thought to encode information by altering synaptic strength via synaptic plasticity. Some forms of synaptic plasticity are induced by lipid-based endocannabinoid signaling molecules that act on cannabinoid receptors (CB1). Endocannabinoids modulate synaptic plasticity of hippocampal pyramidal cells and stratum radiatum interneurons; however, the role of endocannabinoids in mediating synaptic plasticity of stratum oriens interneurons is unclear. These feedback inhibitory interneurons exhibit presynaptic long-term potentiation (LTP), but the exact mechanism is not entirely und

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Madeira, Natália, Ana Drumond, and Rosalina Fonseca. "Temporal Gating of Synaptic Competition in the Amygdala by Cannabinoid Receptor Activation." Cerebral Cortex 30, no. 7 (2020): 4064–75. http://dx.doi.org/10.1093/cercor/bhaa026.

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Abstract The acquisition of fear memories involves plasticity of the thalamic and cortical pathways to the lateral amygdala (LA). In turn, the maintenance of synaptic plasticity requires the interplay between input-specific synaptic tags and the allocation of plasticity-related proteins. Based on this interplay, weakly activated synapses can express long-lasting forms of synaptic plasticity by cooperating with strongly activated synapses. Increasing the number of activated synapses can shift cooperation to competition. Synaptic cooperation and competition can determine whether two events, sepa

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22

Jegminat, Jannes, Simone Carlo Surace, and Jean-Pascal Pfister. "Learning as filtering: Implications for spike-based plasticity." PLOS Computational Biology 18, no. 2 (2022): e1009721. http://dx.doi.org/10.1371/journal.pcbi.1009721.

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Most normative models in computational neuroscience describe the task of learning as the optimisation of a cost function with respect to a set of parameters. However, learning as optimisation fails to account for a time-varying environment during the learning process and the resulting point estimate in parameter space does not account for uncertainty. Here, we frame learning as filtering, i.e., a principled method for including time and parameter uncertainty. We derive the filtering-based learning rule for a spiking neuronal network—the Synaptic Filter—and show its computational and biological

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Madadi Asl, Mojtaba, Alireza Valizadeh, and Peter A. Tass. "Decoupling of interacting neuronal populations by time-shifted stimulation through spike-timing-dependent plasticity." PLOS Computational Biology 19, no. 2 (2023): e1010853. http://dx.doi.org/10.1371/journal.pcbi.1010853.

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The synaptic organization of the brain is constantly modified by activity-dependent synaptic plasticity. In several neurological disorders, abnormal neuronal activity and pathological synaptic connectivity may significantly impair normal brain function. Reorganization of neuronal circuits by therapeutic stimulation has the potential to restore normal brain dynamics. Increasing evidence suggests that the temporal stimulation pattern crucially determines the long-lasting therapeutic effects of stimulation. Here, we tested whether a specific pattern of brain stimulation can enable the suppression

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Croft, Wayne, Katharine L. Dobson, and Tomas C. Bellamy. "Plasticity of Neuron-Glial Transmission: Equipping Glia for Long-Term Integration of Network Activity." Neural Plasticity 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/765792.

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The capacity of synaptic networks to express activity-dependent changes in strength and connectivity is essential for learning and memory processes. In recent years, glial cells (most notably astrocytes) have been recognized as active participants in the modulation of synaptic transmission and synaptic plasticity, implicating these electrically nonexcitable cells in information processing in the brain. While the concept of bidirectional communication between neurons and glia and the mechanisms by which gliotransmission can modulate neuronal function are well established, less attention has bee

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Rubin, Jonathan E., Richard C. Gerkin, Guo-Qiang Bi, and Carson C. Chow. "Calcium Time Course as a Signal for Spike-Timing–Dependent Plasticity." Journal of Neurophysiology 93, no. 5 (2005): 2600–2613. http://dx.doi.org/10.1152/jn.00803.2004.

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Calcium has been proposed as a postsynaptic signal underlying synaptic spike-timing–dependent plasticity (STDP). We examine this hypothesis with computational modeling based on experimental results from hippocampal cultures, some of which are presented here, in which pairs and triplets of pre- and postsynaptic spikes induce potentiation and depression in a temporally asymmetric way. Specifically, we present a set of model biochemical detectors, based on plausible molecular pathways, which make direct use of the time course of the calcium signal to reproduce these experimental STDP results. Our

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Gilson, Matthieu, Moritz Bürck, Anthony N. Burkitt, and J. Leo van Hemmen. "Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity." Neural Computation 24, no. 9 (2012): 2251–79. http://dx.doi.org/10.1162/neco_a_00331.

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Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demons

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Yamasaki, Miwako, and Tomonori Takeuchi. "Locus Coeruleus and Dopamine-Dependent Memory Consolidation." Neural Plasticity 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/8602690.

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Most everyday memories including many episodic-like memories that we may form automatically in the hippocampus (HPC) are forgotten, while some of them are retained for a long time by a memory stabilization process, called initial memory consolidation. Specifically, the retention of everyday memory is enhanced, in humans and animals, when something novel happens shortly before or after the time of encoding. Converging evidence has indicated that dopamine (DA) signaling via D1/D5receptors in HPC is required for persistence of synaptic plasticity and memory, thereby playing an important role in t

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Mochida, Sumiko. "Ca2+/Calmodulin and Presynaptic Short-Term Plasticity." ISRN Neurology 2011 (June 23, 2011): 1–7. http://dx.doi.org/10.5402/2011/919043.

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Synaptic efficacy is remodeled by neuronal firing activity at the presynaptic terminal. Presynaptic activity-dependent changes in transmitter release induce postsynaptic plasticity, including morphological change in spine, gene transcription, and protein synthesis and trafficking. The presynaptic transmitter release is triggered and regulated by Ca2+, which enters through voltage-gated Ca2+ (CaV) channels and diffuses into the presynaptic terminal accompanying action potential firings. Residual Ca2+ is sensed by Ca2+-binding proteins, among other potential actions, it mediates time- and space-

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Stehle, Jörg H., Zhiyuan Sheng, Laura Hausmann, et al. "Exercise-induced Nogo-A influences rodent motor learning in a time-dependent manner." PLOS ONE 16, no. 5 (2021): e0250743. http://dx.doi.org/10.1371/journal.pone.0250743.

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The adult, mature central nervous system (CNS) has limited plasticity. Physical exercising can counteract this limitation by inducing plasticity and fostering processes such as learning, memory consolidation and formation. Little is known about the molecular factors that govern these mechanisms, and how they are connected with exercise. In this study, we used immunohistochemical and behavioral analyses to investigate how running wheel exercise affects expression of the neuronal plasticity-inhibiting protein Nogo-A in the rat cortex, and how it influences motor learning in vivo. Following one w

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Delvendahl, Igor, Katarzyna Kita, and Martin Müller. "Rapid and sustained homeostatic control of presynaptic exocytosis at a central synapse." Proceedings of the National Academy of Sciences 116, no. 47 (2019): 23783–89. http://dx.doi.org/10.1073/pnas.1909675116.

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Animal behavior is remarkably robust despite constant changes in neural activity. Homeostatic plasticity stabilizes central nervous system (CNS) function on time scales of hours to days. If and how CNS function is stabilized on more rapid time scales remains unknown. Here, we discovered that mossy fiber synapses in the mouse cerebellum homeostatically control synaptic efficacy within minutes after pharmacological glutamate receptor impairment. This rapid form of homeostatic plasticity is expressed presynaptically. We show that modulations of readily releasable vesicle pool size and release pro

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Pfister, Jean-Pascal, Taro Toyoizumi, David Barber, and Wulfram Gerstner. "Optimal Spike-Timing-Dependent Plasticity for Precise Action Potential Firing in Supervised Learning." Neural Computation 18, no. 6 (2006): 1318–48. http://dx.doi.org/10.1162/neco.2006.18.6.1318.

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In timing-based neural codes, neurons have to emit action potentials at precise moments in time. We use a supervised learning paradigm to derive a synaptic update rule that optimizes by gradient ascent the likelihood of postsynaptic firing at one or several desired firing times. We find that the optimal strategy of up- and downregulating synaptic efficacies depends on the relative timing between presynaptic spike arrival and desired postsynaptic firing. If the presynaptic spike arrives before the desired postsynaptic spike timing, our optimal learning rule predicts that the synapse should beco

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El-Boustani, Sami, Jacque P. K. Ip, Vincent Breton-Provencher, et al. "Locally coordinated synaptic plasticity of visual cortex neurons in vivo." Science 360, no. 6395 (2018): 1349–54. http://dx.doi.org/10.1126/science.aao0862.

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Plasticity of cortical responses in vivo involves activity-dependent changes at synapses, but the manner in which different forms of synaptic plasticity act together to create functional changes in neurons remains unknown. We found that spike timing–induced receptive field plasticity of visual cortex neurons in mice is anchored by increases in the synaptic strength of identified spines. This is accompanied by a decrease in the strength of adjacent spines on a slower time scale. The locally coordinated potentiation and depression of spines involves prominent AMPA receptor redistribution via tar

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Zhang, X., G. Foderaro, C. Henriquez, A. M. J. VanDongen, and S. Ferrari. "A Radial Basis Function Spike Model for Indirect Learning via Integrate-and-Fire Sampling and Reconstruction Techniques." Advances in Artificial Neural Systems 2012 (October 10, 2012): 1–16. http://dx.doi.org/10.1155/2012/713581.

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This paper presents a deterministic and adaptive spike model derived from radial basis functions and a leaky integrate-and-fire sampler developed for training spiking neural networks without direct weight manipulation. Several algorithms have been proposed for training spiking neural networks through biologically-plausible learning mechanisms, such as spike-timing-dependent synaptic plasticity and Hebbian plasticity. These algorithms typically rely on the ability to update the synaptic strengths, or weights, directly, through a weight update rule in which the weight increment can be decided an

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Volmer, Romain, Christine M. A. Prat, Gwendal Le Masson, André Garenne, and Daniel Gonzalez-Dunia. "Borna Disease Virus Infection Impairs Synaptic Plasticity." Journal of Virology 81, no. 16 (2007): 8833–37. http://dx.doi.org/10.1128/jvi.00612-07.

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ABSTRACT The mechanisms whereby Borna disease virus (BDV) can impair neuronal function and lead to neurobehavioral disease are not well understood. To analyze the electrophysiological properties of neurons infected with BDV, we used cultures of neurons grown on multielectrode arrays, allowing a real-time monitoring of the electrical activity across the network shaped by synaptic transmission. Although infection did not affect spontaneous neuronal activity, it selectively blocked activity-dependent enhancement of neuronal network activity, one form of synaptic plasticity thought to be important

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Robinson, Brian S., Theodore W. Berger, and Dong Song. "Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling." Neural Computation 28, no. 11 (2016): 2320–51. http://dx.doi.org/10.1162/neco_a_00883.

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Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel fu

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Linehan, Victoria, Lisa Z. Fang, Matthew P. Parsons, and Michiru Hirasawa. "High-fat diet induces time-dependent synaptic plasticity of the lateral hypothalamus." Molecular Metabolism 36 (June 2020): 100977. http://dx.doi.org/10.1016/j.molmet.2020.100977.

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Iannella, Nicolangelo, and Roman R. Poznanski. "Spatial interactions impact on Ca-driven synaptic plasticity: An ionic cable theory perspective." Journal of Multiscale Neuroscience 3, no. 2 (2024): 160–85. http://dx.doi.org/10.56280/1631287433.

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We extend our previous paper on deriving an approximate analytical solution of a nonlinear cable equation by including other ion channels in neurons and calcium dynamics based on reaction-diffusion dynamics that lead to a system of nonlinear cable equations. Here, excitable dendrite possesses clusters of voltage-activated ion channels that are discretely distributed as point sources or hotspots of transmembrane current along a continuous cable structure of fixed length. Single and/or trains of action potentials and spatially distributed synaptic inputs drive the depolarisation and activate spa

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Sueoka, Brandon, and Feng Zhao. "Memristive synaptic device based on a natural organic material—honey for spiking neural network in biodegradable neuromorphic systems." Journal of Physics D: Applied Physics 55, no. 22 (2022): 225105. http://dx.doi.org/10.1088/1361-6463/ac585b.

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Abstract Spiking neural network (SNN) in future neuromorphic architectures requires hardware devices to be not only capable of emulating fundamental functionalities of biological synapse such as spike-timing dependent plasticity (STDP) and spike-rate dependent plasticity (SRDP), but also biodegradable to address current ecological challenges of electronic waste. Among different device technologies and materials, memristive synaptic devices based on natural organic materials have emerged as the favourable candidate to meet these demands. The metal–insulator-metal structure is analogous to biolo

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Mochida, Sumiko. "Mechanisms of Synaptic Vesicle Exo- and Endocytosis." Biomedicines 10, no. 7 (2022): 1593. http://dx.doi.org/10.3390/biomedicines10071593.

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Within 1 millisecond of action potential arrival at presynaptic terminals voltage–gated Ca2+ channels open. The Ca2+ channels are linked to synaptic vesicles which are tethered by active zone proteins. Ca2+ entrance into the active zone triggers: (1) the fusion of the vesicle and exocytosis, (2) the replenishment of the active zone with vesicles for incoming exocytosis, and (3) various types of endocytosis for vesicle reuse, dependent on the pattern of firing. These time-dependent vesicle dynamics are controlled by presynaptic Ca2+ sensor proteins, regulating active zone scaffold proteins, fus

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Zhu, Yixin, Baocheng Peng, Li Zhu, et al. "IGZO nanofiber photoelectric neuromorphic transistors with indium ratio tuned synaptic plasticity." Applied Physics Letters 121, no. 13 (2022): 133502. http://dx.doi.org/10.1063/5.0109772.

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Synaptic plasticity divided into long-term and short-term categories is regarded as the origin of memory and learning, which also inspires the construction of neuromorphic systems. However, it is difficult to mimic the two behaviors monolithically, which is due to the lack of time-tailoring approaches for a certain synaptic device. In this Letter, indium-gallium-zinc-oxide (IGZO) nanofiber-based photoelectric transistors are proposed for realizing tunable photoelectric synaptic plasticity by the indium composition ratio. Notably, short-term plasticity to long-term plasticity transition can be

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Kistler, Werner M., and J. Leo van Hemmen. "Short-Term Synaptic Plasticity and Network Behavior." Neural Computation 11, no. 7 (1999): 1579–94. http://dx.doi.org/10.1162/089976699300016151.

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We develop a minimal time-continuous model for use-dependent synaptic short-term plasticity that can account for both short-term depression and short-term facilitation. It is analyzed in the context of the spike response neuron model. Explicit expressions are derived for the synaptic strength as a function of previous spike arrival times. These results are then used to investigate the behavior of large networks of highly interconnected neurons in the presence of short-term synaptic plasticity. We extend previous results so as to elucidate the existence and stability of limit cycles with cohere

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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 (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, i

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Harvey-Girard, Erik, and Leonard Maler. "Dendritic SK channels convert NMDA-R-dependent LTD to burst timing-dependent plasticity." Journal of Neurophysiology 110, no. 12 (2013): 2689–703. http://dx.doi.org/10.1152/jn.00506.2013.

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Feedback and descending projections from higher to lower brain centers play a prominent role in all vertebrate sensory systems. Feedback might be optimized for the specific sensory processing tasks in their target brain centers, but it has been difficult to connect the properties of feedback synapses to sensory tasks. Here, we use the electrosensory system of a gymnotiform fish ( Apteronotus leptorhynchus) to address this problem. Cerebellar feedback to pyramidal cells in the first central electrosensory processing region, the electrosensory lateral line lobe (ELL), is critical for canceling s

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Bramham, C. "BDNF and Control of Synaptic Plasticity in the Adult Brain." European Psychiatry 24, S1 (2009): 1. http://dx.doi.org/10.1016/s0924-9338(09)70554-1.

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Experience-dependent changes in synaptic connectivity are thought to play a vital role not only in memory formation, but also in long-term adaptive responses involved in mood regulation, reward behavior, and pain control. The neurotrophin, brain-derived neurotrophic factor (BDNF), which has recently been implicated in memory formation and aspects of major depression, is also an important regulator of long-term synaptic plasticity in the adult mammalian brain. We have investigated BDNF function in the dentate gyrus, a brain region implicated in depression and the action of antidepressant drugs.

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Quinones, Jason Tait Sanchez, Quinones Karla, and Otto-Meyer Sebastian. "Factors Influencing Short-term Synaptic Plasticity in the Avian Cochlear Nucleus Magnocellularis." Journal of Experimental Neuroscience 9s2 (January 2015): JEN.S25472. http://dx.doi.org/10.4137/jen.s25472.

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Defined as reduced neural responses during high rates of activity, synaptic depression is a form of short-term plasticity important for the temporal filtering of sound. In the avian cochlear nucleus magnocellularis (NM), an auditory brainstem structure, mechanisms regulating short-term synaptic depression include pre-, post-, and extrasynaptic factors. Using varied paired-pulse stimulus intervals, we found that the time course of synaptic depression lasts up to four seconds at late-developing NM synapses. Synaptic depression was largely reliant on exogenous Ca2+-dependent probability of presyn

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Fong, Ming-fai, Peter Sb Finnie, Taekeun Kim, et al. "Distinct Laminar Requirements for NMDA Receptors in Experience-Dependent Visual Cortical Plasticity." Cerebral Cortex 30, no. 4 (2019): 2555–72. http://dx.doi.org/10.1093/cercor/bhz260.

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Abstract Primary visual cortex (V1) is the locus of numerous forms of experience-dependent plasticity. Restricting visual stimulation to one eye at a time has revealed that many such forms of plasticity are eye-specific, indicating that synaptic modification occurs prior to binocular integration of thalamocortical inputs. A common feature of these forms of plasticity is the requirement for NMDA receptor (NMDAR) activation in V1. We therefore hypothesized that NMDARs in cortical layer 4 (L4), which receives the densest thalamocortical input, would be necessary for all forms of NMDAR-dependent a

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Klyubin, Igor, Tomas Ondrejcak, Jennifer Hayes та ін. "Neurotransmitter receptor and time dependence of the synaptic plasticity disrupting actions of Alzheimer's disease Aβ in vivo". Philosophical Transactions of the Royal Society B: Biological Sciences 369, № 1633 (2014): 20130147. http://dx.doi.org/10.1098/rstb.2013.0147.

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Many endogenous factors influence the time course and extent of the detrimental effects of amyloid β-protein (Aβ) on synaptic function. Here, we assessed the impact of varying endogenous glutamatergic and cholinergic transmission by pharmacological means on the disruption of plasticity at hippocampal CA3-to-CA1 synapses in the anaesthetized rat. NMDA receptors (NMDARs) are considered critical in mediating Aβ-induced inhibition of long-term potentiation (LTP). However, intracerebroventricular injection of Aβ 1–42 inhibited not only NMDAR-dependent LTP but also voltage-activated Ca 2+ -dependent

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Zhou, Zhiwen, Kazuki Okamoto, Junya Onodera, et al. "Astrocytic cAMP modulates memory via synaptic plasticity." Proceedings of the National Academy of Sciences 118, no. 3 (2021): e2016584118. http://dx.doi.org/10.1073/pnas.2016584118.

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Astrocytes play a key role in brain homeostasis and functions such as memory. Specifically, astrocytes express multiple receptors that transduce signals via the second messenger cAMP. However, the involvement of astrocytic cAMP in animal behavior and the underlying glial–neuronal interactions remains largely unknown. Here, we show that an increase in astrocytic cAMP is sufficient to induce synaptic plasticity and modulate memory. We developed a method to increase astrocytic cAMP levels in vivo using photoactivated adenylyl cyclase and found that increased cAMP in hippocampal astrocytes at diff

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Edelmann, Elke, and Volkmar Leßmann. "Analyzing synaptic plasticity at the single cell level with STDP." Neuroforum 24, no. 3 (2018): A143—A150. http://dx.doi.org/10.1515/nf-2017-a064.

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Abstract Using patch clamp recordings in acutely isolated brain slices allows to investigate molecular processes that are involved in long-term potentiation (LTP) and long-term depression (LTD) at the level of a single postsynaptic neuron. Via the pipette solution in the recording pipette, it is possible to apply inhibitors of signaling cascades selectively into the postsynaptic neuron to unravel the molecular mechanisms of synaptic plasticity. In recent years, LTP research has been increasingly focused on induction protocols for LTP and LTD that rely on a minimal number of repeated synaptic s

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Elliott, Terry, and Konstantinos Lagogiannis. "Taming Fluctuations in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 21, no. 12 (2009): 3363–407. http://dx.doi.org/10.1162/neco.2009.12-08-916.

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A stochastic model of spike-timing-dependent plasticity proposes that single synapses express fixed-amplitude jumps in strength, the amplitudes being independent of the spike time difference. However, the probability that a jump in strength occurs does depend on spike timing. Although the model has a number of desirable features, the stochasticity of response of a synapse introduces potentially large fluctuations into changes in synaptic strength. These can destabilize the segregated patterns of afferent connectivity characteristic of neuronal development. Previously we have taken these jumps

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