Analysis of the interaction between the retinal ON and OFF channels using CNN-UM models

Quantitative retinal modeling is an important tool for analyzing the circuitry underlying the functional organization of the retina, and developing neuromorphic signal processing for retinal prostheses. The Cellular Neural Network (CNN) is a suitable tool for both purposes due to its structure. Using multilayer CNN models we analyzed two phenomena: First the signal distortion caused by nonlinear synapses and secondly the use of inhibitory interaction between ON and OFF channels. Our simulations show that the ON–OFF interaction can reduce the signal distortion caused by nonlinear synapses. Copyright © 2008 John Wiley & Sons, Ltd.

[1]  Bertram E. Shi,et al.  An eight layer cellular neural network for spatio‐temporal image filtering , 2006, Int. J. Circuit Theory Appl..

[2]  Matthias H Hennig,et al.  The Influence of Different Retinal Subcircuits on the Nonlinearity of Ganglion Cell Behavior , 2002, The Journal of Neuroscience.

[3]  J. Dowling,et al.  Organization of the retina of the mudpuppy, Necturus maculosus. II. Intracellular recording. , 1969, Journal of neurophysiology.

[4]  F. Werblin,et al.  Vertical interactions across ten parallel, stacked representations in the mammalian retina , 2001, Nature.

[5]  Botond Roska,et al.  Parallel processing in retinal ganglion cells: how integration of space-time patterns of excitation and inhibition form the spiking output. , 2006, Journal of neurophysiology.

[6]  Ángel Rodríguez-Vázquez,et al.  ACE16k: A Programmable Focal Plane Vision Processor with 128 x 128 Resolution , 2001 .

[7]  Tamás Roska,et al.  A CNN framework for modeling parallel processing in a mammalian retina , 2002, Int. J. Circuit Theory Appl..

[8]  Péter Szolgay,et al.  Emulated digital CNN-UM solution of partial differential equations , 2006, Int. J. Circuit Theory Appl..

[9]  Tamás Roska,et al.  Digital implementation of cellular sensor-computers , 2006, Int. J. Circuit Theory Appl..

[10]  J. Belgum,et al.  Push‐pull effect of surround illumination on excitatory and inhibitory inputs to mudpuppy retinal ganglion cells. , 1987, The Journal of physiology.

[11]  R F Miller,et al.  Synaptic inputs and morphology of sustained ON-ganglion cells in the mudpuppy retina. , 1988, Journal of neurophysiology.

[12]  R. Miller,et al.  Bipolar origin of synaptic inputs to sustained OFF-ganglion cells in the mudpuppy retina. , 1988, Journal of neurophysiology.

[13]  Leon O. Chua,et al.  The analogic cellular neural network as a bionic eye , 1995, Int. J. Circuit Theory Appl..

[14]  F. Werblin.  Control of Retinal Sensitivity II. Lateral Interactions at the Outer Plexiform Layer , 1974 .

[15]  Frank S. Werblin,et al.  The network behind spatio‐temporal patterns: building low‐complexity retinal models in CNN based on morphology, pharmacology and physiology , 2001, Int. J. Circuit Theory Appl..

[16]  Richard H Masland,et al.  The population of bipolar cells in the rabbit retina , 2004, The Journal of comparative neurology.

[17]  F. Rieke,et al.  Nonlinear Signal Transfer from Mouse Rods to Bipolar Cells and Implications for Visual Sensitivity , 2002, Neuron.

[18]  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.

[19]  J. B. Demb,et al.  Bipolar Cells Contribute to Nonlinear Spatial Summation in the Brisk-Transient (Y) Ganglion Cell in Mammalian Retina , 2001, The Journal of Neuroscience.

[20]  Richard H. Masland,et al.  The Diversity of Ganglion Cells in a Mammalian Retina , 2002, The Journal of Neuroscience.

[21]  Richard H Masland,et al.  Extreme Diversity among Amacrine Cells: Implications for Function , 1998, Neuron.

[22]  R. Masland,et al.  The shapes and numbers of amacrine cells: Matching of photofilled with Golgi‐stained cells in the rabbit retina and comparison with other mammalian species , 1999, The Journal of comparative neurology.

[23]  F S Werblin,et al.  Three Levels of Lateral Inhibition: A Space–Time Study of the Retina of the Tiger Salamander , 2000, The Journal of Neuroscience.

[24]  Tamás Roska,et al.  CNN software library (Templates and algorithms.) Version 7.2 , 1998 .