Mechanism of gain modulation at single neuron and network levels.

Journal of Computational Neuroscience
M BrozovićR A Andersen

Abstract

Gain modulation, in which the sensitivity of a neural response to one input is modified by a second input, is studied at single-neuron and network levels. At the single neuron level, gain modulation can arise if the two inputs are subject to a direct multiplicative interaction. Alternatively, these inputs can be summed in a linear manner by the neuron and gain modulation can arise, instead, from a nonlinear input-output relationship. We derive a mathematical constraint that can distinguish these two mechanisms even though they can look very similar, provided sufficient data of the appropriate type are available. Previously, it has been shown in coordinate transformation studies that artificial neurons with sigmoid transfer functions can acquire a nonlinear additive form of gain modulation through learning-driven adjustment of synaptic weights. We use the constraint derived for single-neuron studies to compare responses in this network with those of another network model based on a biologically inspired transfer function that can support approximately multiplicative interactions.

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Citations

Jul 5, 2013·PLoS Computational Biology·Ritwik K Niyogi, KongFatt Wong-Lin
Jul 25, 2014·PLoS Computational Biology·Hugo Gravato MarquesFumiya Iida
Jul 3, 2013·Journal of Computational Neuroscience·M W Spratling
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Mar 10, 2018·PLoS Computational Biology·Sarah JarvisSimon R Schultz
Mar 5, 2019·International journal for vitamin and nutrition research. Internationale Zeitschrift für Vitamin- und Ernährungsforschung. Journal international de vitaminologie et de nutrition·Sahar Foshati, Maryam Ekramzadeh
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Nov 12, 2021·PLoS Computational Biology·Julia C GormanBrian J Fischer

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