Literature DB >> 3498795

Dynamics of the ganglion cell response in the catfish and frog retinas.

M Sakuranaga1, Y Ando, K Naka.   

Abstract

Responses were evoked from ganglion cells in catfish and frog retinas by a Gaussian modulation of the mean luminance. An algorithm was devised to decompose intracellularly recorded responses into the slow and spike components and to extract the time of occurrence of a spike discharge. The dynamics of both signals were analyzed in terms of a series of first-through third-order kernels obtained by cross-correlating the slow (analog) or spike (discrete or point process) signals against the white-noise input. We found that, in the catfish, (a) the slow signals were composed mostly of postsynaptic potentials, (b) their linear components reflected the dynamics found in bipolar cells or in the linear response component of type-N (sustained) amacrine cells, and (c) their nonlinear components were similar to those found in either type-N or type-C (transient) amacrine cells. A comparison of the dynamics of slow and spike signals showed that the characteristic linear and nonlinear dynamics of slow signals were encoded into a spike train, which could be recovered through the cross-correlation between the white-noise input and the spike (point process signals. In addition, well-defined spike correlates could predict the observed slow potentials. In the spike discharges from frog ganglion cells, the linear (or first-order) kernels were all inhibitory, whereas the second-order kernels had characteristics of on-off transient excitation. The transient and sustained amacrine cells similar to those found in catfish retina were the sources of the nonlinear excitation. We conclude that bipolar cells and possibly the linear part of the type-N cell response are the source of linear, either excitatory or inhibitory, components of the ganglion cell responses, whereas amacrine cells are the source of the cells' static nonlinearity.

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Year:  1987        PMID: 3498795      PMCID: PMC2228836          DOI: 10.1085/jgp.90.2.229

Source DB:  PubMed          Journal:  J Gen Physiol        ISSN: 0022-1295            Impact factor:   4.086


  35 in total

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Authors:  H M Sakai; K Naka; J E Dowling
Journal:  Nature       Date:  1986 Feb 6-12       Impact factor: 49.962

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Authors:  M Mizunami; H Tateda; K Naka
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Authors:  K I Naka; M A Itoh; R L Chappell
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  16 in total

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Authors:  Y Kondoh; H Morishita; T Arima; J Okuma; Y Hasegawa
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Review 5.  The identification of nonlinear biological systems: Wiener kernel approaches.

Authors:  M J Korenberg; I W Hunter
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6.  The identification of nonlinear biological systems: Volterra kernel approaches.

Authors:  M J Korenberg; I W Hunter
Journal:  Ann Biomed Eng       Date:  1996 Mar-Apr       Impact factor: 3.934

7.  Neural circuitry underlying linear representation of wind information in a nonspiking local interneuron of the cockroach.

Authors:  J Okuma; Y Kondoh
Journal:  J Comp Physiol A       Date:  1996-12       Impact factor: 1.836

8.  A nonlinear cascade model for action potential encoding in an insect sensory neuron.

Authors:  A S French; M J Korenberg
Journal:  Biophys J       Date:  1989-04       Impact factor: 4.033

9.  Generation and transformation of second-order nonlinearity in catfish retina.

Authors:  K Naka; H M Sakai; N Ishii
Journal:  Ann Biomed Eng       Date:  1988       Impact factor: 3.934

10.  White-noise analysis of nonlinear behavior in an insect sensory neuron: kernel and cascade approaches.

Authors:  M J Korenberg; A S French; S K Voo
Journal:  Biol Cybern       Date:  1988       Impact factor: 2.086

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