Literature DB >> 1422090

Reentry and the problem of integrating multiple cortical areas: simulation of dynamic integration in the visual system.

G Tononi1, O Sporns, G M Edelman.   

Abstract

Studies of the cerebral cortex, particularly those of the visual system, demonstrate the existence of multiple, anatomically segregated and functionally specialized cortical areas. There is no evidence that these areas, which are linked by a network of reciprocal connections, are coordinated by a higher-order center. The visual image that we perceive, however, seems to be unified and coherent. In this article, we address the problem of integration posed by these observations. In an extension of our previous work, we develop a dynamic model of reentry. Reentry is a process of parallel and recursive signaling along ordered anatomical connections that achieves integration by giving rise to constructive and correlative properties within and among maps. We present and test a computer model simulating nine functionally segregated visual areas organized into three streams for form, color, and motion. The model receives visual input consisting of camera images of objects of different shapes and colors. We show the specialized response properties of the areas in the three streams. A computational strategy involving a phase variable is introduced to represent explicitly the dynamics of short-term temporal correlations among thousands of units distributed across different areas. We then illustrate constructive and correlative consequences of reentry within a system of reciprocal intra- and interareal connections by two examples taken from psychophysics: generation of form from motion and motion capture. The model solves the so-called "binding problem" through short-term correlations, which serve to link similar object features within a simulated cortical area and to bind multiple attributes of one or more objects across several areas, including a nontopographic one. Integration emerges from cooperative effects within and among the specialized areas. These effects lead to a simple output, a simulated foveation response, that is used as a basis for conditioning. Reward is mediated by the activation of a saliency system that is modeled on diffuse projection systems in the brain. As a result, the visual cortical model carries out foveation responses to input stimuli that require the dynamic conjunction and discrimination of form, color, and location for successful performance.

Mesh:

Year:  1992        PMID: 1422090     DOI: 10.1093/cercor/2.4.310

Source DB:  PubMed          Journal:  Cereb Cortex        ISSN: 1047-3211            Impact factor:   5.357


  42 in total

1.  Increased synchronization of neuromagnetic responses during conscious perception.

Authors:  R Srinivasan; D P Russell; G M Edelman; G Tononi
Journal:  J Neurosci       Date:  1999-07-01       Impact factor: 6.167

Review 2.  The labile brain. I. Neuronal transients and nonlinear coupling.

Authors:  K J Friston
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2000-02-29       Impact factor: 6.237

Review 3.  On the relation between brain images and brain neural networks.

Authors:  J G Taylor; B Krause; N J Shah; B Horwitz; H W Mueller-Gaertner
Journal:  Hum Brain Mapp       Date:  2000-03       Impact factor: 5.038

Review 4.  Degeneracy and complexity in biological systems.

Authors:  G M Edelman; J A Gally
Journal:  Proc Natl Acad Sci U S A       Date:  2001-11-06       Impact factor: 11.205

Review 5.  The functional logic of cortico-pulvinar connections.

Authors:  S Shipp
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2003-10-29       Impact factor: 6.237

6.  Distinctive conflict processes associated with different stimulus presentation patterns: an event-related potential study.

Authors:  Xi Zhang; Yuping Wang; Shunwei Li; Luning Wang; Shujuan Tian
Journal:  Exp Brain Res       Date:  2005-03-18       Impact factor: 1.972

7.  MEG phase follows conscious perception during binocular rivalry induced by visual stream segregation.

Authors:  Ramesh Srinivasan; Sanja Petrovic
Journal:  Cereb Cortex       Date:  2005-08-17       Impact factor: 5.357

8.  Localizing complex neural circuits with MEG data.

Authors:  P Belardinelli; L Ciancetta; V Pizzella; C Del Gratta; G L Romani
Journal:  Cogn Process       Date:  2006-01-21

Review 9.  Top-down predictions in the cognitive brain.

Authors:  Kestutis Kveraga; Avniel S Ghuman; Moshe Bar
Journal:  Brain Cogn       Date:  2007-11       Impact factor: 2.310

10.  Investigating neural correlates of conscious perception by frequency-tagged neuromagnetic responses.

Authors:  G Tononi; R Srinivasan; D P Russell; G M Edelman
Journal:  Proc Natl Acad Sci U S A       Date:  1998-03-17       Impact factor: 11.205

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