Decoupling through synchrony in neuronal circuits with propagation delays.

By E. Lubenov, Athanassios Siapas. 2008

189 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks. I. Input selectivity–strengthening correlated input pathways (2009, background): “However, the study of STDP in such recurrent networks has only begun to be addressed, mainly using numerical simulation (Song and Abbott 2001; Senn et al. 2002; Wenisch et al. 2005; Morrison et al. 2007; Lubenov and Siapas 2008; Câteau et al. 2008; Kang et al. 2008).”
Connectivity reflects coding: a model of voltage-based STDP with homeostasis (2010, background): “The advantage of such a minimal model is that it allows us to discuss functional consequences in smal”
Limits to the Development of Feed-Forward Structures in Large Recurrent Neuronal Networks (2011, background): “…results, e.g., distributing the conduction delays or defining the conduction delay to be largely axonal, although such adaptations of the network model may well suppress or alter the pathological state the network enters when the amount of stimulation is too high (see Lubenov and Siapas, 2008).”
On Spike-Timing-Dependent-Plasticity, Memristive Devices, and Building a Self-Learning Visual Cortex (2011, methodology): “For negative weight synapses an STDP learning function with a shape similar to that shown in Figure 3C (Lubenov and Siapas, 2008) is required.”
Does Spike-Timing-Dependent Synaptic Plasticity Couple or Decouple Neurons Firing in Synchrony? (2012, methodology): “By contrast, many theoretical studies assume A+=A− and/or τ+= τ− to reduce the number of free parameters or to ease analyses (Levy et al., 2001; Morrison et al., 2007; Lubenov and Siapas, 2008).”
Sleep and the Price of Plasticity: From Synaptic and Cellular Homeostasis to Memory Consolidation and Integration (2014, background): “The slope of the response evoked in the rat hippocampal CA3 region by electrical stimulation of the fimbria also declines in the sleep period following a wake episode (Lubenov and Siapas, 2008).”
Rotating waves during human sleep spindles organize global patterns of activity that repeat precisely through the night (2016, background): “…the stereotyped activity patterns reported here may enable STDP to establish large-scale neuronal assemblies at scales where axonal conduction delays are long relative to the oscillation cycle (Fries, 2005; Lubenov and Siapas, 2008), and repeat many times throughout sleep with millisecond accuracy.”
State-dependent spike timing relationships between hippocampal and prefrontal circuits during sleep (2009, background): “This circuit reorganization is believed to result from coordinated activity between and within the hippocampus and the neocortex not only during awake behavior, but also during sleep (Wilson and McNaughton, 1994; Siapas and Wilson, 1998; Stickgold et al., 2001; Lubenov and Siapas, 2008).”
REM Sleep Reorganizes Hippocampal Excitability (2012, unknown): “In contrast, ''homeostatic'' models suggest that waking experience-related neurons add to the overall excitability of the cortical networks and sleep (i.e., non-REM) serves to equalize and reduce rates (Borbé ly, 1982; Tononi and Cirelli, 2006; Lubenov and Siapas, 2008).”
Sleep and synaptic renormalization: a computational study. (2010, background): “Presumably, similar results would have been obtained using burst-LTD 752 (Czarnecki et al. 2007) and decoupling through synchrony (Lubenov & Siapas 2008).”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, methodology): “Our model focused on consolidation at the synaptic level, whereas other mechanisms at the system level, such as theta rhythms, replay, and slow-wave sleep, also affect memory maintenance (Lubenov and Siapas, 2008; Rossato et al., 2009).”
Disruption of ripple-associated hippocampal activity during rest impairs spatial learning in the rat (2009, background): “It has been recently suggested that the population bursts of sharp waveripples serve to desynchronize neurons through STDP rules, thus preventing blowup of excitation in the hippocampal network and selectively erasing some memories but not others (Lubenov and Siapas, 2008).”
Emergence of network structure due to spike-timing-dependent plasticity in recurrent neuronal networks V: self-organization schemes and weight dependence (2010, background): “2009d), which makes it possible to obtain more synchrony or decorrelation for neuronal groups (Iglesias et al. 2005; Lubenov and Siapas 2008; Câteau et al. 2008).”
Stable Learning in Stochastic Network States (2012, background): “In this case, synaptic weights directly change the network dynamics and vice versa (Lubenov and Siapas, 2008).”
Cortical firing and sleep homeostasis. (2009, background): “Highly synchronous burst firing during the ON periods of early NREM sleep, the alternation of depolarization and hyperpolarization at around 1 Hz, and the neuromodulatory milieu of sleep may help restore synaptic strength (Czarnecki et al., 2007; Lubenov and Siapas, 2008; Seol et al., 2007).”