Gain modulation from background synaptic input.

By Frances S. Chance, L. F. Abbott, A. Reyes. 2002

1,015 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

Normalization as a canonical neural computation (2011, background): “This variation could be achieved if the conductance changes were evoked by the noisy activity of other neuron”
Dendritic excitability and synaptic plasticity. (2008, neutral): “Changes in the statistics of noisy synaptic inputs and changes in the ratio of excitation to inhibition can change the slope of the f/I curve and thus the gain of the neuron (120, 528, 611).”
A Canonical Neural Circuit for Cortical Nonlinear Operations (2008, methodology): “(For a similar discussion, see Carandini & Heeger, 1994; Reichardt et al., 1983; Chance et al., 2002; Holt & Koch, 1997.)”
Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. (2003, background): “conductance can affect the gain of the fI curve—such a conductance could be a target for gain modulation (Chance et al. 2002; Holt and Koch 1997; Smith et al. 2002).”
Variations on an inhibitory theme: phasic and tonic activation of GABAA receptors (2005, background): “The slope of the input–output relationship depends on the variability of the input conductance: with tonic inhibition, a higher frequency of synaptic excitatory input (with higher variance) is required to achieve a given output rat”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “and gain modulation by balanced inputs (163) or by a”
Inference and computation with population codes. (2003, background): “This solution requires neurons that can multiply their inputs, a readily achievable neural operation (Chance et al. 2002, Salinas & Abbott 1996).”
Computing and Stability in Cortical Networks (2004, background): “These properties are typical of both model neurons (Brunel & Sergi, 1998; Tiesinga et al., 2000; Fourcaud & Brunel, 2002; Brunel & Latham, 2003) and real neurons (McCormick et al., 1985; Chance, Abbott, & Reyes, 2002 ), and in the next two sections we will see how they connect to the problem of rob”
Tonically active GABA A receptors: modulating gain and maintaining the tone. (2004, background): “This occurs because the gain of the input–output relationship depends on the variability of the input conductance [49, 50 ].”
Unsupervised Discovery of Demixed, Low-Dimensional Neural Dynamics across Multiple Timescales through Tensor Component Analysis (2017, background): “Furthermore, plausible cellular mechanisms for gain modulation have been examined by a number of experimental and computational studies [38–41].”