A Neural Network for the Processing of Optic Flow from Ego-Motion in Man and Higher Mammals

Lappe, Markus; Rauschecker, Josef P.

doi:10.1162/neco.1993.5.3.374

Cited by 148 publications

(120 citation statements)

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“…Finally, MST and VIP neurons also respond to spiraling motion patterns (Graziano et al 1994;Schaafsma and Duysens 1996). It was suggested that such sensitivity might be related to a visual mechanism for heading estimation during eye movements that analyzes the distorted structure of the retinal flow induced by tracking eye movements and that functions as a backup for extraretinal processes (Lappe and Rauschecker 1993;Perrone and Stone 1994). The data from our current study on area VIP as well as our data on MST (Bremmer et al 2010) provide strong evidence in support of this hypothesis.…”

Section: Discussionsupporting

confidence: 76%

“…The receptive field structure of VIP neurons is not fully understood but is known to be complex (Chen et al 2014;Schaafsma and Duysens 1996). The existence of neuronal mechanisms that determine heading visually from distorted flow fields has been proposed in several neural models for heading detection (Beintema and Van den Berg 1998;Lappe and Rauschecker 1993;Perrone and Stone 1994). Curiously, these models share the prediction of a bicircular receptive field (RF) structure (Beintema et al 2004).…”

Section: Discussionmentioning

confidence: 99%

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Visual selectivity for heading in the macaque ventral intraparietal area

Kaminiarz

Schlack²,

Hoffmann

et al. 2014

Journal of Neurophysiology

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Kaminiarz A, Schlack A, Hoffmann KP, Lappe M, Bremmer F. Visual selectivity for heading in the macaque ventral intraparietal area. J Neurophysiol 112: 2470 -2480, 2014. First published August 13, 2014 doi:10.1152/jn.00410.2014.-The patterns of optic flow seen during self-motion can be used to determine the direction of one's own heading. Tracking eye movements which typically occur during everyday life alter this task since they add further retinal image motion and (predictably) distort the retinal flow pattern. Humans employ both visual and nonvisual (extraretinal) information to solve a heading task in such case. Likewise, it has been shown that neurons in the monkey medial superior temporal area (area MST) use both signals during the processing of self-motion information. In this article we report that neurons in the macaque ventral intraparietal area (area VIP) use visual information derived from the distorted flow patterns to encode heading during (simulated) eye movements. We recorded responses of VIP neurons to simple radial flow fields and to distorted flow fields that simulated self-motion plus eye movements. In 59% of the cases, cell responses compensated for the distortion and kept the same heading selectivity irrespective of different simulated eye movements. In addition, response modulations during real compared with simulated eye movements were smaller, being consistent with reafferent signaling involved in the processing of the visual consequences of eye movements in area VIP. We conclude that the motion selectivities found in area VIP, like those in area MST, provide a way to successfully analyze and use flow fields during self-motion and simultaneous tracking movements.self-motion; primate; parietal cortex; eye movements SELF-MOTION THROUGH AN ENVIRONMENT induces visual, vestibular, tactile, and auditory signals. Neurophysiological research over the last two decades has shown in the animal model, i.e., the macaque monkey, how these signals interact to enhance and disambiguate the perception of heading during self-motion. Two areas of the primate extrastriate and parietal cortex proved to be of specific importance in this context, i.e., the medial superior temporal area (area MST) and the ventral intraparietal area (area VIP). Neurons in area MST respond to visual and vestibular self-motion signals, and their causal role in heading perception has been confirmed (Bremmer et al.

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Section: Discussionsupporting

confidence: 76%

Section: Discussionmentioning

confidence: 99%

Visual selectivity for heading in the macaque ventral intraparietal area

Kaminiarz

Schlack²,

Hoffmann

et al. 2014

Journal of Neurophysiology

Self Cite

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“…The subspace algorithm solves for the global minimum in an error function, but biologically plausible synaptic learning laws only make use of local information about cell activation levels. Lappe and Rauschecker (1993) model the MTMSTd network by constructing cosine-tuned motion detectors in MT that produce a heading response in MSTd through precomputed weights. The weights themselves embed components from Heeger and Jepson's subspace solution.…”

Section: Discussionmentioning

confidence: 99%

A neural model of visually guided steering, obstacle avoidance, and route selection.

Elder¹,

Grossberg²,

Mingolla³

2009

Journal of Experimental Psychology: Human Perception and Perfor

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A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Key Words: Heading Perception, Steering, Optic Flow, Obstacle, Goal, Pursuit Eye Movement, Gain Fields, Peak Shift, V2, MT, MST, VIP, LIP 3 IntroductionMany important steering tasks are guided by visual information, including walking through a cluttered environment (Fajen & Warren, 2003), driving (Land & Horwood, 1995;Hildreth, Beusmans, Boer, & Royden, 2000;Wallis, Chatziastros, & Bülthoff, 2002), vehicle braking (Lee, 1976, piloting an aircraft (Gibson, Olum, & Rosenblatt, 1955;Beall & Loomis, 1997), and intercepting a moving target on foot (Fajen & Warren, 2004). Steering through a cluttered environment involves the selection of a path that avoids obstacles and simultaneously approaches the intended goal. Human steering is guided by visual information about the spatial layout of the environment and the direction of self-motion through the environment, as well as proprioceptive feedback and information from other sensory systems. In particular, the visual system provides information about the relative positions of the goal object and obstacles in the environment.Movement through the world creates a full-field pattern of motion on the retina, called optic flow, which contains information about the direction of self-motion, or heading (Gibson, 1950). In principle, optic flow can be used to compute heading (Longuet-Higgins & Prazdny, 1980). During translational movement of the eye, the optic flow field contains a singularity, called the focus of expansion, which specifies the direction of heading in the absence of an eye rotation.Figures 1 & 2 Whereas optic flow relates observer motion to the available visual stimuli, recent research clarifies how visual information governs the dynamics of human navigational behavior. Fajen and Warren (2003) studied the dynamics of human steering behavior in simple goal approach and obstacle avoidance tasks using an immersive virtual reality system. They found that human performa...

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“…If the flow field can be decomposed into these two contributions, then the focus of the expansion field corresponds to the direction of heading. This is not an easy computational problem, but several researchers have suggested various algorithms and explanations of how the nervous system might perform this task using retinal cues (Lounguet-Higgins & Prazdny 1980, Koenderink & van Doorn 1981, Rieger & Lawton 1985Heeger & Jepson 1990, Lappe & Rauschecker 1993, Perrone & Stone 1994. Royden et al (1992) recently showed that the eye movement itself generates a signal, presumably an efference copy of the pursuit command, that can be used to solve this problem.…”

Section: Visual Motion and Pursuitmentioning

confidence: 99%

Multimodal Representation of Space in the Posterior Parietal Cortex and Its Use in Planning Movements

Andersen

Snyder

Bradley

et al. 1997

Annu. Rev. Neurosci.

1,269

637

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Recent experiments are reviewed that indicate that sensory signals from many modalities, as well as efference copy signals from motor structures, converge in the posterior parietal cortex in order to code the spatial locations of goals for movement. These signals are combined using a specific gain mechanism that enables the different coordinate frames of the various input signals to be combined into common, distributed spatial representations. These distributed representations can be used to convert the sensory locations of stimuli into the appropriate motor coordinates required for making directed movements. Within these spatial representations of the posterior parietal cortex are neural activities related to higher cognitive functions, including attention. We review recent studies showing that the encoding of intentions to make movements is also among the cognitive functions of this area.

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A Neural Network for the Processing of Optic Flow from Ego-Motion in Man and Higher Mammals

Cited by 148 publications

References 31 publications

Visual selectivity for heading in the macaque ventral intraparietal area

Visual selectivity for heading in the macaque ventral intraparietal area

A neural model of visually guided steering, obstacle avoidance, and route selection.

Multimodal Representation of Space in the Posterior Parietal Cortex and Its Use in Planning Movements

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