Literature DB >> 10936913

Models of the posterior parietal cortex which perform multimodal integration and represent space in several coordinate frames.

J Xing1, R A Andersen.   

Abstract

Many neurons in the posterior-parietal cortex (PPC) have saccadic responses to visual and auditory targets. The responses are modulated by eye position and head position. These findings suggest that PPC integrates multisensory inputs and may provide information about saccadic targets represented in different coordinate frames. In addition to an eyecentered output representation, PPC may also project to brain areas which contain head-centered and body-centered representations of the space. In this report, possible coordinate transformations in PPC were examined by comparing several sets of models of PPC, each having different representations in the output layer: (i) an eye-centered map only; (ii) a head-centered map only; (iii) an eye-centered map and a head-centered map; and (iv) an eye-centered map, a head-centered map, and a body-centered map. These output maps correctly encoded saccades to visual and auditory targets through training. The units in the hidden layers of the models exhibited the following properties: (1) The units had gain fields (GFs) for eye position, and also for head position if the model had a body-centered output representation; (2) As the result of the GF and the nonlinear activation function of the units, the hidden layers often employed "intermediate" coding, e.g., the hidden units coded targets partially in eye-centered coordinates and, partially, in head-centered coordinates; (3) Different types of coordinate transformations in these models were carried out by different relationships between the receptive fields (RFs) and the GFs of the hidden units; and (4) The properties of PPC neurons are in better accordance with the hidden units of the models that had multiple-output representations than the models that had only one single-output representation. In conclusion, the results show that the GF is an effective mechanism for performing coordinate transformations. The models also suggest that neurons with intermediate coding are to be expected in the process of coordinate transformations.

Mesh:

Year:  2000        PMID: 10936913     DOI: 10.1162/089892900562363

Source DB:  PubMed          Journal:  J Cogn Neurosci        ISSN: 0898-929X            Impact factor:   3.225


  58 in total

1.  Functional anatomy of nonvisual feedback loops during reaching: a positron emission tomography study.

Authors:  M Desmurget; H Gréa; J S Grethe; C Prablanc; G E Alexander; S T Grafton
Journal:  J Neurosci       Date:  2001-04-15       Impact factor: 6.167

2.  The composite neuron: a realistic one-compartment Purkinje cell model suitable for large-scale neuronal network simulations.

Authors:  A D Coop; G N Reeke
Journal:  J Comput Neurosci       Date:  2001 Mar-Apr       Impact factor: 1.621

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Authors:  Steve W C Chang; Lawrence H Snyder
Journal:  J Neurophysiol       Date:  2012-02-01       Impact factor: 2.714

4.  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

5.  Do you know where your arm is if you think your head has moved?

Authors:  Joanna J Knox; Michel W Coppieters; Paul W Hodges
Journal:  Exp Brain Res       Date:  2006-03-25       Impact factor: 1.972

6.  Spatial heterogeneity of cortical receptive fields and its impact on multisensory interactions.

Authors:  Brian N Carriere; David W Royal; Mark T Wallace
Journal:  J Neurophysiol       Date:  2008-02-20       Impact factor: 2.714

7.  Mechanism of gain modulation at single neuron and network levels.

Authors:  M Brozović; L F Abbott; R A Andersen
Journal:  J Comput Neurosci       Date:  2008-01-23       Impact factor: 1.621

8.  Sensory-guided motor tasks benefit from mental training based on serial prediction.

Authors:  Ellen Binder; Klara Hagelweide; Ling E Wang; Katja Kornysheva; Christian Grefkes; Gereon R Fink; Ricarda I Schubotz
Journal:  Neuropsychologia       Date:  2013-12-07       Impact factor: 3.139

9.  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

10.  Coding of the reach vector in parietal area 5d.

Authors:  Lindsay R Bremner; Richard A Andersen
Journal:  Neuron       Date:  2012-07-26       Impact factor: 17.173

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