Literature DB >> 9391024

A neural model of multimodal adaptive saccadic eye movement control by superior colliculus.

S Grossberg1, K Roberts, M Aguilar, D Bullock.   

Abstract

How does the saccadic movement system select a target when visual, auditory, and planned movement commands differ? How do retinal, head-centered, and motor error coordinates interact during the selection process? Recent data on superior colliculus (SC) reveal a spreading wave of activation across buildup cells the peak activity of which covaries with the current gaze error. In contrast, the locus of peak activity remains constant at burst cells, whereas their activity level decays with residual gaze error. A neural model answers these questions and simulates burst and buildup responses in visual, overlap, memory, and gap tasks. The model also simulates data on multimodal enhancement and suppression of activity in the deeper SC layers and suggests a functional role for NMDA receptors in this region. In particular, the model suggests how auditory and planned saccadic target positions become aligned and compete with visually reactive target positions to select a movement command. For this to occur, a transformation between auditory and planned head-centered representations and a retinotopic target representation is learned. Burst cells in the model generate teaching signals to the spreading wave layer. Spreading waves are produced by corollary discharges that render planned and visually reactive targets dimensionally consistent and enable them to compete for attention to generate a movement command in motor error coordinates. The attentional selection process also helps to stabilize the map-learning process. The model functionally interprets cells in the superior colliculus, frontal eye field, parietal cortex, mesencephalic reticular formation, paramedian pontine reticular formation, and substantia nigra pars reticulata.

Entities:  

Mesh:

Year:  1997        PMID: 9391024      PMCID: PMC6573406     

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


  73 in total

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Journal:  Vision Res       Date:  1996-06       Impact factor: 1.886

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Authors:  M Rucci; G Tononi; G M Edelman
Journal:  J Neurosci       Date:  1997-01-01       Impact factor: 6.167

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Authors:  D L Sparks; L E Mays
Journal:  Brain Res       Date:  1980-05-19       Impact factor: 3.252

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9.  Importance of NMDA receptors for multimodal integration in the deep layers of the cat superior colliculus.

Authors:  K E Binns; T E Salt
Journal:  J Neurophysiol       Date:  1996-02       Impact factor: 2.714

Review 10.  Normal and amnesic learning, recognition and memory by a neural model of cortico-hippocampal interactions.

Authors:  G A Carpenter; S Grossberg
Journal:  Trends Neurosci       Date:  1993-04       Impact factor: 13.837

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  14 in total

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2.  Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements.

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3.  Noise-rearing disrupts the maturation of multisensory integration.

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4.  Embodied inference and spatial cognition.

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5.  Learning the optimal control of coordinated eye and head movements.

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Journal:  PLoS Comput Biol       Date:  2011-11-03       Impact factor: 4.475

6.  Perceptions as hypotheses: saccades as experiments.

Authors:  Karl Friston; Rick A Adams; Laurent Perrinet; Michael Breakspear
Journal:  Front Psychol       Date:  2012-05-28

7.  Active inference and oculomotor pursuit: the dynamic causal modelling of eye movements.

Authors:  Rick A Adams; Eduardo Aponte; Louise Marshall; Karl J Friston
Journal:  J Neurosci Methods       Date:  2015-01-10       Impact factor: 2.390

8.  Active inference, eye movements and oculomotor delays.

Authors:  Laurent U Perrinet; Rick A Adams; Karl J Friston
Journal:  Biol Cybern       Date:  2014-08-16       Impact factor: 2.086

9.  Smooth pursuit and visual occlusion: active inference and oculomotor control in schizophrenia.

Authors:  Rick A Adams; Laurent U Perrinet; Karl Friston
Journal:  PLoS One       Date:  2012-10-26       Impact factor: 3.240

10.  What is value-accumulated reward or evidence?

Authors:  Karl Friston; Rick Adams; Read Montague
Journal:  Front Neurorobot       Date:  2012-11-02       Impact factor: 2.650

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