Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A neural model of contour integration in the primary visual cortex
Neural Computation
Attentional gain modulation as a basis for translation invariance
CNS '96 Proceedings of the annual conference on Computational neuroscience : trends in research, 1997: trends in research, 1997
A model of contextual interactions and contour detection in primary visual cortex
Neural Networks - 2004 Special issue Vision and brain
A Feedback Model of Visual Attention
Journal of Cognitive Neuroscience
Journal of Cognitive Neuroscience
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
Enhancement of Perceptually Salient Contours using a Parallel Artificial Cortical Network
Biological Cybernetics
A neural contextual model for detecting perceptually salient contours
Pattern Recognition Letters
Input Feedback Networks: Classification and Inference Based on Network Structure
Proceedings of the 2008 conference on Artificial General Intelligence 2008: Proceedings of the First AGI Conference
Virtual uteral inhibition in parallel activation models of associative memory
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
International Journal of Imaging Systems and Technology
Predictive coding accounts for V1 response properties recorded using reverse correlation
Biological Cybernetics
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
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A distinction is commonly made between synaptic connections capable of evoking a response ("drivers") and those that can alter ongoing activity but not initiate it ("modulators"). Here it is proposed that, in cortex, both drivers and modulators are an emergent property of the perceptual inference performed by cortical circuits. Hence, it is proposed that there is a single underlying computational explanation for both forms of synaptic connection. This idea is illustrated using a predictive coding model of cortical perceptual inference. In this model all synaptic inputs are treated identically. However, functionally, certain synaptic inputs drive neural responses while others have a modulatory influence. This model is shown to account for driving and modulatory influences in bottom-up, lateral, and top-down pathways, and is used to simulate a wide range of neurophysiological phenomena including surround suppression, contour integration, gain modulation, spatio-temporal prediction, and attention. The proposed computational model thus provides a single functional explanation for drivers and modulators and a unified account of a diverse range of neurophysiological data.