A study of the response properties of retinula cells of flies using nonlinear identification theory

Gemperlein, R.; McCann, G. D.

doi:10.1007/bf00337254

Cited by 18 publications

(6 citation statements)

References 4 publications

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“…1. The firstorder kernel had a form similar to those measured previously by flash and white noise stimuli (Eckert and Bishop, 1975; Gemperlein and McCann, 1975;Marmarelis and McCann, 1977;Weckstrom et al, 1988). The effectiveness of the parallel cascade model, and the kernels obtained, were independent of the order in which the step responses were combined.…”

Section: Resultsmentioning

confidence: 56%

“…Surprisingly, the strong second-order nonlinearity was almost entirely limited to the diagonal. Previous kernels obtained from white noise stimulation (Eckert and Bishop, 1975;Gemperlein and McCann, 1975;Marmarelis and McCann, 1977;Pece et al, 1990) and paired flash experiments (Pece et al, 1990) had significant off-diagonal components. These differences presumably arose from the different method used here, which tested the cells over a large part of their normal operating range.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, photoreceptors behave as dynamic nonlinear systems and require appropriate techniques for their analysis. Surprisingly, previous investigations have yielded only a small second-order nonlinear contribution to the response (Eckert and Bishop, 1975; Gemperlein and McCann, 1975; Marmarelis and McCann, 1977;Pece et al, 1990) or pointed to an almost totally linear system (French and Jarvilehto, 1978;French, 1980;Weckstrom et al, 1988). The method used in these studies, and a popular general method for analyzing dynamic nonlinear systems, is to use a random or pseudo-random stimulus and measure the Wiener kernels (Marmarelis and Marmarelis, 1978;Sakai, 1992).…”

Section: Introductionmentioning

confidence: 92%

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The dynamic nonlinear behavior of fly photoreceptors evoked by a wide range of light intensities

French¹,

Korenberg²,

Järvilehto³

et al. 1993

Biophysical Journal

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Fly photoreceptor cells were stimulated with steps of light over a wide intensity range. First- and second-order Volterra kernels were then computed from sequences of combined step responses. Diagonal values of the second-order Volterra kernels were much greater than the off-diagonal values, and the diagonal values were roughly proportional to the corresponding first-order kernels, suggesting that the response could be approximated by a static nonlinearity followed by a dynamic linear component (Hammerstein model). The amplitudes of the second-order kernels were much smaller in light-adapted than in dark-adapted photoreceptors. Hammerstein models constructed from the step input/output measurements gave reasonable approximations to the actual photoreceptor responses, with light-adapted responses being relatively better fitted. However, Hammerstein models could not account for several features of the photoreceptor behavior, including the dependence of the step response shape on step amplitude. A model containing an additional static nonlinearity after the dynamic linear component gave significantly better fits to the data. These results indicate that blowfly photoreceptors have a strong early gain control nonlinearity acting before the processes that create the characteristic time course of the response, in addition to the nonlinearities caused by membrane conductances.

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Section: Resultsmentioning

confidence: 56%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 92%

See 1 more Smart Citation

The dynamic nonlinear behavior of fly photoreceptors evoked by a wide range of light intensities

French¹,

Korenberg²,

Järvilehto³

et al. 1993

Biophysical Journal

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“…In electrophysiological measurements, there can sometimes be a linear relationship between stimulus intensities and response amplitudes for small modulations of the stimulus, particularly for flicker of a constant illumination (16,17). In the linear range of response, the response magnitudes are then proportional to the number of quanta which are effectively absorbed.…”

Section: Linear-response Methodsmentioning

confidence: 99%

Electrophysiological measurements of spectral sensitivities: a review

DeVoe

de-Souza²,

Ventura³

1997

Braz J Med Biol Res

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Spectral sensitivities of visual systems are specified as the reciprocals of the intensities of light (quantum fluxes) needed at each wavelength to elicit the same criterion amplitude of responses. This review primarily considers the methods that have been developed for electrophysiological determinations of criterion amplitudes of slow-wave responses from single retinal cells. Traditional flash methods can require tedious dark adaptations and may yield erroneous spectral sensitivity curves which are not seen in such modifications as ramp methods. Linear response methods involve interferometry, while constant response methods involve manual or automatic adjustments of continuous illumination to keep response amplitudes constant during spectral scans. In DC or AC computerized constant response methods, feedback to determine intensities at each wavelength is derived from the response amplitudes themselves. Although all but traditional flash methods have greater or lesser abilities to provide on-line determinations of spectral sensitivities, computerized constant response methods are the most satisfactory due to flexibility, speed and maintenance of a constant adaptation level. Correspondence

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“…We may call this the recognition problem without implying consciousness. Much has been written either under this name or under terms like sensory-motor linkage, or triggers, or behavior-switching mechanisms [sample references include Schneider, 1967;Griisser andGriisserCornehls, 1968, 1969;Ingle, 1968Ingle, , 1970Ingle, , 1982Ingle, , 1983Gose, 1969;McCann and Dill, 1969;Grossberg, 1970;Watanabe, 1972;Wehner, 1972;Wiersma and York, 1972;Ewert, 1973;Gross, 1973;Uttal, 1973;Ewert and von Seelen, 1974;Hoeppner, 1974;Gemperlein and McCann, 1975;Henry, 1977;Rowell et al, 1977;Ewert et al, 1978;Hyvarinen and Poranen, 1978;Whitfield, 1979Whitfield, , 1984Schlag et al, 1980;Reichardt and Poggio, 1981;Arbib, 1982, 1985;Roth and Jordan, 1982;Huber and Thorson, 1985].…”

Section: Recognition Revisitedmentioning

confidence: 99%

Some Principles in the Brain Analysis of Important Signals: Mapping and Stimulus Recognition

Bullock¹

1986

Brain Behav Evol

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This essay addresses two questions: (1) What are the meanings of different kinds of brain maps? (2) Can the recognition of important communicative signals and other stimuli known to the system be adequately explained by an ensemble code? Several kinds and grades of brain maps are distinguished. Before we can properly state the meaning, especially of the higher forms of multiple and multifactor maps, we need to learn where they project. At the higher levels this is a labor-intensive task, requiring also ingenuity and ethological thinking to devise stimuli. The neuronal basis of recognition in those cases where behavior or perception is triggered in an either-or manner has chiefly been attributed to one or the other of two classes of models: (a) a large spatiotemporal configuration of many kinds of cells in which there is little convergence of input, and (b) a small set of nearly equivalent cells after successive convergences. The latter is known to exist but it is not clear how far it goes or for what classes of stimuli. The former is a more popular view but carries several burdens in required assumptions and is essentially not demonstrable or disprovable. I believe both exist and operate in sequence: specific ensembles are inputs to small sets of equivalent recognition units and specific arrays of different recognition units at higher and higher levels of abstraction may constitute the specific configurations for the most sophisticated recognitions. Our information base is pitifully small, especially in comparative physiology of higher integrative functions of the brain and in comparative behaviour.

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A study of the response properties of retinula cells of flies using nonlinear identification theory

Cited by 18 publications

References 4 publications

The dynamic nonlinear behavior of fly photoreceptors evoked by a wide range of light intensities

The dynamic nonlinear behavior of fly photoreceptors evoked by a wide range of light intensities

Electrophysiological measurements of spectral sensitivities: a review

Some Principles in the Brain Analysis of Important Signals: Mapping and Stimulus Recognition

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