The maintenance of spatial accuracy by the periasaccadic remapping of visual receptive fields
Neural Networks - Special issue on neural control and robotics: biology and technology
Perceptual grouping and the interactions between visual cortical areas
Neural Networks - 2004 Special issue Vision and brain
Journal of Cognitive Neuroscience
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Unsupervised learning of overlapping image components using divisive input modulation
Computational Intelligence and Neuroscience
A single functional model of drivers and modulators in cortex
Journal of Computational Neuroscience
| Hi-index | 0.00 |
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).