Literature DB >> 7719126

Sequence seeking and counter streams: a computational model for bidirectional information flow in the visual cortex.

S Ullman1.   

Abstract

A computational model is proposed for some general aspects of information flow in the visual cortex. The basic process, called "sequence seeking," is a search for a sequence of mappings, or transformations, linking source and target patterns. The process has two main characteristics: it is bidirectional, bottom-up as well as top-down, and it explores in parallel a large number of alternative sequences. This operation is performed in a "counter streams" structure, in which multiple sequences are explored along two complementary pathways, an ascending and a descending one, seeking to meet. A biological embodiment of this model in cortical circuitry is proposed. The model serves to account for known aspects of cortical interconnections and to derive new predictions.

Mesh:

Year:  1995        PMID: 7719126     DOI: 10.1093/cercor/5.1.1

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


  55 in total

1.  A feedback model of attention and context dependence in visual cortical networks.

Authors:  K L Kirkland; G L Gerstein
Journal:  J Comput Neurosci       Date:  1999 Nov-Dec       Impact factor: 1.621

Review 2.  Is most of neural plasticity in the thalamus cortical?

Authors:  J H Kaas
Journal:  Proc Natl Acad Sci U S A       Date:  1999-07-06       Impact factor: 11.205

3.  Integrating top-down and bottom-up sensory processing by somato-dendritic interactions.

Authors:  M Siegel; K P Körding; P König
Journal:  J Comput Neurosci       Date:  2000 Mar-Apr       Impact factor: 1.621

4.  Precise spatiotemporal patterns among visual cortical areas and their relation to visual stimulus processing.

Authors:  Inbal Ayzenshtat; Elhanan Meirovithz; Hadar Edelman; Uri Werner-Reiss; Elie Bienenstock; Moshe Abeles; Hamutal Slovin
Journal:  J Neurosci       Date:  2010-08-18       Impact factor: 6.167

Review 5.  Neural networks and perceptual learning.

Authors:  Misha Tsodyks; Charles Gilbert
Journal:  Nature       Date:  2004-10-14       Impact factor: 49.962

Review 6.  The importance of being agranular: a comparative account of visual and motor cortex.

Authors:  Stewart Shipp
Journal:  Philos Trans R Soc Lond B Biol Sci       Date:  2005-04-29       Impact factor: 6.237

7.  Top-down facilitation of visual recognition.

Authors:  M Bar; K S Kassam; A S Ghuman; J Boshyan; A M Schmid; A M Schmidt; A M Dale; M S Hämäläinen; K Marinkovic; D L Schacter; B R Rosen; E Halgren
Journal:  Proc Natl Acad Sci U S A       Date:  2006-01-03       Impact factor: 11.205

Review 8.  The prefrontal cortex and flexible behavior.

Authors:  Helen Barbas; Basilis Zikopoulos
Journal:  Neuroscientist       Date:  2007-10       Impact factor: 7.519

9.  Parallel input makes the brain run faster.

Authors:  Tommi Raij; Jari Karhu; Dubravko Kicić; Pantelis Lioumis; Petro Julkunen; Fa-Hsuan Lin; Jyrki Ahveninen; Risto J Ilmoniemi; Jyrki P Mäkelä; Matti Hämäläinen; Bruce R Rosen; John W Belliveau
Journal:  Neuroimage       Date:  2008-02-14       Impact factor: 6.556

10.  Recognition alters the spatial pattern of FMRI activation in early retinotopic cortex.

Authors:  P-J Hsieh; E Vul; N Kanwisher
Journal:  J Neurophysiol       Date:  2010-01-13       Impact factor: 2.714

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