Neural mechanisms of orientation selectivity in the visual cortex.

By D. Ferster, Kenneth D. Miller. Annual Review of Neuroscience, 2000

719 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/d12866a3c9b7afac61ec8dbff4763346e9463921

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In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Melting the iceberg: contrast invariance in visual cortex. (2007, background): “In the half-century that followed this classic model, mountains of evidence have accumulated both to support it and to show its limitations, resulting in considerable controversy (Ferster and Miller, 2000).”
Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. (2003, background): “After a long-standing debate, most recent models favor the second possibility (see review in Ferster and Miller, 2000, and Douglas et al., 1999), largely because experi-”
A Theory of Object Recognition: Computations and Circuits in the Feedforward Path of the Ventral Stream in Primate Visual Cortex (2005, methodology): “Then, we borrow and extend Hubel and Wiesel’s classical model of complex cells as being combination of simple cells with the same orientation selectivity at different spatial location, giving them the translation invariance properties [Ferster and Miller, 2000].”
Timescale-dependent shaping of correlation by olfactory bulb lateral inhibition (2011, neutral): “ρT 1⁄4 hn1n2i− hn1ihn2i ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n1 − hn1i q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi n2 − hn2i q : [1]”
The Normalization Model of Attention (2009, background): “The biophysical mechanism for the linear summation might depend on a push-pull combination of synaptic excitation and inhibition (Ferster and Miller, 2000; Hirsch and Martinez, 2006).”
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression (2009, background): “The strong excitatory recurrence in ISNs allows the networks to perform complex computations, and yet remain stable enough to respond in a graded way to stimuli of different strengths [8].”
Orientation Selectivity in Macaque V1: Diversity and Laminar Dependence (2002, background): “The color scale represents the relative density of neurons in the distribution and ranges from 0 (blue) to 1 (red). view, see Sompolinsky and Shapley, 1997; Ferster and Miller, 2000).”
Timing in the absence of clocks: encoding time in neural network states. (2007, background): “For example, the coding and representation of simple spatial patterns, such as the orientation of a bar of light, are well characterized in primary visual cortex (Hubel and Wiesel, 1962; Ferster and Miller, 2000).”
A Canonical Neural Circuit for Cortical Nonlinear Operations (2008, background): “Such a postulate takes an intermediate position between the two competing theories about the origin of orientation selectivity in the primary visual cortex V1 (Ferster & Miller, 2000).”
The dynamics of perceptual learning: an incremental reweighting model. (2005, methodology): “This representational framework is consistent with the principles of both the functional organization of the early cortical visual areas (De Valois & De Valois, 1988; Ferster & Miller, 2000) and the perceptual template model (Lu & Dosher, 1998).”
Mechanisms underlying cross-orientation suppression in cat visual cortex (2006, background): “equation (2) are therefore not absolute estimates of excitation and inhibition,”
Parvalbumin-Expressing Interneurons Linearly Transform Cortical Responses to Visual Stimuli (2012, background): “…neurons are tuned to orientation (Anderson et al., 2000) (although PV cells are likely to be less turned than other neurons; Cardin et al., 2007; Nowak et al., 2008), more complex models may be necessary to describe their impact on visual responses (Ferster and Miller, 2000; Katzner et al., 2011).”
GABAA inhibition controls response gain in visual cortex (2011, positive): “According to this view, the sharpness of the orientation tuning curve of a neuron would depend critically on inhibition suppressing responses at the flanks of the curve (Vidyasagar et al., 1996; Sompolinsky and Shapley, 1997; Ferster and Miller, 2000).”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “…e.g., with nonlinearities (Brito and Gerstner, 2016), can give rise to diﬀerent forms of correlation and competition between neurons, leading to the self-organized formation of ocular dominance columns, self-organizing maps and orientation columns (Miller et al., 1989; Ferster and Miller, 2000).”