Computations for geometrically accurate visually guided reaching in 3-D space

Blohm, Gunnar; Crawford, J. Douglas

doi:10.1167/7.5.4

Cited by 79 publications

(119 citation statements)

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“…A feed-forward threelayer neural network trained using back-propogation accomplishes this using gaze-centered, gain-modulated nodes, not intermediate coding (11,12,14,16,17,30,36,37). Although it is true that neural networks can be designed to produce intermediate representations (15,17,26), it nonetheless behooves us to ask if intermediate representations might be artifactual.…”

Section: Resultsmentioning

confidence: 99%

Idiosyncratic and systematic aspects of spatial representations in the macaque parietal cortex

Chang

Snyder

2010

Proc. Natl. Acad. Sci. U.S.A.

114

121

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The sensorimotor transformations for visually guided reaching were originally thought to take place in a series of discrete transitions from one systematic frame of reference to the next with neurons coding location relative to the fixation position (gaze-centered) in occipital and posterior parietal areas, relative to the shoulder in dorsal premotor cortex, and in muscle-or jointbased coordinates in motor output neurons. Recent empirical and theoretical work has suggested that spatial encodings that use a range of idiosyncratic representations may increase computational power and flexibility. We now show that neurons in the parietal reach region use nonuniform and idiosyncratic frames of reference. We also show that these nonsystematic reference frames coexist with a systematic compound gain field that modulates activity proportional to the distance between the eyes and the hand. Thus, systematic and idiosyncratic signals may coexist within individual neurons.gain field | posterior parietal cortex | reference frame transformation W e are currently at a theoretical crossroads regarding how the brain computes motor commands from sensory information. The linear systems engineering tradition taught that neural circuits are assembled to compute particular transfer functions and that intermediate stages represent quantities with straightforward physical interpretations (1). At each stage, neurons encode information in a single frame of reference, which facilitates simple pooling. Each cell has a unique receptive field or preferred direction. This results in a distributed population code where each neuron encodes similar information in a similar manner. The vestibular-ocular reflex provides an example of this approach. Head rotation, measured by the vestibular semicircular canals using a canal-centered frame of reference, was believed to be transformed directly into oculomotor commands by virtue of appropriate synaptic weights on vestibular and oculomotor nuclei neurons (1). Visually guided reaching provides another example. Visual spatial information was originally thought to undergo a series of discrete transformations from a sensory (gaze-centered) frame of reference in the occipital and parietal cortices to hand-centered in the premotor cortex (extrinsic motor coordinates) and finally, to muscle commands in the primary motor cortex (intrinsic motor commands) (2-6). The parietal reach region (PRR) in the posterior parietal cortex (PPC) was seen as a discrete processing stage in which reachrelated spatial information was encoded using a uniform gazecentered reference frame and passed on to dorsal premotor cortex (PMd) (7,8).An alternative design borrows from connectionist principles and the field of artificial intelligence. In a trained neural network, the organizational principles may be obscure and individual nodes may encode information idiosyncratically (9, 10). Flexibility and computational power are increased when individual nodes code diverse, seemingly random permutations of the input. Subsequent processing ...

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Section: Resultsmentioning

confidence: 99%

Idiosyncratic and systematic aspects of spatial representations in the macaque parietal cortex

Chang

Snyder

2010

Proc. Natl. Acad. Sci. U.S.A.

114

121

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“…We also incorporated static and dynamic VOR to account for ocular counter-roll with different gains. Retinal geometry was modeled using spherical projections (see also Blohm and Crawford 2007). Using quaternion algebra, eye-in-head rotation can be described by the following quaternion q (Tweed 1997a) …”

Section: Discussionmentioning

confidence: 99%

“…A6). Finally we implemented the spherical projection geometry of the visual image onto the retina (Blohm and Crawford 2007;Crawford and Guitton 1997), which-together with Listing's law-resulted in the misalignment for the spatial and retinal axes in oblique eye positions. The predictions of this model will be described in more details in RESULTS. We proposed two extreme working hypotheses that should allow interpreting the experimental data.…”

Section: Modelmentioning

confidence: 99%

Visuomotor Velocity Transformations for Smooth Pursuit Eye Movements

Blohm

Lefèvre

2010

Journal of Neurophysiology

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Blohm G, Lefèvre P. Visuomotor velocity transformations for smooth pursuit eye movements. J Neurophysiol 104: 2103-2115, 2010. First published August 18, 2010 doi:10.1152/jn.00728.2009. Smooth pursuit eye movements are driven by retinal motion signals. These retinal motion signals are converted into motor commands that obey Listing's law (i.e., no accumulation of ocular torsion). The fact that smooth pursuit follows Listing's law is often taken as evidence that no explicit reference frame transformation between the retinal velocity input and the head-centered motor command is required. Such eye-position-dependent reference frame transformations between eye-and head-centered coordinates have been well-described for saccades to static targets. Here we suggest that such an eye (and head)-position-dependent reference frame transformation is also required for target motion (i.e., velocity) driving smooth pursuit eye movements. Therefore we tested smooth pursuit initiation under different three-dimensional eye positions and compared human performance to model simulations. We specifically tested if the ocular rotation axis changed with vertical eye position, if the misalignment of the spatial and retinal axes during oblique fixations was taken into account, and if ocular torsion (due to head roll) was compensated for. If no eye-position-dependent velocity transformation was used, the pursuit initiation should follow the retinal direction, independently of eye position; in contrast, a correct visuomotor velocity transformation would result in spatially correct pursuit initiation. Overall subjects accounted for all three components of the visuomotor velocity transformation, but we did observe differences in the compensatory gains between individual subjects. We concluded that the brain does perform a visuomotor velocity transformation but that this transformation was prone to noise and inaccuracies of the internal model.

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“…In recent years, much emphasis has been placed on the visuomotor transformations underlying reach planning (see Crawford et al 2011 for review) where it has been shown that both eye and head position are taken into account in the three-dimensional (3D) transformation of eyecentered visual signals into shoulder-centered motor commands (Blohm and Crawford 2007;Leclercq et al 2012).…”

Section: Soechting and Flanders 1989mentioning

confidence: 99%