Top-down influences on visual processing

By C. Gilbert, Wu Li. Nature Reviews Neuroscience, 2013

1,155 citations recorded by the source corpus.

Re-entrant or feedback pathways between cortical areas carry rich and varied information about behavioural context, including attention, expectation, perceptual tasks, working memory and motor commands. Neurons receiving such inputs effectively function as adaptive processors that are able to assume different functional states according to the task being executed. Recent data suggest that the selection of particular inputs, representing different components of an association field, enable neurons to take on different functional roles. In this Review, we discuss the various top-down influences exerted on the visual cortical pathways and highlight the dynamic nature of the receptive field, which allows neurons to carry information that is relevant to the current perceptual demands.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c36a411efba48afb635ebb69891733cd815882f4

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In Cognitive Neuroscience

Citations in the corpus

Towards deep learning with segregated dendrites (2016, background): “Indeed, framing cortico-cortical feedback as a mechanism to predict or modulate incoming sensory activity is a more common way of viewing feedback signals in the neocortex (Larkum, 2013; Gilbert and Li, 2013; Zhang et al., 2014; Fiser et al., 2016; Leinweber et al., 2017).”
Theory of cortical function (2017, background): “This is similar to classic recurrent network models of working memory that maintain a memory representation with a self-sustaining pattern of persistent activity (28-32), and also to some models of perceptual organization, segmentation, and grouping (3336).”
Direct Feedback Alignment Provides Learning in Deep Neural Networks (2016, background): “This is biologically plausible in the sense that cortical areas are known to be reciprocally connected [3].”
How Deep is the Feature Analysis underlying Rapid Visual Categorization? (2016, background): “This would be followed by re-entrant and other top-down signals [Gilbert and Li, 2013] when more time is available for visual processing.”
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions. (2017, neutral): “This modulation can be driven by attention (Roelfsema et al., 1998; Zhang et al., 2014), context (Fiser et al., 2016; Zipser et al., 1996), or expectations (Gilbert and Li, 2013; Gilbert and Sigman, 2007).”
Attentional Correlation Filter Network for Adaptive Visual Tracking (2017, methodology): “The importance of attentional mechanisms of the human visual system is highlighted by recent trends in neuroscience research [13,14,28], as well as by theoretical work on the computational aspects of visual attention [12, 34].”
Computing with a Canonical Neural Circuits Model with Pool Normalization and Modulating Feedback (2014, result): “The same generic framework has also been extended and applied in the motion domain to account for the mechanism of motion integration, disambiguation (aperture problem), and segregation as well as selective motion feature attention (Barnes & Mingolla, 2013; Bayerl & Neumann, 2004, 2006; Berzhanskaya”
STNet: Selective Tuning of Convolutional Networks for Object Localization (2017, background): “It is significantly supported by reliable evidence [9, 12] that information is processed in both directions throughout the visual hierarchy: the data-driven Bottom-Up (BU) processing stream convolves the input data using some forms of information transformation.”
The cortical dynamics of speaking: present shortcomings and future avenues (2016, background): “…target-irrelevant pathways, just as advocated in other domains of cognition (e.g. Bar, 2003; Corbetta & Shulman, 2002; Desimone & Duncan, 1995; Engel et al., 2001; Gilbert & Li, 2013; Heald & Nusbaum, 2014; Hillyard et al., 1998; Kastner et al., 1999; Kiefer, 2007, 2012; Naccache et al., 2002).”
Cortical feedback decorrelates olfactory bulb output in awake mice (2015, background): “Visual, auditory, and somatosensory experiments have proposed several roles for cortical feedback, including sharpening of sensory representations, or relaying information pertaining to expectation, reward, attention, learning, and action (Gilbert and Li, 2013; Glickfeld et al., 2013; Harris and Mrs”
A generative vision model that trains with high data efficiency and breaks text-based CAPTCHAs (2017, background): “In the mammalian brain, feedback connections in the visual cortex play roles in figure-ground-segmentation, and in object-based top-down attention that isolates the contours of an object even when partially transparent objects occupy the same spatial locations ( 13 – 16 ).”