Literature DB >> 7276237

Population magnitudes and distribution of the major modal classes of cat retinal ganglion cell as estimated from HRP filling and a systematic survey of the soma diameter spectra for classical neurones.

A Hughes.   

Abstract

A survey of diameter spectra in presented for classic neurones of the cat retinal ganglion cell layer. From these, with the aid of retrograde HRP filling for central retina, a set of density distribution maps has been prepared for each of the major modes of the neuronal diameter spectrum. The total population of classical neurones, the presumed ganglion cells of Hughes ('75), confirms published values with a minimum of some 207,000 comprised 5,600 cells in the alpha mode, 80,700 cells in the beta mode, and 120,700 cells in the gamma mode. A proportion of classical neurones in the gamma mode do not fill by retrograde transport of HRP from either optic nerve or superior colliculus. Their morphology is characteristic and includes a conspicuous basophilic nuclear bar or fold; they remain subsequent to ganglion cell retrograde degeneration and resemble some profiles of the amacrine layer. It is presumed that they represent a class of displaced amacrine cells. Estimates based on ganglion cell identification by HRP filling indicate populations of about 80,000 cells in both the gamma and the beta modes and a total count of about 170,000 ganglion cells; a good agreement with Hughes and Wässle's ('76) optic nerve fibre count, but lower than the classic neurone count. It is concluded that the distribution maps for ganglion cells in each of the three modes of the soma diameter spectra are similar in form and resemble that of the total neurone density map. The ganglion cell population of the gamma mode in the visual streak is not found to increase in proportion relative to that of the beta mode, as has been reported elsewhere.

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Year:  1981        PMID: 7276237     DOI: 10.1002/cne.901970209

Source DB:  PubMed          Journal:  J Comp Neurol        ISSN: 0021-9967            Impact factor:   3.215


  22 in total

1.  Functional consequences of neuronal divergence within the retinogeniculate pathway.

Authors:  Chun-I Yeh; Carl R Stoelzel; Chong Weng; Jose-Manuel Alonso
Journal:  J Neurophysiol       Date:  2009-01-28       Impact factor: 2.714

2.  Combined application of BDNF to the eye and brain enhances ganglion cell survival and function in the cat after optic nerve injury.

Authors:  Arthur J Weber; Suresh Viswanáthan; Chidambaram Ramanathan; Christine D Harman
Journal:  Invest Ophthalmol Vis Sci       Date:  2009-08-26       Impact factor: 4.799

3.  Morphology of P and M retinal ganglion cells of the bush baby.

Authors:  E S Yamada; D W Marshak; L C Silveira; V A Casagrande
Journal:  Vision Res       Date:  1998-11       Impact factor: 1.886

4.  Topographic variations in W-cell input to cat superior colliculus.

Authors:  D M Berson; J Lu; J J Stein
Journal:  Exp Brain Res       Date:  1990       Impact factor: 1.972

5.  Retinal inputs and laminar distributions of the dorsal lateral geniculate nucleus relay cells in the eastern chipmunk (Tamias sibiricus asiaticus).

Authors:  K Morigiwa; H Sawai; K Wakakuwa; Y Mitani-Yamanishi; Y Fukuda
Journal:  Exp Brain Res       Date:  1988       Impact factor: 1.972

6.  Orientation bias of brisk-transient y-cells of the cat retina for drifting and alternating gratings.

Authors:  L N Thibos; W R Levick
Journal:  Exp Brain Res       Date:  1985       Impact factor: 1.972

7.  A statistical analysis and comparison of soma diameter spectra for classical neurones from different regions of the cat retinal ganglion cell layer.

Authors:  A Hughes; D Caille; J F Vibert
Journal:  Pflugers Arch       Date:  1980-12       Impact factor: 3.657

8.  Response to the velocity of moving visual stimuli of the brisk classes of ganglion cells in the cat retina.

Authors:  B G Cleland; T H Harding
Journal:  J Physiol       Date:  1983-12       Impact factor: 5.182

9.  Statistical wiring of thalamic receptive fields optimizes spatial sampling of the retinal image.

Authors:  Luis M Martinez; Manuel Molano-Mazón; Xin Wang; Friedrich T Sommer; Judith A Hirsch
Journal:  Neuron       Date:  2014-02-19       Impact factor: 17.173

10.  A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES.

Authors:  Qiwei Li; Xinlei Wang; Faming Liang; Guanghua Xiao
Journal:  Ann Appl Stat       Date:  2019-10-17       Impact factor: 2.083

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