Integrating contrast invariance into a model for cortical orientation map formation

Hebbian models of orientation map formation in the primary visual cortex typically represent the cortex as an array of neurons that are excited by both ON and OFF lateral geniculate nucleus neurons. However, simple cells with only thalamic excitation exhibit tuning curves that widen with stimulus contrast. This is inconsistent with the contrast-invariant width actually observed. We propose a map formation model that achieves contrast invariance through anti-phase inhibition. We describe how inhibition between columns implements the coverage constraint to ensure orientations are uniformly sampled. We find that the model exhibits more robust receptive field formation than a simpler model without anti-phase inhibition.

[1]  D. Ferster.  Spatially opponent excitation and inhibition in simple cells of the cat visual cortex , 1988, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[2]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[3]  K. Miller,et al.  Ocular dominance column development: analysis and simulation. , 1989, Science.

[4]  C. Blakemore,et al.  An analysis of orientation selectivity in the cat's visual cortex , 1974, Experimental Brain Research.

[5]  D. P. Andrews,et al.  Orientation tuning of cells in areas 17 and 18 of the cat's visual cortex , 1978, Experimental Brain Research.

[6]  Risto Miikkulainen,et al.  Pattern-generator-driven development in self-organizing models , 1998 .

[7]  Risto Miikkulainen,et al.  Computational Maps in the Visual Cortex , 2005 .

[8]  R. Reid,et al.  Synaptic Integration in Striate Cortical Simple Cells , 1998, The Journal of Neuroscience.

[9]  Risto Miikkulainen,et al.  Computational Neuroscience: Trends in Research, 1998 , 1998 .

[10]  Nicholas J. Priebe,et al.  Contrast-Invariant Orientation Tuning in Cat Visual Cortex: Thalamocortical Input Tuning and Correlation-Based Intracortical Connectivity , 1998, The Journal of Neuroscience.

[11]  G. Sclar,et al.  Expression of “retinal” contrast gain control by neurons of the cat's lateral geniculate nucleus , 2004, Experimental Brain Research.

[12]  Terrence J. Sejnowski,et al.  The “independent components” of natural scenes are edge filters , 1997, Vision Research.

[13]  Aapo Hyvärinen,et al.  Emergence of Phase- and Shift-Invariant Features by Decomposition of Natural Images into Independent Feature Subspaces , 2000, Neural Computation.

[14]  V. Mountcastle.  The columnar organization of the neocortex. , 1997, Brain : a journal of neurology.

[15]  P. O. Bishop,et al.  Orientation specificity of cells in cat striate cortex. , 1974, Journal of neurophysiology.

[16]  David J. Field,et al.  Emergence of simple-cell receptive field properties by learning a sparse code for natural images , 1996, Nature.

[17]  K. Miller,et al.  Opponent Inhibition A Developmental Model of Layer 4 of the Neocortical Circuit , 2002, Neuron.

[18]  K. Miller,et al.  Correlation-Based Development of Ocularly Matched Orientation and Ocular Dominance Maps: Determination of Required Input Activities , 1998, The Journal of Neuroscience.

[19]  C. Malsburg.  Self-organization of orientation sensitive cells in the striate cortex , 2004, Kybernetik.

[20]  R Linsker,et al.  From basic network principles to neural architecture: emergence of spatial-opponent cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Dezhe Z. Jin,et al.  The Coordinated Mapping of Visual Space and Response Features in Visual Cortex , 2005, Neuron.

[22]  D. Hubel.  Eye, brain, and vision , 1988 .

[23]  Roman Bek,et al.  Discourse on one way in which a quantum-mechanics language on the classical logical base can be built up , 1978, Kybernetika.

[24]  M. Carandini,et al.  Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex. , 2000, Journal of neurophysiology.

[25]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation-selective cells. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[26]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[27]  D. Ferster.  Orientation selectivity of synaptic potentials in neurons of cat primary visual cortex , 1986, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[28]  Richard Durbin,et al.  A dimension reduction framework for understanding cortical maps , 1990, Nature.

[29]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[30]  Risto Miikkulainen,et al.  Learning Innate Face Preferences , 2003, Neural Computation.

[31]  R Linsker,et al.  From basic network principles to neural architecture: emergence of orientation columns. , 1986, Proceedings of the National Academy of Sciences of the United States of America.

[32]  David Willshaw,et al.  Application of the elastic net algorithm to the formation of ocular dominance stripes , 1990 .

[33]  Geoffrey J. Goodhill,et al.  Topography and ocular dominance: a model exploring positive correlations , 1993, Biological Cybernetics.

[34]  Amiram Grinvald,et al.  Visual cortex maps are optimized for uniform coverage , 2000, Nature Neuroscience.

[35]  KD Miller.  A model for the development of simple cell receptive fields and the ordered arrangement of orientation columns through activity-dependent competition between ON- and OFF-center inputs , 1994, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[36]  N. Swindale.  The development of topography in the visual cortex: a review of models. , 1996 .