Eye position information is used to compensate the consequences of ocular torsion on V1 receptive fields

Daddaoua, Nabil; Dicke, Peter W.; Thier, Peter

doi:10.1038/ncomms4047

Cited by 18 publications

(18 citation statements)

References 24 publications

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“…Neural recordings in monkeys confirm that V1 neurons represent the visual scene in egocentric coordinates (Daddaoua et al, 2014). This finding conflicts with several earlier studies conducted in cats, which suggested that some V1 neurons might represent the visual scene relative to gravity (Denney and Adorjanti, 1972; Horn et al, 1972; Tomko et al, 1981).…”

Section: Achieving Gravity-centered Visual Perceptionmentioning

confidence: 97%

Gravity estimation and verticality perception

Dakin

Rosenberg

2018

Handbook of Clinical Neurology

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Gravity is a defining force that governs the evolution of mechanical forms, shapes and anchors our perception of the environment, and imposes fundamental constraints on our interactions with the world. Within the animal kingdom, humans are relatively unique in having evolved a vertical, bipedal posture. Although a vertical posture confers numerous benefits, it also renders us less stable than quadrupeds, increasing susceptibility to falls. The ability to accurately and precisely estimate our orientation relative to gravity is therefore of utmost importance. Here we review sensory information and computational processes underlying gravity estimation and verticality perception. Central to gravity estimation and verticality perception is multisensory cue combination, which serves to improve the precision of perception and resolve ambiguities in sensory representations by combining information from across the visual, vestibular, and somatosensory systems. We additionally review experimental paradigms for evaluating verticality perception, and discuss how particular disorders affect the perception of upright. Together, the work reviewed here highlights the critical role of multisensory cue combination in gravity estimation, verticality perception, and creating stable gravity-centered representations of our environment.

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Section: Achieving Gravity-centered Visual Perceptionmentioning

confidence: 97%

Gravity estimation and verticality perception

Dakin

Rosenberg

2018

Handbook of Clinical Neurology

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“…Another form is ocular counter-roll evoked by tilting the head about the roll axis. A specific subset of V1 neurons uses information on counter-roll to render visual orientation and position in a head-centered FOR , thereby helping to establish a world-centered representation of the visual world, invariant to roll tilt of the head and body 18 . As ocular counter-roll is small, its contribution to a tilt-independent percept of the vertical is negligible.…”

Section: Discussionmentioning

confidence: 99%

V1 neurons encode the perceptual compensation of “false torsion” arising from Listing’s law

Khazali

Thier

2018

Preprint

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11We try to deploy the retinal fovea to optimally scrutinize an object of interest by directing our eyes 12 to it. Horizontal and vertical components of these fixation eye movements are determined by the 13 object's location. However, fixation eye movements also involve a torsional component, which 14 according to Listing's law is fully determined by the 2D eye position acquired. According to Von 15 Helmholtz knowledge of the torsion provided by this law alleviates the perceptual interpretation 16 of the image tilt that changes with fixation, a view supported by psychophysical experiments he 17 pioneered. We address the question where and how Listing's law is implemented in the visual 18 system and we show that neurons in monkey area V1 use knowledge of torsion to compensate the 19 image tilt associated with specific eye positions as set by Listing's law. 20 65the actual experiment starting after several weeks of intensive training. Here we stopped presenting 66 the visual reference line, assuming that the monkey would now use an internal reference 67 corresponding to his subjective vertical. The results described below support the conclusion that 68

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“…In certain regions such as parietal area 7a and LIP, the gain modulation is usually a monotonic function of the relevant bodily posture, 22 such as eye or head position. For example, eye-position modulation often takes the form 23 of a linear or saturating function of eye-position, which multiplicatively modulates the 24 underlying Gaussian retinotopic receptive field of a visually responsive neuron [6]. 25 While in other areas, such as V6A, there can be a higher proportion of retinotopic 26 visual neurons with peaked eye-position gain fields [13].…”

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confidence: 99%

“…Moreover, even if the model has its 253 synaptic connectivity perfectly prewired to perform a coordinate transformation from 254 eye-centred input neurons with monotonic gain modulation to head-centred output 255 neurons, subsequently introducing plasticity into the synaptic connections with the 256 standard trace learning rule quickly degrades the synaptic connectivity and eventually 257 leads to elimination of the head-centred output responses. Since eye-centred visual 258 neurons with monotonic eye-position gain modulation are so common in the dorsal 259 visual pathway [7,23,24], it is an important challenge to show how efferent synaptic 260 connections from these neurons may self-organise to produce head-centred visual 261 responses in a subpopulation of postsynaptic receiving neurons. A subsequent analysis 262 of the nature of the failure of the self-organisation of the synaptic connectivities led us 263 to explore the performance of the model with a variety of modified, yet still biologically 264 plausible, more powerful versions of the standard trace learning rule.…”

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confidence: 99%

Self-organising coordinate transformation with peaked and monotonic gain modulation in the primate dorsal visual pathway

Navarro

Mender

Smithson

et al. 2018

Preprint

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We study a self-organising neural network model of how visual representations in the primate dorsal visual pathway are transformed from an eye-centred to head-centred frame of reference. The model has previously been shown to robustly develop head-centred output neurons with a standard trace learning rule [1], but only under limited conditions. Specifically it fails when incorporating visual input neurons with monotonic gain modulation by eye-position. Since eye-centred neurons with monotonic gain modulation are so common in the dorsal visual pathway, it is an important challenge to show how efferent synaptic connections from these neurons may self-organise to produce head-centred responses in a subpopulation of postsynaptic neurons. We show for the first time how a variety of modified, yet still biologically plausible, versions of the standard trace learning rule enable the model to perform a coordinate transformation from eye-centred to head-centred reference frames when the visual input neurons have monotonic gain modulation by eye-position. Author summaryCoordinate transformations are an essential aspect of behaviour. For instance, sensory information encoded in the coordinates of the retina needs to be transformed to relevant coordinates for planning and movement. Particularly, head-centred coordinates are essential for accurate motor behaviours and required to compute more complex coordinate transformations for sensorimotor integration [2]. Head-centred coordinates are obtained by combining information about the the retinal location of visual stimuli and the position of the eye. Previous work did not address the influence of different forms of gain modulation by eye position, albeit a variety of forms being widely reported for several cortical areas. Here we show how a biologically plausible model that successfully self-organised head-centred responses [1,3] fails when the visual input units have a commonly observed form of eye-position gain modulation, i.e. monotonic modulation. Our work makes an important contribution to understanding how head-centred responses may develop in the brain through an unsupervised process of visually-guided learning using a set of more sophisticated, and yet still biologically

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Eye position information is used to compensate the consequences of ocular torsion on V1 receptive fields

Cited by 18 publications

References 24 publications

Gravity estimation and verticality perception

Gravity estimation and verticality perception

V1 neurons encode the perceptual compensation of “false torsion” arising from Listing’s law

Self-organising coordinate transformation with peaked and monotonic gain modulation in the primate dorsal visual pathway

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