Normalization of cell responses in cat striate cortex

By David J. Heeger. 1992

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In Neuroscience

Citations in the corpus

Fixup Initialization: Residual Learning Without Normalization (2019, methodology): “The ﬁrst use of normalization in neural networks appears in the modeling of biological visual system and dates back at least to Heeger (1992) in neuroscience and to Pinto et al. (2008); Lyu & Simon-celli (2008) in computer vision, where each neuron output is divided by the sum (or norm) of all of…”
End-to-end Optimized Image Compression (2016, background): “These may be signiﬁcantly reduced through the use of joint local nonlinear gain control operations (Schwartz and Simoncelli, 2001; Lyu, 2010; Sinz and Bethge, 2013), inspired by models of visual neurons (Heeger, 1992; Carandini and Heeger, 2012).”
Density Modeling of Images using a Generalized Normalization Transformation (2015, background): “The transformation is a generalization of divisive normalization , a form of local gain control ﬁrst introduced as a means of modeling nonlinear properties of cortical neurons (Heeger, 1992), in which linear responses are divided by pooled responses of their rectiﬁed neighbors.”
Normalization as a canonical neural computation (2011, background): “Normalization was proposed in the early 1990s to explain non-linear properties of neurons in the primary visual corte”
Adaptive resonance theory (1997, background): “The combined effect of the bottom-up LGN pathways is to stimulate layer 4 via an on-center off-surround, which provides divisive contrast normalization [12][13][14] of layer 4 cell responses.”
A model of neuronal responses in visual area MT. (1998, neutral): “There is evidence that complex cells do not receive input from simple cell afferents, but directly from the LGN (see Heeger, 1992b for review).”
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes (2016, background): “Divisive Normalization (DN) on the other hand modulates the neural activity by the activity of a pool of neighboring neurons (Heeger, 1992; Bonds, 1989).”
Surrounding Suppression and Facilitation in the Determination of Border Ownership (2006, background): “The contrast signal passes through divisive normalization and compressive static nonlinearity to reproduce the response characteristics of V1 neurons (Wilson, 1993; Heeger, 1992).”
Contrast gain control and cortical TrkB signaling shape visual acuity (2010, methodology): “For this reason, we employed Heeger's normalization mode”
Adaptive Resonance Theory: How a brain learns to consciously attend, learn, and recognize a changing world (2013, background): “Because the ART Matching Rule is realized by a circuit with a modulatory on-center, and top-down signals can jump from layer 6 of a higher cortical level to layer 6 of a lower cortical level, a topdown signal from a much higher cortical level (e.g., the prefrontal cortex) can modulate, or prime, all”
Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging (2015, background): “All of the filter responses were rectified and divisively normalized to account for the process of non-linear contrast gain control in visual cortex [42].”
A Normalization Model of Multisensory Integration (2011, unknown): “…each combination of dominance weights were simulated, for a total population of 45 neurons (similar to that recorded by Alvarado et al. 26 ), Responses were computed for five stimulus intensities (4, 16, 64, 256, and 1024), Poisson noise was added, and 8 repetitions of each stimulus were simulated.”
A Synaptic Explanation of Suppression in Visual Cortex (2002, methodology): “Because the effects of synaptic depression are divisive, the model correctly predicts that the effects of suppression are divisive (Bonds, 1989; Heeger, 1992a; Carandini et al., 1997; Freeman et al., 2002).”
The Neurobiology of Decision: Consensus and Controversy (2009, unknown): “Parietal neurons, presumably using a normalization mechanism like the one studied in visual cortex (Heeger, 1992), rescale these absolute values so as to maximize the differences between the available options before choice is attempted.”
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features (2014, methodology): “To further remove local correlations, adaptive gain control [40] or divisive normalization models [38], [39] have been developed, both aiming to model nonlinear cortical function and to conduct objective IQA.”