Synaptic reverberation underlying mnemonic persistent activity.

By X. Wang. Trends in Neurosciences, 2001

1,056 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/0c0c1c674ef20445ec0707e22f810bd789c4719d

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Neural basis of reinforcement learning and decision making. (2012, background): “Such persistent activity has been often associated with working memory (Funahashi et al. 1989, Wang 2001), but may also subserve the temporal integration of noisy inputs (Shadlen & Newsome 2001, Roitman & Shadlen, 2002, Wang, 2002, Curtis & Lee 2010).”
Arc Regulates Experience-Dependent Persistent Firing Patterns in Frontal Cortex (2014, background): “Investigating these mechanisms during cognitive tasks in vivo remains a significant technical challenge (Williams and Goldman-Rakic, 1995; Wang et al., 2013).”
Memory without Feedback in a Neural Network (2009, background): “Electrophysiological recordings have revealed a neural correlate of the storage of memorized stimuli in which the persistent firing rate of individual neurons varies in a graded manner with the stored stimulus (Brody et al., 2003b; Durstewitz et al., 2000; Huk and Shadlen, 2005; Major and Tank, 2004”
A model of interval timing by neural integration (2011, methodology): “Given current knowledge about the time constants and noise characteristics of neural processing, the level of synaptic tuning precision required to cancel leak by this method is thought by many to be implausible (cf. Wang, 2001; Major and Tank, 2004).”
Decision making in recurrent neuronal circuits. (2008, methodology): “A leading candidate mechanism for the generation of persistent activity is strong recurrent excitation in a local cortical circuit that gives rise to stimulus-selective attractor states—self-sustained population activity patterns of a neural network (Amit, 1995; Goldman-Rakic, 1995; Wang, 2001).”
Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics (2018, background): “…models have shown how hierarchical differences in synaptic properties can contribute to functional specialization of areas, such as their capacity to generate the robust persistent activity thought to underlie working memory computations (Wang, 2001; Chaudhuri et al., 2015; Murray et al., 2017).”
State-dependent computations: spatiotemporal processing in cortical networks (2009, background): “Although there is evidence that in higher-order cortical areas these fixed-point attractors play a part in working memor”
Computational models of schizophrenia and dopamine modulation in the prefrontal cortex (2008, background): “For example, researchers can investigate the effects of alterations in ion channels (for instance, in the currents that they pass) on the operation of the whole neural network in, for example, maintaining a short-term memor”