A Dynamical Model for Receptive Field Self-organization in V1 Cortical Columns

We present a dynamical model of processing and learning in the visual cortex, which reflects the anatomy of V1 cortical columns and properties of their neuronal receptive fields (RFs). The model is described by a set of coupled differential equations and learns by self-organizing the RFs of its computational units - sub-populations of excitatory neurons. If natural image patches are presented as input, self-organization results in Gabor-like RFs. In quantitative comparison with in vivo measurements, we find that these RFs capture statistical properties of V1 simple-cells that learning algorithms such as ICA and sparse coding fail to reproduce.

[1]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[2]  P. Földiák,et al.  Forming sparse representations by local anti-Hebbian learning , 1990, Biological Cybernetics.

[3]  J. H. Hateren,et al.  Independent component filters of natural images compared with simple cells in primary visual cortex , 1998 .

[4]  E. Bienenstock,et al.  Theory for the development of neuron selectivity: orientation specificity and binocular interaction in visual cortex , 1982, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[5]  E. Callaway,et al.  Laminar sources of synaptic input to cortical inhibitory interneurons and pyramidal neurons , 2000, Nature Neuroscience.

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

[7]  E. Callaway,et al.  Excitatory cortical neurons form fine-scale functional networks , 2005, Nature.

[8]  Michael A. Arbib,et al.  The handbook of brain theory and neural networks , 1995, A Bradford book.

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

[10]  Martin Rehn,et al.  A network that uses few active neurones to code visual input predicts the diverse shapes of cortical receptive fields , 2007, Journal of Computational Neuroscience.

[11]  Jörg Lücke,et al.  Dynamics of Cortical Columns - Self-organization of Receptive Fields , 2005, ICANN.

[12]  Jörg Lücke,et al.  Hierarchical self-organization of minicolumnar receptive fields , 2004, Neural Networks.

[13]  S. Nelson,et al.  An emergent model of orientation selectivity in cat visual cortical simple cells , 1995, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[14]  Rajesh P. N. Rao,et al.  Probabilistic Models of the Brain: Perception and Neural Function , 2002 .

[15]  M. A. Repucci,et al.  Spatial Structure and Symmetry of Simple-Cell Receptive Fields in Macaque Primary Visual Cortex , 2002 .

[16]  Jörg Lücke,et al.  Rapid Processing and Unsupervised Learning in a Model of the Cortical Macrocolumn , 2004, Neural Computation.

[17]  Jörg Lücke,et al.  Dynamics of Cortical Columns - Sensitive Decision Making , 2005, ICANN.

[18]  David J. Fleet,et al.  Probabilistic Models of the Brain : Perception and Neural Function , 2001 .

[19]  Geoffrey E. Hinton,et al.  Topographic Product Models Applied to Natural Scene Statistics , 2006, Neural Computation.

[20]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[21]  José M. Jerez,et al.  A Learning Rule to Model the Development of Orientation Selectivity in Visual Cortex , 2005 .