How MT cells analyze the motion of visual patterns

By Nicole C. Rust, Valerio Mante, Eero P. Simoncelli, J. Anthony Movshon. 2006

562 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Molecular Biology

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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”
A Biologically Inspired System for Action Recognition (2007, methodology): “The directional tuning curve of V1 neurons is modeled as a circular-Gaussian-like function [6, 21, 57].”
Functional mechanisms shaping lateral geniculate responses to artificial and natural stimuli. (2008, methodology): “By using the model as a building block in cascaded models of the visual system (Rust et al., 2006), it will be possible to disentangle computations performed by the later stages from those already implemented in the retina and LGN.”
Using goal-driven deep learning models to understand sensory cortex (2016, background): “Models cannot be limited just to explaining a narrow phenomenon identified on carefully chosen neurons, defined only for highly controlled and simplified stimuli 8,9 .”
Navigating the Neural Space in Search of the Neural Code. (2017, unknown): “First, many behaviors depend on converging and redundant signals from large populations of similarly behaving neurons (Cohen and Maunsell, 2009; Froudarakis et al., 2014; Jazayeri and Movshon, 2006; Priebe and Lisberger, 2004; Rust et al., 2006; Shadlen et al., 1996; Zohary et al., 1994).”
Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos (2010, methodology): “Our temporal quality computation bears several similarities with the neuronal model of MT in [58], [ 72 ].”
Neural networks and neuroscience-inspired computer vision. (2014, background): “The analysis of time-varying signals for object perception has long been the subject of convergent interest across computational neuroscience and computer vision [76–86], but we argue that there is a good deal of room tomake these connections stronger going forward.”
Learning to Extract Motion from Videos in Convolutional Neural Networks (2016, background): “Subsequent improvements [29, 31, 38] focused on the design of those ﬁlters.”
A neural code for three-dimensional object shape in macaque inferotemporal cortex (2008, methodology): “We quantified this response pattern with a multi-component model analogous to those applied recently to dorsal pathway area MT and in posterior IT corte”
A Canonical Neural Circuit for Cortical Nonlinear Operations (2008, methodology): “As shown here and as considered by Rust et al. (2006), such " tuned " normalization can sharpen the tuning curves within the afferent input space, and it may be implemented by the stronger self-inhibiting synapses in Figure 1B.”