Processing of Natural Temporal Stimuli by Macaque Retinal Ganglion Cells

This study quantifies the performance of primate retinal ganglion cells in response to natural stimuli. Stimuli were confined to the temporal and chromatic domains and were derived from two contrasting environments, one typically northern European and the other a flower show. The performance of the cells was evaluated by investigating variability of cell responses to repeated stimulus presentations and by comparing measured to model responses. Both analyses yielded a quantity called the coherence rate (in bits per second), which is related to the information rate. Magnocellular (MC) cells yielded coherence rates of up to 100 bits/sec, rates of parvocellular (PC) cells were much lower, and short wavelength (S)-cone-driven ganglion cells yielded intermediate rates. The modeling approach showed that for MC cells, coherence rates were generated almost exclusively by the luminance content of the stimulus. Coherence rates of PC cells were also dominated by achromatic content. This is a consequence of the stimulus structure; luminance varied much more in the natural environment than chromaticity. Only approximately one-sixth of the coherence rate of the PC cells derived from chromatic content, and it was dominated by frequencies below 10 Hz. S-cone-driven ganglion cells also yielded coherence rates dominated by low frequencies. Below 2–3 Hz, PC cell signals contained more power than those of MC cells. Response variation between individual ganglion cells of a particular class was analyzed by constructing generic cells, the properties of which may be relevant for performance higher in the visual system. The approach used here helps define retinal modules useful for studies of higher visual processing of natural stimuli.

[1]  J. Pokorny,et al.  Spectral sensitivity of the foveal cone photopigments between 400 and 500 nm , 1975, Vision Research.

[2]  A. Papoulis Signal Analysis , 1977 .

[3]  J. Mollon,et al.  A theory of theΠ1 andΠ3 color mechanisms of stiles , 1979, Vision Research.

[4]  Political support in F.W. Programme: need and willingness. , 1980, POPCEN news letter. Population Centre.

[5]  Robert M. Boynton,et al.  Chromatic difference steps of moderate size measured along theoretically critical axes , 1980 .

[6]  S. Laughlin A Simple Coding Procedure Enhances a Neuron's Information Capacity , 1981, Zeitschrift fur Naturforschung. Section C, Biosciences.

[7]  S. Laughlin,et al.  Predictive coding: a fresh view of inhibition in the retina , 1982, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[8]  J. Victor The dynamics of the cat retinal X cell centre. , 1987, The Journal of physiology.

[9]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[10]  P. Lennie,et al.  Mechanisms of color vision. , 1988, Critical reviews in neurobiology.

[11]  William Bialek,et al.  Reading a Neural Code , 1991, NIPS.

[12]  B. B. Lee,et al.  Sensitivity of macaque retinal ganglion cells to chromatic and luminance flicker. , 1989, The Journal of physiology.

[13]  Barry B. Lee,et al.  Chapter 7 New views of primate retinal function , 1990 .

[14]  William H. Press,et al.  Numerical Recipes: FORTRAN , 1988 .

[15]  J. Pokorny,et al.  Luminance and chromatic modulation sensitivity of macaque ganglion cells and human observers. , 1990, Journal of the Optical Society of America. A, Optics and image science.

[16]  J. V. van Hateren,et al.  Real and optimal neural images in early vision , 1992, Nature.

[17]  J. Pokorny,et al.  Responses of macaque ganglion cells to the relative phase of heterochromatically modulated lights. , 1992, The Journal of physiology.

[18]  B. Knight,et al.  Contrast gain control in the primate retina: P cells are not X-like, some M cells are , 1992, Visual Neuroscience.

[19]  B. B. Lee,et al.  Responses of macaque ganglion cells to movement of chromatic borders. , 1992, Journal of Physiology.

[20]  M. J. M. Lankheet,et al.  The dynamics of light adaptation in cat horizontal cell responses , 1993, Vision Research.

[21]  G. H. Jacobs THE DISTRIBUTION AND NATURE OF COLOUR VISION AMONG THE MAMMALS , 1993, Biological reviews of the Cambridge Philosophical Society.

[22]  J. V. van Hateren Spatial, temporal and spectral pre-processing for colour vision , 1993, Proceedings of the Royal Society of London. Series B: Biological Sciences.

[23]  B. B. Lee,et al.  Physiological mechanisms underlying psychophysical sensitivity to combined luminance and chromatic modulation. , 1993, Journal of the Optical Society of America. A, Optics and image science.

[24]  Joel Pokorny,et al.  Responses to pulses and sinusoids in macaque ganglion cells , 1994, Vision Research.

[25]  J. Atick,et al.  Temporal decorrelation: a theory of lagged and nonlagged responses in the lateral geniculate nucleus , 1995 .

[26]  B. B. Lee,et al.  Temporal response of ganglion cells of the macaque retina to cone-specific modulation. , 1995, Journal of the Optical Society of America. A, Optics, image science, and vision.

[27]  J. Miller,et al.  Information theoretic analysis of dynamical encoding by four identified primary sensory interneurons in the cricket cercal system. , 1996, Journal of neurophysiology.

[28]  R C Reid,et al.  Efficient Coding of Natural Scenes in the Lateral Geniculate Nucleus: Experimental Test of a Computational Theory , 1996, The Journal of Neuroscience.

[29]  L. Abbott,et al.  Responses of neurons in primary and inferior temporal visual cortices to natural scenes , 1997, Proceedings of the Royal Society of London. Series B: Biological Sciences.

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

[31]  H R Wilson,et al.  A neural model of foveal light adaptation and afterimage formation , 1997, Visual Neuroscience.

[32]  F. Dodge,et al.  Deciphering a neural code for vision. , 1997, Proceedings of the National Academy of Sciences of the United States of America.

[33]  W. G. Owen,et al.  Linear transduction of natural stimuli by dark‐adapted and light‐adapted rods of the salamander, Ambystoma tigrinum , 1997, The Journal of physiology.

[34]  A. Borst,et al.  Active Membrane Properties and Signal Encoding in Graded Potential Neurons , 1998, The Journal of Neuroscience.

[35]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[36]  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.

[37]  G B Stanley,et al.  Reconstruction of Natural Scenes from Ensemble Responses in the Lateral Geniculate Nucleus , 1999, The Journal of Neuroscience.

[38]  Alexander Borst,et al.  Information theory and neural coding , 1999, Nature Neuroscience.

[39]  R. Reid,et al.  Temporal Coding of Visual Information in the Thalamus , 2000, The Journal of Neuroscience.

[40]  J L Gallant,et al.  Sparse coding and decorrelation in primary visual cortex during natural vision. , 2000, Science.

[41]  Retinal responses to natural movies , 2000 .

[42]  J. V. van Hateren,et al.  A temporal model for early vision that explains detection thresholds for light pulses on flickering backgrounds , 2000, Visual Neuroscience.

[43]  D. Dacey Parallel pathways for spectral coding in primate retina. , 2000, Annual review of neuroscience.

[44]  Review: Imagery , 2000 .

[45]  William Bialek,et al.  Synergy in a Neural Code , 2000, Neural Computation.

[46]  G D Lewen,et al.  Neural coding of naturalistic motion stimuli , 2001, Network.

[47]  M. Egelhaaf,et al.  Neural Processing of Naturalistic Optic Flow , 2001, The Journal of Neuroscience.

[48]  J. Pokorny,et al.  Primate horizontal cell dynamics: an analysis of sensitivity regulation in the outer retina. , 2001, Journal of neurophysiology.

[49]  J. H. Hateren,et al.  Information theoretical evaluation of parametric models of gain control in blowfly photoreceptor cells , 2001, Vision Research.

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