Literature DB >> 1556602

Visuomotor transformations underlying arm movements toward visual targets: a neural network model of cerebral cortical operations.

Y Burnod1, P Grandguillaume, I Otto, S Ferraina, P B Johnson, R Caminiti.   

Abstract

We propose a biologically realistic neural network that computes coordinate transformations for the command of arm reaching movements in 3-D space. This model is consistent with anatomical and physiological data on the cortical areas involved in the command of these movements. Studies of the neuronal activity in the motor (Georgopoulos et al., 1986; Schwartz et al., 1988; Caminiti et al., 1990a) and premotor (Caminiti et al., 1990b, 1991) cortices of behaving monkeys have shown that the activity of individual arm-related neurons is broadly tuned around a preferred direction of movements in 3-D space. Recent data demonstrate that in both frontal areas (Caminiti et al., 1990a,b, 1991) these cell preferred directions rotate with the initial position of the arm. Furthermore, the rotation of the population of preferred directions precisely corresponds to the rotation of the arm in space. The neural network model computes the motor command by combining the visual information about movement trajectory with the kinesthetic information concerning the orientation of the arm in space. The appropriate combination, learned by the network from spontaneous movement, can be approximated by a bilinear operation that can be interpreted as a projection of the visual information on a reference frame that rotates with the arm. This bilinear combination implies that neural circuits converging on a single neuron in the motor and premotor cortices can learn and generalize the appropriate command in a 2-D subspace but not in the whole 3-D space. However, the uniform distribution of cell preferred directions in these frontal areas can explain the computation of the correct solution by a population of cortical neurons. The model is consistent with the existing neurophysiological data and predicts how visual and somatic information can be combined in the different processing steps of the visuomotor transformation subserving visual reaching.

Mesh:

Year:  1992        PMID: 1556602      PMCID: PMC6575802     

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


  24 in total

1.  Spatial generalization from learning dynamics of reaching movements.

Authors:  R Shadmehr; Z M Moussavi
Journal:  J Neurosci       Date:  2000-10-15       Impact factor: 6.167

2.  The representations of reach endpoints in posterior parietal cortex depend on which hand does the reaching.

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

3.  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

4.  Optimal sensorimotor integration in recurrent cortical networks: a neural implementation of Kalman filters.

Authors:  Sophie Denève; Jean-René Duhamel; Alexandre Pouget
Journal:  J Neurosci       Date:  2007-05-23       Impact factor: 6.167

5.  Reaching in depth: hand position dominates over binocular eye position in the rostral superior parietal lobule.

Authors:  Stefano Ferraina; Emiliano Brunamonti; Maria Assunta Giusti; Stefania Costa; Aldo Genovesio; Roberto Caminiti
Journal:  J Neurosci       Date:  2009-09-16       Impact factor: 6.167

6.  Vector reconstruction from firing rates.

Authors:  E Salinas; L F Abbott
Journal:  J Comput Neurosci       Date:  1994-06       Impact factor: 1.621

7.  Responses of Purkinje cells in the cerebellar anterior vermis to off-vertical axis rotation.

Authors:  D Manzoni; P Andre; O Pompeiano
Journal:  Pflugers Arch       Date:  1995-12       Impact factor: 3.657

8.  Motor Cortex Embeds Muscle-like Commands in an Untangled Population Response.

Authors:  Abigail A Russo; Sean R Bittner; Sean M Perkins; Jeffrey S Seely; Brian M London; Antonio H Lara; Andrew Miri; Najja J Marshall; Adam Kohn; Thomas M Jessell; Laurence F Abbott; John P Cunningham; Mark M Churchland
Journal:  Neuron       Date:  2018-02-01       Impact factor: 17.173

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.  Repeated practice of a Go/NoGo visuomotor task induces neuroplastic change in the human posterior parietal cortex: an MEG study.

Authors:  Kazuhiro Sugawara; Hideaki Onishi; Koya Yamashiro; Toshio Soma; Mineo Oyama; Hikari Kirimoto; Hiroyuki Tamaki; Hiroatsu Murakami; Shigeki Kameyama
Journal:  Exp Brain Res       Date:  2013-02-28       Impact factor: 1.972

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.