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Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow.

Layton OW, Fajen BR - PLoS Comput. Biol. (2016)

Bottom Line: Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments.Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field.Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

ABSTRACT
Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model's heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

No MeSH data available.


Related in: MedlinePlus

Diagram of the model V1–MT+ motion detection microcircuit.V1 complex cells receive on-center/off-surround input from simple cells, broadly tuned to motion. Simple cells are grouped by complex cells parallel to their preferred direction of motion, and simple cells positioned perpendicular to the preferred direction inhibit the complex cell. MT+ units refine the motion estimates by grouping over a larger spatial extent, and sending feedback to inhibit complex cells with discrepant direction selectivities in the receptive field.
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pcbi.1004942.g013: Diagram of the model V1–MT+ motion detection microcircuit.V1 complex cells receive on-center/off-surround input from simple cells, broadly tuned to motion. Simple cells are grouped by complex cells parallel to their preferred direction of motion, and simple cells positioned perpendicular to the preferred direction inhibit the complex cell. MT+ units refine the motion estimates by grouping over a larger spatial extent, and sending feedback to inhibit complex cells with discrepant direction selectivities in the receptive field.

Mentions: The detection of motion direction occurs through a three stage process that corresponds to simple and complex cells in V1, and cells in area MT+ with excitatory surrounds (Fig 13). First, motion is detected by simple cells using a Reichardt or correlation-based mechanism based on the arrival of signals from LGN with different conduction delays and receptive field locations [71] (but see [73,74,82–84] for an alternative biological mechanism that relies on ing inhibition). The motion signal is refined through short-range feedforward on-center/off-surround pooling of simple cell activity by complex cells (Fig 13, bottom two panels). Finally, a feedback loop between V1 complex cells and MT+ cells disambiguates local motion signals (i.e. solves the aperture problem) through the spatial pooling of complex cells by units in MT+ tuned to the same motion direction and the suppression of complex cells tuned to dissimilar motion directions (Fig 13, top two panels).


Competitive Dynamics in MSTd: A Mechanism for Robust Heading Perception Based on Optic Flow.

Layton OW, Fajen BR - PLoS Comput. Biol. (2016)

Diagram of the model V1–MT+ motion detection microcircuit.V1 complex cells receive on-center/off-surround input from simple cells, broadly tuned to motion. Simple cells are grouped by complex cells parallel to their preferred direction of motion, and simple cells positioned perpendicular to the preferred direction inhibit the complex cell. MT+ units refine the motion estimates by grouping over a larger spatial extent, and sending feedback to inhibit complex cells with discrepant direction selectivities in the receptive field.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4920404&req=5

pcbi.1004942.g013: Diagram of the model V1–MT+ motion detection microcircuit.V1 complex cells receive on-center/off-surround input from simple cells, broadly tuned to motion. Simple cells are grouped by complex cells parallel to their preferred direction of motion, and simple cells positioned perpendicular to the preferred direction inhibit the complex cell. MT+ units refine the motion estimates by grouping over a larger spatial extent, and sending feedback to inhibit complex cells with discrepant direction selectivities in the receptive field.
Mentions: The detection of motion direction occurs through a three stage process that corresponds to simple and complex cells in V1, and cells in area MT+ with excitatory surrounds (Fig 13). First, motion is detected by simple cells using a Reichardt or correlation-based mechanism based on the arrival of signals from LGN with different conduction delays and receptive field locations [71] (but see [73,74,82–84] for an alternative biological mechanism that relies on ing inhibition). The motion signal is refined through short-range feedforward on-center/off-surround pooling of simple cell activity by complex cells (Fig 13, bottom two panels). Finally, a feedback loop between V1 complex cells and MT+ cells disambiguates local motion signals (i.e. solves the aperture problem) through the spatial pooling of complex cells by units in MT+ tuned to the same motion direction and the suppression of complex cells tuned to dissimilar motion directions (Fig 13, top two panels).

Bottom Line: Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments.Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field.Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

View Article: PubMed Central - PubMed

Affiliation: Department of Cognitive Science, Rensselaer Polytechnic Institute, Troy, New York, United States of America.

ABSTRACT
Human heading perception based on optic flow is not only accurate, it is also remarkably robust and stable. These qualities are especially apparent when observers move through environments containing other moving objects, which introduce optic flow that is inconsistent with observer self-motion and therefore uninformative about heading direction. Moving objects may also occupy large portions of the visual field and occlude regions of the background optic flow that are most informative about heading perception. The fact that heading perception is biased by no more than a few degrees under such conditions attests to the robustness of the visual system and warrants further investigation. The aim of the present study was to investigate whether recurrent, competitive dynamics among MSTd neurons that serve to reduce uncertainty about heading over time offer a plausible mechanism for capturing the robustness of human heading perception. Simulations of existing heading models that do not contain competitive dynamics yield heading estimates that are far more erratic and unstable than human judgments. We present a dynamical model of primate visual areas V1, MT, and MSTd based on that of Layton, Mingolla, and Browning that is similar to the other models, except that the model includes recurrent interactions among model MSTd neurons. Competitive dynamics stabilize the model's heading estimate over time, even when a moving object crosses the future path. Soft winner-take-all dynamics enhance units that code a heading direction consistent with the time history and suppress responses to transient changes to the optic flow field. Our findings support recurrent competitive temporal dynamics as a crucial mechanism underlying the robustness and stability of perception of heading.

No MeSH data available.


Related in: MedlinePlus