Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function

Kris De Meyer; Michael W. Spratling

doi:10.1162/NECO_a_00130

Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function

Kris De Meyer, Michael W. Spratling

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Abstract

The combination of two or more population-coded signals in a neural model of predictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian learning rule. The behavior of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. This letter thus connects an often-observed neurophysiological phenomenon and important neurocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).

Original language	English
Article number	N/A
Pages (from-to)	1536-1567
Number of pages	32
Journal	Neural Computation
Volume	23
Issue number	6
DOIs	https://doi.org/10.1162/NECO_a_00130
Publication status	Published - Jun 2011

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Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical FunctionAccepted author manuscript, 578 KB

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abstract = "The combination of two or more population-coded signals in a neural model of predictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian learning rule. The behavior of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. This letter thus connects an often-observed neurophysiological phenomenon and important neurocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).",

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AB - The combination of two or more population-coded signals in a neural model of predictive coding can give rise to multiplicative gain modulation in the response properties of individual neurons. Synaptic weights generating these multiplicative response properties can be learned using an unsupervised, Hebbian learning rule. The behavior of the model is compared to empirical data on gaze-dependent gain modulation of cortical cells and found to be in good agreement with a range of physiological observations. Furthermore, it is demonstrated that the model can learn to represent a set of basis functions. This letter thus connects an often-observed neurophysiological phenomenon and important neurocomputational principle (gain modulation) with an influential theory of brain operation (predictive coding).

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Multiplicative Gain Modulation Arises Through Unsupervised Learning in a Predictive Coding Model of Cortical Function

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