Literature DB >> 7472409

Transfer of coded information from sensory to motor networks.

E Salinas1, L F Abbott.   

Abstract

During sensory-guided motor tasks, information must be transferred from arrays of neurons coding target location to motor networks that generate and control movement. We address two basic questions about this information transfer. First, what mechanisms assure that the different neural representations align properly so that activity in the sensory network representing target location evokes a motor response generating accurate movement toward the target? Coordinate transformations may be needed to put the sensory data into a form appropriate for use by the motor system. For example, in visually guided reaching the location of a target relative to the body is determined by a combination of the position of its image on the retina and the direction of gaze. What assures that the motor network responds to the appropriate combination of sensory inputs corresponding to target position in body- or arm-centered coordinates? To answer these questions, we model a sensory network coding target position and use it to drive a similarly modeled motor network. To determine the actual motor response we use decoding methods that have been developed and verified in experimental work. We derive a general set of conditions on the sensory-to-motor synaptic connections that assure a properly aligned and transformed response. The accuracy of the response for different numbers of coding cells is computed. We show that development of the synaptic weights needed to generate the correct motor response can occur spontaneously through the observation of random movements and correlation-based synaptic modification. No error signal or external teaching is needed during this process. We also discuss nonlinear coordinate transformations and the presence of both shifting and nonshifting receptive fields in sensory/motor systems.

Mesh:

Year:  1995        PMID: 7472409      PMCID: PMC6578023     

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  70 in total

1.  A laterally interconnected neural architecture in MST accounts for psychophysical discrimination of complex motion patterns.

Authors:  S A Beardsley; L M Vaina
Journal:  J Comput Neurosci       Date:  2001 May-Jun       Impact factor: 1.621

2.  Fast remapping of sensory stimuli onto motor actions on the basis of contextual modulation.

Authors:  Emilio Salinas
Journal:  J Neurosci       Date:  2004-02-04       Impact factor: 6.167

Review 3.  Role of uncertainty in sensorimotor control.

Authors:  Robert J van Beers; Pierre Baraduc; Daniel M Wolpert
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2002-08-29       Impact factor: 6.237

4.  Perception-action and the Müller-Lyer illusion: amplitude or endpoint bias?

Authors:  Cheryl M Glazebrook; Victoria P Dhillon; Katherine M Keetch; James Lyons; Eric Amazeen; Daniel J Weeks; Digby Elliott
Journal:  Exp Brain Res       Date:  2005-01       Impact factor: 1.972

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

Authors:  Steve W C Chang; Lawrence H Snyder
Journal:  Proc Natl Acad Sci U S A       Date:  2010-04-07       Impact factor: 11.205

6.  Acquiring and adapting a novel audiomotor map in human grasping.

Authors:  Daniel Säfström; Benoni B Edin
Journal:  Exp Brain Res       Date:  2006-02-28       Impact factor: 1.972

7.  Calibration of visually guided reaching is driven by error-corrective learning and internal dynamics.

Authors:  Sen Cheng; Philip N Sabes
Journal:  J Neurophysiol       Date:  2007-01-03       Impact factor: 2.714

8.  Neural theory for the perception of causal actions.

Authors:  Falk Fleischer; Andrea Christensen; Vittorio Caggiano; Peter Thier; Martin A Giese
Journal:  Psychol Res       Date:  2012-04-26

9.  Computing vector differences using a gain field-like mechanism in monkey frontal eye field.

Authors:  Carlos R Cassanello; Vincent P Ferrera
Journal:  J Physiol       Date:  2007-05-17       Impact factor: 5.182

10.  Using a compound gain field to compute a reach plan.

Authors:  Steve W C Chang; Charalampos Papadimitriou; Lawrence H Snyder
Journal:  Neuron       Date:  2009-12-10       Impact factor: 17.173

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