A Simple Competitive Account of Some Response Properties of Visual Neurons in Area MSTd

Wang, Ruye

doi:10.1162/neco.1995.7.2.290

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…The incremental enhancement of our models with changes in the relative influence of receptive field segments supports the view that MSTd is optimized for optic flow direction selectivity (Duffy and Wurtz 1995;Lappe et al 1996) independent of other motion parameters (Calow and Lappe 2007). This selectivity may be the result of the dynamic adjustment of weightings on the inputs to each neuron (Wang 1995), possibly through Hebbian shaping guided by heading direction feedback (Zhang et al 1993). This optimization might use MSTd's co-activation by signals from object motion (Logan and Duffy 2006;Royden and Hildreth 1999;Zemel et al 1998) and vestibular input (Duffy 1998;Gu et al 2006;Page and Duffy 2003) about self-movement.…”

Section: Gain Modulation By Optic Flowsupporting

confidence: 71%

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

Page

Gaborski

et al. 2010

Journal of Neurophysiology

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Yu CP, Page WK, Gaborski R, Duffy CJ. Receptive field dynamics underlying MST neuronal optic flow selectivity. J Neurophysiol 103: 2794 -2807, 2010. First published March 24, 2010 doi:10.1152/jn.01085.2009. Optic flow informs moving observers about their heading direction. Neurons in monkey medial superior temporal (MST) cortex show heading selective responses to optic flow and planar direction selective responses to patches of local motion. We recorded MST neuronal responses to a 90 ϫ 90°optic flow display and to a 3 ϫ 3 array of local motion patches covering the same area. Our goal was to test the hypothesis that the optic flow responses reflect the sum of the local motion responses. The local motion responses of each neuron were modeled as mixtures of Gaussians, combining the effects of two Gaussian response functions derived using a genetic algorithm, and then used to predict that neuron's optic flow responses. Some neurons showed good correspondence between local motion models and optic flow responses, others showed substantial differences. We used the genetic algorithm to modulate the relative strength of each local motion segment's responses to accommodate interactions between segments that might modulate their relative efficacy during co-activation by global patterns of optic flow. These gain modulated models showed uniformly better fits to the optic flow responses, suggesting that coactivation of receptive field segments alters neuronal response properties. We tested this hypothesis by simultaneously presenting local motion stimuli at two different sites. These two-segment stimuli revealed that interactions between response segments have direction and location specific effects that can account for aspects of optic flow selectivity. We conclude that MST's optic flow selectivity reflects dynamic interactions between spatially distributed local planar motion response mechanisms.

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Section: Gain Modulation By Optic Flowsupporting

confidence: 71%

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

Page

Gaborski

et al. 2010

Journal of Neurophysiology

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“…An earlier model (Sereno and Sereno, 1991;Z hang et al, 1993) showed how unsupervised Hebbian synapses can yield weight patterns resembling the combinations of motion components that neurons in area MST prefer, and a recent model (Wang, 1995) demonstrated similar results for a competitive optimization rule; the inputs to these Figure 2. Distributed representation of velocity.…”

Section: Methodsmentioning

confidence: 86%

“…This novel approach, which has produced an alternative interpretation for what the responses of MST neurons are encoding, has two primary advantages. First, it has been successful on more realistic inputs than the previous optimization models (Sereno and Sereno, 1991;Zhang et al, 1993;Wang, 1995), and it has demonstrated that the neurophysiologically determined MSTd response properties are consistent with the statistics of complex motion images. Second, this approach expands the potential role for the information represented in MST to include other aspects of optic flow field analysis and other behaviors in addition to heading detection.…”

Section: Discussionmentioning

confidence: 99%

A Model for Encoding Multiple Object Motions and Self-Motion in Area MST of Primate Visual Cortex

Zemel

Sejnowski

1998

J. Neurosci.

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Many cells in the dorsal part of the medial superior temporal (MST) region of visual cortex respond selectively to specific combinations of expansion/contraction, translation, and rotation motions. Previous investigators have suggested that these cells may respond selectively to the flow fields generated by self-motion of an observer. These patterns can also be generated by the relative motion between an observer and a particular object. We explored a neurally constrained model based on the hypothesis that neurons in MST partially segment the motion fields generated by several independently moving objects. Inputs to the model were generated from sequences of ray-traced images that simulated realistic motion situations, combining observer motion, eye movements, and independent object motions. The input representation was based on the response properties of neurons in the middle temporal area (MT), which provides the primary input to area MST. After applying an unsupervised optimization technique, the units became tuned to patterns signaling coherent motion, matching many of the known properties of MST cells. The results of this model are consistent with recent studies indicating that MST cells primarily encode information concerning the relative three-dimensional motion between objects and the observer.

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“…In theory, it is possible to build an MST-like neuron from MT-like local motion inputs, even with position-invariance properties (Saito et al, 1986;Poggio et al, 1991;Sereno and Sereno, 1991;Z hang et al, 1993). Tuning to spiral motion was predicted based on Hebbian learning of optic flow patterns (Z hang et al, 1993) and by other unsupervised learning algorithms (Wang, 1995; Z emel and Sejnowski, 1998).…”

Section: Three-dimensional Object Motion Spiral Motionmentioning

confidence: 99%

A Theory of Geometric Constraints on Neural Activity for Natural Three-Dimensional Movement

Zhang

Sejnowski

1999

J. Neurosci.

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Although the orientation of an arm in space or the static view of an object may be represented by a population of neurons in complex ways, how these variables change with movement often follows simple linear rules, reflecting the underlying geometric constraints in the physical world. A theoretical analysis is presented for how such constraints affect the average firing rates of sensory and motor neurons during natural movements with low degrees of freedom, such as a limb movement and rigid object motion. When applied to nonrigid reaching arm movements, the linear theory accounts for cosine directional tuning with linear speed modulation, predicts a curl-free spatial distribution of preferred directions, and also explains why the instantaneous motion of the hand can be recovered from the neural population activity. For three-dimensional motion of a rigid object, the theory predicts that, to a first approximation, the response of a sensory neuron should have a preferred translational direction and a preferred rotation axis in space, both with cosine tuning functions modulated multiplicatively by speed and angular speed, respectively. Some known tuning properties of motion-sensitive neurons follow as special cases. Acceleration tuning and nonlinear speed modulation are considered in an extension of the linear theory. This general approach provides a principled method to derive mechanism-insensitive neuronal properties by exploiting the inherently low dimensionality of natural movements.

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A Simple Competitive Account of Some Response Properties of Visual Neurons in Area MSTd

Cited by 19 publications

References 19 publications

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

Receptive Field Dynamics Underlying MST Neuronal Optic Flow Selectivity

A Model for Encoding Multiple Object Motions and Self-Motion in Area MST of Primate Visual Cortex

A Theory of Geometric Constraints on Neural Activity for Natural Three-Dimensional Movement

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