Adaptive shape processing in primary visual cortex

The ability to derive meaning from complex sensory input requires the integration of information over space and time, as well as cognitive mechanisms to shape that integration. We studied these processes in the primary visual cortex (V1), where neurons are thought to integrate visual inputs along contours defined by an association field (AF). We recorded extracellularly from single cells in macaque V1 to map the AF, by using an optimization algorithm to find the contours that maximally activated individual cells. We combined the algorithm with a delayed-match-to-sample task, to test how the optimal contours might be molded by the monkey's expectation for particular cue shapes. We found that V1 neurons were selective for complex shapes, a property previously ascribed to higher cortical areas. Furthermore, the shape selectivity was reprogrammed by perceptual task: Over the whole network, the optimal modes of geometric selectivity shifted between distinct subsets of the AF, alternately representing different stimulus features known to predominate in natural scenes. Our results suggest a general model of cortical function, whereby horizontal connections provide a broad domain of potential associations, and top–down inputs dynamically gate these linkages to task switch the function of a network.

[1]  David Marr,et al.  VISION A Computational Investigation into the Human Representation and Processing of Visual Information , 2009 .

[2]  Wu Li,et al.  Global contour saliency and local colinear interactions. , 2001, Journal of neurophysiology.

[3]  David J. Field,et al.  Contour integration by the human visual system: Evidence for a local “association field” , 1993, Vision Research.

[4]  W. Singer,et al.  Synchronization of neuronal responses in primary visual cortex of monkeys viewing natural images. , 2008, Journal of neurophysiology.

[5]  Geoffrey M. Ghose,et al.  Attention directed by expectations enhances receptive fields in cortical area MT , 2010, Vision Research.

[6]  C. Gilbert,et al.  Attention Modulates Contextual Influences in the Primary Visual Cortex of Alert Monkeys , 1999, Neuron.

[7]  B. Motter.  Neural correlates of attentive selection for color or luminance in extrastriate area V4 , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[8]  B. C. Motter.  Focal attention produces spatially selective processing in visual cortical areas V1, V2, and V4 in the presence of competing stimuli. , 1993, Journal of neurophysiology.

[9]  Jean Bennett,et al.  Lateral Connectivity and Contextual Interactions in Macaque Primary Visual Cortex , 2002, Neuron.

[10]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[11]  R. Desimone,et al.  Attention Increases Sensitivity of V4 Neurons , 2000, Neuron.

[12]  S. Ullman.  Object recognition and segmentation by a fragment-based hierarchy , 2007, Trends in Cognitive Sciences.

[13]  R. von der Heydt,et al.  Illusory contours and cortical neuron responses. , 1984, Science.

[14]  Isaac Amidror,et al.  Scattered data interpolation methods for electronic imaging systems: a survey , 2002, J. Electronic Imaging.

[15]  R. Desimone,et al.  Attention Increases Sensitivity of V4 Neurons , 2000, Neuron.

[16]  C. Gilbert,et al.  Contour Saliency in Primary Visual Cortex , 2006, Neuron.

[17]  C. Summerfield,et al.  Expectation (and attention) in visual cognition , 2009, Trends in Cognitive Sciences.

[18]  T. Albright,et al.  Contextual influences on visual processing. , 2002, Annual review of neuroscience.

[19]  C. Gilbert,et al.  Perceptual learning and top-down influences in primary visual cortex , 2004, Nature Neuroscience.

[20]  D. Mumford,et al.  The role of the primary visual cortex in higher level vision , 1998, Vision Research.

[21]  C. Gilbert,et al.  On a common circle: natural scenes and Gestalt rules. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[22]  David Fitzpatrick,et al.  Emergent Properties of Layer 2/3 Neurons Reflect the Collinear Arrangement of Horizontal Connections in Tree Shrew Visual Cortex , 2003, The Journal of Neuroscience.

[23]  Zhaoping Li,et al.  A Neural Model of Contour Integration in the Primary Visual Cortex , 1998, Neural Computation.

[24]  G. Reeke,et al.  Network model of top-down influences on local gain and contextual interactions in visual cortex , 2013, Proceedings of the National Academy of Sciences.

[25]  Shimon Ullman,et al.  A computational model of perceptual fill-in following retinal degeneration. , 2008, Journal of neurophysiology.

[26]  Stefan Treue,et al.  Feature-based attention influences motion processing gain in macaque visual cortex , 1999, Nature.

[27]  J. Gallant,et al.  Attention to Stimulus Features Shifts Spectral Tuning of V4 Neurons during Natural Vision , 2008, Neuron.

[28]  C. Gilbert,et al.  Improvement in visual sensitivity by changes in local context: Parallel studies in human observers and in V1 of alert monkeys , 1995, Neuron.

[29]  C. Gilbert,et al.  Spatial distribution of contextual interactions in primary visual cortex and in visual perception. , 2000, Journal of neurophysiology.

[30]  D. Fitzpatrick,et al.  Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex , 1997, The Journal of Neuroscience.

[31]  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.

[32]  M. V. Velzen,et al.  Self-organizing maps , 2007 .

[33]  Shimon Ullman,et al.  Image interpretation by a single bottom-up top-down cycle , 2008, Proceedings of the National Academy of Sciences.

[34]  R. Desimone,et al.  Responses of Neurons in Inferior Temporal Cortex during Memory- Guided Visual Search , 1998 .

[35]  J. Reynolds,et al.  Attentional modulation of visual processing. , 2004, Annual review of neuroscience.

[36]  S. Thorpe,et al.  A Limit to the Speed of Processing in Ultra-Rapid Visual Categorization of Novel Natural Scenes , 2001, Journal of Cognitive Neuroscience.

[37]  R. Desimone,et al.  A backward progression of attentional effects in the ventral stream , 2009, Proceedings of the National Academy of Sciences.

[38]  Sir G. Archaeopteryx.  Object-based attention in the primary visual cortex of the macaque monkey , 1998 .

[39]  John H. R. Maunsell,et al.  Feature-based attention in visual cortex , 2006, Trends in Neurosciences.

[40]  Ohad Ben-Shahar,et al.  Geometrical Computations Explain Projection Patterns of Long-Range Horizontal Connections in Visual Cortex , 2004, Neural Computation.