Modeling the variability of firing rate of retinal ganglion cells. 1992

M W Levine
University of Illinois, Department of Psychology and Committee on Neuroscience, Chicago 60680.

Impulse trains simulating the maintained discharges of retinal ganglion cells were generated by digital realizations of the integrate-and-fire model. If the mean rate were set by a "bias" level added to "noise," the variability of firing would be related to the mean firing rate as an inverse square root law; the maintained discharges of retinal ganglion cells deviate systematically from such a relationship. A more realistic relationship can be obtained if the integrate-and-fire mechanism is "leaky"; with this refinement, the integrate-and-fire model captures the essential features of the data. However, the model shows that the distribution of intervals is insensitive to that of the underlying variability. The leakage time constant, threshold, and distribution of the noise are confounded, rendering the model unspecifiable. Another aspect of variability is presented by the variance of responses to repeated discrete stimuli. The variance of response rate increases with the mean response amplitude; the nature of that relationship depends on the duration of the periods in which the response is sampled. These results have defied explanation. But if it is assumed that variability depends on mean rate in the way observed for maintained discharges, the variability of responses to abrupt changes in lighting can be predicted from the observed mean responses. The parameters that provide the best fits for the variability of responses also provide a reasonable fit to the variability of maintained discharges.

UI MeSH Term Description Entries
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
D002415 Cats The domestic cat, Felis catus, of the carnivore family FELIDAE, comprising over 30 different breeds. The domestic cat is descended primarily from the wild cat of Africa and extreme southwestern Asia. Though probably present in towns in Palestine as long ago as 7000 years, actual domestication occurred in Egypt about 4000 years ago. (From Walker's Mammals of the World, 6th ed, p801) Felis catus,Felis domesticus,Domestic Cats,Felis domestica,Felis sylvestris catus,Cat,Cat, Domestic,Cats, Domestic,Domestic Cat
D004594 Electrophysiology The study of the generation and behavior of electrical charges in living organisms particularly the nervous system and the effects of electricity on living organisms.
D000200 Action Potentials Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli. Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D001369 Axons Nerve fibers that are capable of rapidly conducting impulses away from the neuron cell body. Axon
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
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model

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