Simulation of retinal ganglion cell response using fast independent component analysis

Advances in neurobiology suggest that neuronal response of the primary visual cortex to natural stimuli may be attributed to sparse approximation of images, encoding stimuli to activate specific neurons although the underlying mechanisms are still unclear. The responses of retinal ganglion cells (RGCs) to natural and random checkerboard stimuli were simulated using fast independent component analysis. The neuronal response to stimuli was measured using kurtosis and Treves–Rolls sparseness, and the kurtosis, lifetime and population sparseness were analyzed. RGCs exhibited significant lifetime sparseness in response to natural stimuli and random checkerboard stimuli. About 65 and 72% of RGCs do not fire all the time in response to natural and random checkerboard stimuli, respectively. Both kurtosis of single neurons and lifetime response of single neurons values were larger in the case of natural than in random checkerboard stimuli. The population of RGCs fire much less in response to random checkerboard stimuli than natural stimuli. However, kurtosis of population sparseness and population response of the entire neurons were larger with natural than random checkerboard stimuli. RGCs fire more sparsely in response to natural stimuli. Individual neurons fire at a low rate, while the occasional “burst” of neuronal population transmits information efficiently.

[1]  Joseph J Atick,et al.  Could information theory provide an ecological theory of sensory processing? , 2011, Network.

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

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

[4]  Constantinos I. Siettos,et al.  Children with well controlled epilepsy possess different spatio-temporal patterns of causal network connectivity during a visual working memory task , 2016, Cognitive Neurodynamics.

[5]  D. Tolhurst,et al.  Characterizing the sparseness of neural codes , 2001 .

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

[7]  Arturo Tozzi,et al.  From abstract topology to real thermodynamic brain activity , 2016, Cognitive Neurodynamics.

[8]  Gidon Felsen,et al.  A natural approach to studying vision , 2005, Nature Neuroscience.

[9]  H E Bedell,et al.  Relative Legibility and Confusions of Letter Acuity Targets in the Peripheral and Central Retina , 2000, Optometry and vision science : official publication of the American Academy of Optometry.

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

[11]  Jingyi Qu,et al.  Effect of different glucose supply conditions on neuronal energy metabolism , 2016, Cognitive Neurodynamics.

[12]  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.

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

[14]  Edmund T. Rolls,et al.  What determines the capacity of autoassociative memories in the brain? Network , 1991 .

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

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

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

[18]  Alex E. Hadjinicolaou,et al.  Retinal ganglion cells: mechanisms underlying depolarization block and differential responses to high frequency electrical stimulation of ON and OFF cells , 2016, Journal of neural engineering.

[19]  J. Touryan,et al.  Spatial Structure of Complex Cell Receptive Fields Measured with Natural Images , 2005, Neuron.

[20]  G. Richard,et al.  Retinal cells integrate into the outer nuclear layer and differentiate into mature photoreceptors after subretinal transplantation into adult mice. , 2008, Experimental eye research.

[21]  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.

[22]  Yating Zhu,et al.  Optimal path-finding through mental exploration based on neural energy field gradients , 2016, Cognitive Neurodynamics.

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

[24]  T. Sejnowski,et al.  Metabolic cost as a unifying principle governing neuronal biophysics , 2010, Proceedings of the National Academy of Sciences.

[25]  David B. Grayden,et al.  A Simple and Accurate Model to Predict Responses to Multi-electrode Stimulation in the Retina , 2016, PLoS Comput. Biol..

[26]  S. Gharibzadeh,et al.  Scale-freeness of dominant and piecemeal perceptions during binocular rivalry , 2017, Cognitive Neurodynamics.

[27]  S. Laughlin.  Energy as a constraint on the coding and processing of sensory information , 2001, Current Opinion in Neurobiology.

[28]  Osamu Araki,et al.  Involvement of the visual change detection process in facilitating perceptual alternation in the bistable image , 2017, Cognitive Neurodynamics.

[29]  S. Schultz.  Principles of Neural Science, 4th ed. , 2001 .

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

[31]  Rubin Wang,et al.  Sparse coding network model based on fast independent component analysis , 2017, Neural Computing and Applications.

[32]  Tarek F. Abdelzaher,et al.  Composable context aware services [guest editorial] , 2008, IEEE Network.

[33]  Andrew Hunter,et al.  A Bayesian Framework for the Local Configuration of Retinal Junctions , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[34]  Pierre Kornprobst,et al.  Virtual Retina: A biological retina model and simulator, with contrast gain control , 2009, Journal of Computational Neuroscience.

[35]  M. R. Khoshbin-e-Khoshnazar.  Quantum Superposition in the Retina: Evidences and Proposals , 2014 .

[36]  Mohammad Reza Daliri,et al.  Predicting the eye fixation locations in the gray scale images in the visual scenes with different semantic contents , 2015, Cognitive Neurodynamics.

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

[38]  Tatiana Kameneva,et al.  Frequency Responses of Rat Retinal Ganglion Cells , 2016, PloS one.

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

[40]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[41]  Hai-Qing Gong,et al.  Temporal and Spatial Patterns of Retinal Ganglion Cells in Response to Natural Stimuli*: Temporal and Spatial Patterns of Retinal Ganglion Cells in Response to Natural Stimuli* , 2010 .

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

[43]  H. B. Barlow,et al.  Possible Principles Underlying the Transformations of Sensory Messages , 2012 .

[44]  Pu-Ming Zhang,et al.  The oscillation-like activity in bullfrog ON–OFF retinal ganglion cell , 2016, Cognitive Neurodynamics.

[45]  Sheela Ramanna,et al.  The human brain from above: an increase in complexity from environmental stimuli to abstractions , 2017, Cognitive Neurodynamics.

[46]  N. C. Singh,et al.  Estimating spatio-temporal receptive fields of auditory and visual neurons from their responses to natural stimuli , 2001 .

[47]  Abdul Wahab,et al.  Neural network modelling of the influence of channelopathies on reflex visual attention , 2016, Cognitive Neurodynamics.

[48]  P. H. Schiller.  The central visual system , 1986, Vision Research.

[49]  Feng Qi Han,et al.  Cortical Sensitivity to Visual Features in Natural Scenes , 2005, PLoS biology.

[50]  David B. Grayden,et al.  The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results , 2013, Journal of Computational Neuroscience.

[51]  Shigang He,et al.  Temporal properties of dual-peak responses of mouse retinal ganglion cells and effects of inhibitory pathways , 2016, Cognitive Neurodynamics.

[52]  David Cai,et al.  Sparsity and Compressed Coding in Sensory Systems , 2014, PLoS Comput. Biol..

[53]  Eduardo Mizraji,et al.  The feeling of understanding: an exploration with neural models , 2017, Cognitive Neurodynamics.

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

[55]  C. Gross,et al.  How inferior temporal cortex became a visual area. , 1994, Cerebral cortex.

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

[57]  Chris Fields,et al.  Disrupted development and imbalanced function in the global neuronal workspace: a positive-feedback mechanism for the emergence of ASD in early infancy , 2017, Cognitive Neurodynamics.

[58]  D. Tolhurst,et al.  Characterizing the sparseness of neural codes , 2001, Network.

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