Sparse coding network model based on fast independent component analysis

Neurobiological studies have shown that neurons in the primary visual cortex (V1) may employ sparse presentations to represent stimuli. We describe a network model for sparse coding which includes input layer, base functional layer and output layer. We simulated standard sparse coding and sparse coding based on fast independent component analysis (ICA). The duration of training bases, the convergence speed of objective function and the sparsity of coefficient matrix were compared, respectively. The results show that sparse coding based on fast ICA is more effective than standard sparse coding.

[1]  Bruno A Olshausen,et al.  Sparse coding of sensory inputs , 2004, Current Opinion in Neurobiology.

[2]  Aapo Hyvärinen,et al.  A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images , 2001, Vision Research.

[3]  Alan R. Palmer,et al.  Psychophysical and Physiological Advances in Hearing , 1998 .

[4]  Ichiro Tsuda,et al.  A New Work Mechanism on Neuronal Activity , 2015, Int. J. Neural Syst..

[5]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[6]  Aapo Hyvärinen,et al.  Topographic Independent Component Analysis , 2001, Neural Computation.

[7]  Andrzej Cichocki,et al.  On the synchrony of steady state visual evoked potentials and oscillatory burst events , 2009, Cognitive Neurodynamics.

[8]  Chunguang Li,et al.  Fast and robust image segmentation by small-world neural oscillator networks , 2011, Cognitive Neurodynamics.

[9]  Yating Zhu,et al.  Can the activities of the large scale cortical network be expressed by neural energy? A brief review , 2015, Cognitive Neurodynamics.

[10]  Nobuki Murayama,et al.  A retinal circuit model accounting for wide-field amacrine cells , 2009, Cognitive Neurodynamics.

[11]  Michael S. Lewicki,et al.  Efficient coding of natural sounds , 2002, Nature Neuroscience.

[12]  Z. Zhang,et al.  Phase Synchronization Motion and Neural Coding in Dynamic Transmission of Neural Information , 2011, IEEE Transactions on Neural Networks.

[13]  Eero P. Simoncelli.  Vision and the statistics of the visual environment , 2003, Current Opinion in Neurobiology.

[14]  Junwei Han,et al.  Encoding brain network response to free viewing of videos , 2014, Cognitive Neurodynamics.

[15]  P O Hoyer,et al.  Independent component analysis applied to feature extraction from colour and stereo images , 2000, Network.

[16]  Pei-Ji Liang,et al.  Neural coding properties based on spike timing and pattern correlation of retinal ganglion cells , 2010, Cognitive Neurodynamics.

[17]  Eero P. Simoncelli,et al.  Spatio-temporal correlations and visual signalling in a complete neuronal population , 2008, Nature.

[18]  D. Hubel,et al.  Ferrier lecture - Functional architecture of macaque monkey visual cortex , 1977, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[19]  William Bialek,et al.  Statistics of Natural Images: Scaling in the Woods , 1993, NIPS.

[20]  Si Wu,et al.  Robustness of neural codes and its implication on natural image processing , 2007, Cognitive Neurodynamics.

[21]  D. Ruderman,et al.  INDEPENDENT COMPONENT ANALYSIS OF NATURAL IMAGE SEQUENCES YIELDS SPATIOTEMPORAL FILTERS SIMILAR TO SIMPLE CELLS IN PRIMARY VISUAL CORTEX , 1998 .

[22]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[23]  J. Gallant,et al.  Natural Stimulation of the Nonclassical Receptive Field Increases Information Transmission Efficiency in V1 , 2002, The Journal of Neuroscience.

[24]  J. Rothwell.  Principles of Neural Science , 1982 .

[25]  Hermann Haken,et al.  Towards a unifying model of neural net activity in the visual cortex , 2007, Cognitive Neurodynamics.

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

[27]  Eero P. Simoncelli,et al.  Natural image statistics and neural representation. , 2001, Annual review of neuroscience.

[28]  D. Ruderman,et al.  Independent component analysis of natural image sequences yields spatio-temporal filters similar to simple cells in primary visual cortex , 1998, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[29]  T. Wiesel,et al.  Functional architecture of macaque monkey visual cortex , 1977 .

[30]  Ziyin Wang,et al.  Energy coding in neural network with inhibitory neurons , 2014, Cognitive Neurodynamics.

[31]  Rubin Wang,et al.  Energy coding in biological neural networks , 2007, Cognitive Neurodynamics.

[32]  David J. Field,et al.  What Is the Goal of Sensory Coding? , 1994, Neural Computation.

[33]  Rubin Wang,et al.  Energy distribution property and energy coding of a structural neural network , 2014, Front. Comput. Neurosci..

[34]  M. Feller,et al.  Mechanisms underlying development of visual maps and receptive fields. , 2008, Annual review of neuroscience.