Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

Browning, N. Andrew; Grossberg, Stephen; Mingolla, Ennio

doi:10.1016/j.neunet.2009.05.007

Cited by 42 publications

(16 citation statements)

References 99 publications

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“…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 nulling 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).…”

Section: Methodsmentioning

confidence: 99%

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

Layton

Fajen

2016

PLoS Comput Biol

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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.

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Section: Methodsmentioning

confidence: 99%

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

Layton

Fajen

2016

PLoS Comput Biol

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“…The winning unit signals the path curvature through its pattern selectivity in spiral space. As noted in other computational studies [51]–[53], [57], broad activation in the network could implicate a greater degree of uncertainty about the path curvature and the dynamical competitive interactions require longer to resolve a high confidence solution. We configured model MSTd with a single set of parameters, but it is possible in vivo that different subpopulations exhibit differential response latencies [63].…”

Section: Discussionmentioning

confidence: 71%

“…In this paper, we do not model retinal input, but rather use analytical equations to model the vector-based optic flow representation in V1 [51]. A prior version of the model demonstrates how retinal inputs are processed through neural circuits to generate those representations [52], [53] …”

Section: Methodsmentioning

confidence: 99%

A Unified Model of Heading and Path Perception in Primate MSTd

Layton

Browning

2014

PLoS Comput Biol

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Self-motion, steering, and obstacle avoidance during navigation in the real world require humans to travel along curved paths. Many perceptual models have been proposed that focus on heading, which specifies the direction of travel along straight paths, but not on path curvature, which humans accurately perceive and is critical to everyday locomotion. In primates, including humans, dorsal medial superior temporal area (MSTd) has been implicated in heading perception. However, the majority of MSTd neurons respond optimally to spiral patterns, rather than to the radial expansion patterns associated with heading. No existing theory of curved path perception explains the neural mechanisms by which humans accurately assess path and no functional role for spiral-tuned cells has yet been proposed. Here we present a computational model that demonstrates how the continuum of observed cells (radial to circular) in MSTd can simultaneously code curvature and heading across the neural population. Curvature is encoded through the spirality of the most active cell, and heading is encoded through the visuotopic location of the center of the most active cell's receptive field. Model curvature and heading errors fit those made by humans. Our model challenges the view that the function of MSTd is heading estimation, based on our analysis we claim that it is primarily concerned with trajectory estimation and the simultaneous representation of both curvature and heading. In our model, temporal dynamics afford time-history in the neural representation of optic flow, which may modulate its structure. This has far-reaching implications for the interpretation of studies that assume that optic flow is, and should be, represented as an instantaneous vector field. Our results suggest that spiral motion patterns that emerge in spatio-temporal optic flow are essential for guiding self-motion along complex trajectories, and that cells in MSTd are specifically tuned to extract complex trajectory estimation from flow.

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“…The second process separates objects-including goal objects and obstacles-from each other and the background using their relative motion to enable tracking of a goal without bumping into obstacles. Browning et al (2009b) and Elder et al (2009) review alternative models and data about tracking.…”

Section: Steering During Optic Flow Navigationmentioning

confidence: 99%

“…A peak shift due to the negative Gaussian of an obstacle prevents a collision with the obstacle without losing the information that is coded in the sum of the goal and heading Gaussians of how to reach the goal [adapted with permission fromBrowning et al (2009b)]. …”

mentioning

confidence: 99%

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

Cortical dynamics of navigation and steering in natural scenes: Motion-based object segmentation, heading, and obstacle avoidance

Cited by 42 publications

References 99 publications

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

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

A Unified Model of Heading and Path Perception in Primate MSTd

Developmental Designs and Adult Functions of Cortical Maps in Multiple Modalities: Perception, Attention, Navigation, Numbers, Streaming, Speech, and Cognition

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