The Normalization Model of Attention

By John H. Reynolds, David J. Heeger. 2009

1,384 citations recorded by the source corpus.

Attention has been found to have a wide variety of effects on the responses of neurons in visual cortex. We describe a model of attention that exhibits each of these different forms of attentional modulation, depending on the stimulus conditions and the spread (or selectivity) of the attention field in the model. The model helps reconcile proposals that have been taken to represent alternative theories of attention. We argue that the variety and complexity of the results reported in the literature emerge from the variety of empirical protocols that were used, such that the results observed in any one experiment depended on the stimulus conditions and the subject’s attentional strategy, a notion that we define precisely in terms of the attention field in the model, but that has not typically been completely under experimental control.

Subfields: Cognitive Neuroscience

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In Cognitive Neuroscience

Citations in the corpus

Theory of Mind: A Neural Prediction Problem (2013, neutral): “…speed and accuracy of performance by increasing overall neural responses to attended stimuli (Feldman and Friston, 2010; Friston, 2010; Herrmann et al., 2010; Hillyard et al., 1998; Kok et al., 2012b; Martinez-Trujillo and Treue, 2004; Reynolds and Heeger, 2009; Treue and Martínez-Trujillo, 1999).”
Normalization as a canonical neural computation (2011, neutral): “This characterization of the responses of neural population”
Top-Down Neural Attention by Excitation Backprop (2016, background): “ioral goals can affect the feature and location expectancy in visual processing (Wolfe 1994;Anne 1980;Koch andUllman 1987; Desimone and Duncan 1995), and bias the competition among the neurons (Reynolds and Heeger 2009; Tsotsos et al. 1995; Desimone and Duncan 1995; Desimone 1998; Diane 2009).”
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes (2016, neutral): “…(Simoncelli & Heeger, 1998), stimulus decoding (Ringach, 2009; Froudarakis et al., 2014), winner-take-all mechanisms (Busse et al., 2009), attention (Reynolds & Heeger, 2009), redundancy reduction (Schwartz & Simoncelli, 2001; Sinz & Bethge, 2008; Lyu & Simoncelli, 2008; Sinz & Bethge, 2013),…”
Artificial Neural Networks for Neuroscientists: A Primer (2020, methodology): “Computational models are able to capture various aspects of bottom-up [Koch and Ullman, 1987] and top-down attention [Ardid et al., 2007, Reynolds and Heeger, 2009].”
A mechanistic cortical microcircuit of attention for amplification, normalization and suppression. (2015, methodology): “The model of Reynolds and Heeger (2009) is probably the most popular one as it can replicate data from several experiments: modulation of the contrast response function, modulation of the neuronal tuning curve and biased competition with feature-based attention.”
Broadly tuned response properties of diverse inhibitory neuron subtypes in mouse visual cortex (2010, neutral): “Uniform sensory tuning that reflects local population responses may enable different GABAergic subtypes to perform a similar balancing operation on local excitatory neurons, such as a simple normalization (Carandini et al., 1997; Reynolds and Heeger, 2009), but within different behavioral contexts.”
Attention, Uncertainty, and Free-Energy (2010, background): “Finally, Risko and Stolz (2010) demonstrated that knowledge of the proportion of valid trials did not affect reaction time.”
Population coding under normalization (2009, background): “Furthermore, normalization models appear to account well for the modulatory effects of attention (Reynolds, Chelazzi, & Desimone, 1999; Reynolds & Heeger, 2009).”
An Introduction to Model-Based Cognitive Neuroscience (2015, background): “progress in the manufacture and use of such polytrodes [96] and there exists a large”
Waking State: Rapid Variations Modulate Neural and Behavioral Responses (2015, neutral): “…as arousal, attention, and awareness are ostensibly less dramatic, and therefore the mechanisms of waking state variations have remained more elusive (reviewed by Harris and Thiele, 2011; Kanwisher, 2001; Lamme, 2003; Maunsell and Treue, 2006; Reynolds and Heeger, 2009; Zagha and McCormick, 2014).”
Foundations of Vision (2018, methodology): “According to the normalization model of attention, shifts in contrast gain will predominate if the attentional window is large and the stimulus is much smaller (Reynolds & Heeger, 2009).”
Interaction of feedforward and feedback streams in visual cortex in a firing-rate model of columnar computations (2014, neutral): “…detection and grouping (Grossberg, Mingolla, & Ross, 1997; Neumann & Sepp, 1999; Thielscher & Neumann, 2003), motion integration (Bayerl & Neumann, 2004; Berzhanskaya, Grossberg, & Mingolla, 2007), figure-ground segregation (Barnes & Mingolla, 2013), and feature attention (Reynolds & Heeger, 2009).”
State-of-the-Art in Visual Attention Modeling (2013, background): “For a review on computational models of visual attention in general, including biased competition [10], selective tuning [15], normalization models of attention [181], and many others; please refer to [8].”