Biomaterial Database

A unified neural network model of spatiotemporal processing in X and Y retinal ganglion cells. II. Temporal adaptation and simulation of experimental data. 1992

P Gaudiano

Department of Cognitive & Neural Systems, Boston University, MA 02215.

This article makes use of a push-pull shunting network, which was introduced in the companion article, to model certain properties of X and Y retinal ganglion cells. Input to the push-pull network is preprocessed by a nonlinear mechanism for temporal adaptation, which is ascribed here to photoreceptor dynamics. The complete circuit is used to show that a simple change in receptive field morphology within a single model equation can change the network's response characteristics to closely resemble those of either X or Y cells. Specifically, an increase in width of the receptive field center mechanism is sufficient to account for generation of on-off (Y-like) instead of null (X-like) responses to modulated gratings. In agreement with experimental data, the Y cell on-off response is independent of spatial phase. Also, the model accurately predicts that on-off responses can be observed in X cells for particular stimulus configurations. Taken together, the results show how the retina combines individually inadequate modules to efficiently handle the tasks required for accurate spatial and temporal visual information processing. The model is also able to clarify a number of controversial experimental findings on the nature of spatiotemporal visual processing in the retina.

UI	MeSH Term	Description	Entries
D008433	Mathematics	The deductive study of shape, quantity, and dependence. (From McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)	Mathematic
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D010786	Photoreceptor Cells	Specialized cells that detect and transduce light. They are classified into two types based on their light reception structure, the ciliary photoreceptors and the rhabdomeric photoreceptors with MICROVILLI. Ciliary photoreceptor cells use OPSINS that activate a PHOSPHODIESTERASE phosphodiesterase cascade. Rhabdomeric photoreceptor cells use opsins that activate a PHOSPHOLIPASE C cascade.	Ciliary Photoreceptor Cells,Ciliary Photoreceptors,Rhabdomeric Photoreceptor Cells,Rhabdomeric Photoreceptors,Cell, Ciliary Photoreceptor,Cell, Photoreceptor,Cell, Rhabdomeric Photoreceptor,Cells, Ciliary Photoreceptor,Cells, Photoreceptor,Cells, Rhabdomeric Photoreceptor,Ciliary Photoreceptor,Ciliary Photoreceptor Cell,Photoreceptor Cell,Photoreceptor Cell, Ciliary,Photoreceptor Cell, Rhabdomeric,Photoreceptor Cells, Ciliary,Photoreceptor Cells, Rhabdomeric,Photoreceptor, Ciliary,Photoreceptor, Rhabdomeric,Photoreceptors, Ciliary,Photoreceptors, Rhabdomeric,Rhabdomeric Photoreceptor,Rhabdomeric Photoreceptor Cell
D000222	Adaptation, Physiological	The non-genetic biological changes of an organism in response to challenges in its ENVIRONMENT.	Adaptation, Physiologic,Adaptations, Physiologic,Adaptations, Physiological,Adaptive Plasticity,Phenotypic Plasticity,Physiological Adaptation,Physiologic Adaptation,Physiologic Adaptations,Physiological Adaptations,Plasticity, Adaptive,Plasticity, Phenotypic
D012165	Retinal Ganglion Cells	Neurons of the innermost layer of the retina, the internal plexiform layer. They are of variable sizes and shapes, and their axons project via the OPTIC NERVE to the brain. A small subset of these cells act as photoreceptors with projections to the SUPRACHIASMATIC NUCLEUS, the center for regulating CIRCADIAN RHYTHM.	Cell, Retinal Ganglion,Cells, Retinal Ganglion,Ganglion Cell, Retinal,Ganglion Cells, Retinal,Retinal Ganglion Cell
D014785	Vision, Ocular	The process in which light signals are transformed by the PHOTORECEPTOR CELLS into electrical signals which can then be transmitted to the brain.	Vision,Light Signal Transduction, Visual,Ocular Vision,Visual Light Signal Transduction,Visual Phototransduction,Visual Transduction,Phototransduction, Visual,Transduction, Visual
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron