Large-scale neural model for visual attention: integration of experimental single-cell and fMRI data.

A computational neuroscience framework is proposed to better understand the role and the neuronal correlate of spatial attention modulation in visual perception. The model consists of several interconnected modules that can be related to the different areas of the dorsal and ventral paths of the visual cortex. Competitive neural interactions are implemented at both microscopic and interareal levels, according to the biased competition hypothesis. This hypothesis has been experimentally confirmed in studies in humans using functional magnetic resonance imaging (fMRI) techniques and also in single-cell recording studies in monkeys. Within this neuro-dynamical approach, numerical simulations are carried out that describe both the fMRI and the electrophysiological data. The proposed model draws together data of different spatial and temporal resolution, as are the above-mentioned imaging and single-cell results.

[1]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[2]  Fred H. Hamker,et al.  The role of feedback connections in task-driven visual search , 1999 .

[3]  J. Duncan.  Cooperating brain systems in selective perception and action. , 1996 .

[4]  R. Desimone,et al.  The Role of Neural Mechanisms of Attention in Solving the Binding Problem , 1999, Neuron.

[5]  J. Duncan,et al.  Visual search and stimulus similarity. , 1989, Psychological review.

[6]  E. Niebur,et al.  Modeling the Temporal Dynamics of IT Neurons in Visual Search: A Mechanism for Top-Down Selective Attention , 1996, Journal of Cognitive Neuroscience.

[7]  H. Spitzer,et al.  Increased attention enhances both behavioral and neuronal performance. , 1988, Science.

[8]  R. L. de Valois,et al.  Relationship between spatial-frequency and orientation tuning of striate-cortex cells. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[9]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[10]  John Daugman,et al.  Neural networks for image transformation, analysis, and compression , 1988, Neural Networks.

[11]  R. Desimone,et al.  Competitive Mechanisms Subserve Attention in Macaque Areas V2 and V4 , 1999, The Journal of Neuroscience.

[12]  Daniel J. Amit,et al.  Quantitative Study of Attractor Neural Network Retrieving at Low Spike Rates: I , 1991 .

[13]  A. Treisman.  Features and Objects: The Fourteenth Bartlett Memorial Lecture , 1988, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[14]  Carrie J. McAdams,et al.  Effects of Attention on Orientation-Tuning Functions of Single Neurons in Macaque Cortical Area V4 , 1999, The Journal of Neuroscience.

[15]  F. AbbottPhysics.  FIRING-RATE MODELS FOR NEURAL POPULATIONSL , 1991 .

[16]  D. G. Albrecht,et al.  Spatial frequency selectivity of cells in macaque visual cortex , 1982, Vision Research.

[17]  Gustavo Deco,et al.  A neuronal model of binding and selective attention in visual search. , 1999 .

[18]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  P. O. Bishop,et al.  Spatial vision. , 1971, Annual review of psychology.

[20]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[21]  R. Desimone,et al.  Neural mechanisms of selective visual attention. , 1995, Annual review of neuroscience.

[22]  R. Desimone,et al.  Neural mechanisms of spatial selective attention in areas V1, V2, and V4 of macaque visual cortex. , 1997, Journal of neurophysiology.

[23]  L. Chelazzi.  Serial attention mechanisms in visual search: A critical look at the evidence , 1999, Psychological research.

[24]  Leslie G. Ungerleider,et al.  Increased Activity in Human Visual Cortex during Directed Attention in the Absence of Visual Stimulation , 1999, Neuron.

[25]  A. Treisman.  Perceptual grouping and attention in visual search for features and for objects. , 1982, Journal of experimental psychology. Human perception and performance.

[26]  R. Desimone,et al.  Attentional control of visual perception: cortical and subcortical mechanisms. , 1990, Cold Spring Harbor symposia on quantitative biology.

[27]  John Duncan,et al.  A neural basis for visual search in inferior temporal cortex , 1993, Nature.

[28]  D. Amit,et al.  Quantitative study of attractor neural networks retrieving at low spike rates: II. Low-rate retrieval in symmetric networks , 1991 .

[29]  Leslie G. Ungerleider,et al.  Mechanisms of directed attention in the human extrastriate cortex as revealed by functional MRI. , 1998, Science.

[30]  R. Desimone,et al.  Selective attention gates visual processing in the extrastriate cortex. , 1985, Science.

[31]  G. Deco,et al.  Top-down selective visual attention: A neurodynamical approach , 2001 .