Computational models of cortical visual processing.

By David J. Heeger, Eero P. Simoncelli, J. Anthony Movshon. 1996

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Citations in the corpus

Normalization as a canonical neural computation (2011, background): “Normalization in other cortical areas There is evidence for normalization downstream from area V1, and particularly in the visual cortical area MT”
Evaluating the Visualization of What a Deep Neural Network Has Learned (2015, background): “In this paper we refrain from mimicking the complex human heatmap evaluation process which includes attention, interest point models and perception models of saliency [36], [37], [38], [39] for the reason that we are interested in finding those regions that are relevant for a given classifier.”
How does the brain solve visual object recognition? (2012, neutral): “At an elemental level, we have respectable models (e.g. NLN class; Heeger et al., 1996; Kouh and Poggio, 2008) of how each single unit computes its firing rate output from its inputs.”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, background): “1 would involve another set of synaptic weights w̃ in the denominator, as explored in a few different contexts such as to increase the independence of correlated signals [Heeger et al., 1996; Schwartz and Simoncelli, 2001] and the biased competition model of attention [Reynolds et al.”
Linearity and Normalization in Simple Cells of the Macaque Primary Visual Cortex (1997, background): “, 1983), the vestibuloocular reflex (Lisberger and Sejnowski, 1992), and velocityselective neurons in area MT of the primate cortex (Heeger et al., 1996; Simoncelli and Heeger, 1997).”
Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects. (1999, background): “In addition, the repetitive subtraction of neighboring neuronal activities (Equation 8) may produce a net effect similar to divisive normalizatio”
Theory of cortical function (2017, background): “Because of space limitations, a number of influential papers were not cited in the main text (1-27).”
Correspondence of Deep Neural Networks and the Brain for Visual Textures (2018, background): “For instance, biological studies have found selectivity in V2 to conjunctions of orientations, and to ﬁgure ground [51], [52], [53], with some aspects addressed in computational models of V2 [54], [55], [56], [57], [58] [59].”
Multiple Object Response Normalization in Monkey Inferotemporal Cortex (2005, methodology): “This finding suggests that divisive normalization mechanisms analogous to those proposed to explain response rescaling in early visual stages (Heeger, 1992; Desimone and Duncan, 1995; Heeger et al., 1996; Carandini et al., 1997; Reynolds et al., 1999) and area MT (Recanzone et al.”
Shunting Inhibition Does Not Have a Divisive Effect on Firing Rates (1997, background): “Discussion Divisive normalization of ring rates has become a popular idea in visual cortex (Albrecht and Geisler, 1991; Heeger, 1992; Heeger et al., 1996).”
Predictive Coding as a Model of Response Properties in Cortical Area V1 (2010, methodology): “The normalization model is capable of simulating a subset of the results presented here (Heeger, 1994; Heeger et al., 1996; Schwartz and Simoncelli, 2001) and has also been recently extended (Reynolds and Heeger, 2009) to model a subset of the attentional data that can be simulated by PC/BC (Spratli”
Mechanisms underlying cross-orientation suppression in cat visual cortex (2006, background): “Cross-orientation inhibition, in turn, has been the cornerstone of several feedback models for orientation selectivit”
Stochastic resonance and sensory information processing: a tutorial and review of application. (2004, background): “…to be a widespread characteristic of neuronal mechanisms involved in vision (for instance, simple cells in V1 feed forward to complex cells with a threshold nonlinearity according to the canonical model by Hubel and Wiesel) (Maunsell and Van Essen, 1983; Heeger et al., 1996; Koch and Segev, 2000).”
Analysis of Direction Selectivity Arising from Recurrent Cortical Interactions (1998, background): “Recurrent inhibitory interactions between differently tuned cells are a common feature of computational models of visual cortex, often invoked to explain nonlinear nonspecific-oppression effects found in both physiological and psychophysical experiments (Qian et al., 1994; Qian & Anderson, 1994; Hee”