The spatial frequency sensitivity of bipolar cells

Interphotoreceptor coupling: an evolutionary perspective

In the vertebrate retina, signals generated by cones of different spectral preference and by highly sensitive rod photoreceptors interact at various levels to extract salient visual information. The first opportunity for such interaction is offered by electrical coupling of the photoreceptors themselves, which is mediated by gap junctions located at the contact points of specialised cellular processes: synaptic terminals, telodendria and radial fins. Here, we examine the evolutionary pressures for and against interphotoreceptor coupling, which are likely to have shaped how coupling is deployed in different species. The impact of coupling on signal to noise ratio, spatial acuity, contrast sensitivity, absolute and increment threshold, retinal signal flow and colour discrimination is discussed while emphasising available data from a variety of vertebrate models spanning from lampreys to primates. We highlight the many gaps in our knowledge, persisting discrepancies in the literature, as well as some major unanswered questions on the actual extent and physiological role of cone-cone, rod-cone and rod-rod communication. Lastly, we point toward limited but intriguing evidence suggestive of the ancestral form of coupling among ciliary photoreceptors.

The role of starburst amacrine cells in visual signal processing

Starburst amacrine cells (SBACs) within the adult mammalian retina provide the critical inhibition that underlies the receptive field properties of direction-selective ganglion cells (DSGCs). The SBACs generate direction-selective output of GABA that differentially inhibits the DSGCs. We review the biophysical mechanisms that produce directional GABA release from SBACs and test a network model that predicts the effects of reciprocal inhibition between adjacent SBACs. The results of the model simulations suggest that reciprocal inhibitory connections between closely spaced SBACs should be spatially selective, while connections between more widely spaced cells could be indiscriminate. SBACs were initially identified as cholinergic neurons and were subsequently shown to contain release both acetylcholine and GABA. While the role of the GABAergic transmission is well established, the role of the cholinergic transmission remains unclear.

Contrast sensitivity in seasonal and nonseasonal depression

BACKGROUND

Psychophysics has been used for the early diagnosis of many diseases that affect the visual pathway including those not usually considered vision-related (e.g., Parkinson's disease). Little has been done, however, to investigate visual functioning in psychological disorders known to be effectively treated by phototherapy. We measured the static and dynamic spatial contrast detection thresholds of seasonally depressed (SAD), nonseasonally depressed (Depressed) and nondepressed (Control) individuals.

METHODS

Two psychophysical experiments which measured luminance contrast detection thresholds were conducted. Experiment 1 presented static, vertically oriented Gabors with center spatial frequencies ranging from 0.3 to 12.0 cpd (cycles per degree). Experiment 2 presented 0.5, 1.5 and 4.0 cpd Gabors whose phases were sinusoidally reversed at 2.0, 4.0, 8.0, 16.0, and 32.0 c/s (Hz).

RESULTS

SAD showed significantly greater contrast sensitivities than Controls for static spatial frequencies equal to or greater than 6.0 cpd. Depressed showed significantly greater contrast sensitivities at 6.0 cpd and 12.0 cpd. With phase modulation, the SAD group showed significantly enhanced contrast sensitivity with 4.0 cpd-2.0 Hz Gabors. All other results at lower spatial-higher temporal frequencies were not significant.

LIMITATIONS

Most of the subjects were drawn from the student population instead of the community or clinics, even though they met the criteria for clinical depression. Antidepressant use was not controlled for among the subjects.

CONCLUSIONS

These findings suggest that clinical depression can enhance contrast sensitivity when stimuli elicit strong parvocellular responses. These enhancements implicate differences in retinal functionality. Mechanisms that link neuromodulatory activity to retinal signal processing are proposed.

Syncytial integration by a network of coupled bipolar cells in the retina

A model system for syncytial integration is the outer vertebrate retina, where graded signals or electrotonic potentials interact laterally via gap junctions to form an integrated response that is relayed by chemical synapses to the next layer of interconnected cells. Morphological and physiological experiments confirm that bipolar cells form quasisyncytial lattices, and so this review will aim to address two important issues: the function of coupling in visual information processing and the construction of a robust mathematical model that can adequately simulate signal spread in the bipolar cell syncytium. It is shown that the role of coupling in bipolar cells differs from that associated in the presynaptic networks, namely, loss in spatial resolution in order to increase the signal-to-noise ratio. The intrinsic membrane properties of bipolar cells which give rise to voltage-dependent currents are inactive over the normal in vivo operating range of membrane potential and may be shunted as a direct result of electrotonic coupling, suppressing any possibility of action potential propagation in the bipolar cell syncytium. It is therefore speculated that the mechanisms underlying processing of information in bipolar networks are dependent on the structure of bipolar cells and in particular, on the presence of gap junctions. It is proposed that a three-dimensional model which incorporates the spatial properties of each bipolar cell in the network in the form of a leaky cable is the most likely model to simulate signal spread in the bipolar cell syncytium in vivo. This is because discrete network models represent each bipolar cell in the syncytium as isopotential units without any spatial structure, and thus are unable to reproduce the temporal characteristics of electrotonic potential spread within the central receptive field of bipolar cells.

Nonlinear analysis of spatial vision using first-and-second-order Volterra transfer functions measurement

The harmonic input method of nonlinear system identification is modified to allow the Volterra series approach to be used for psychophysical investigation of various aspects of human pattern vision in the spatial frequency domain. While it is well known that only one modulation transfer function provides a complete characterization of a linear system, a number of multidimensional transfer functions are needed to identify a nonlinear system. We have shown, that so far as the contrast sensitivity to sine-wave gratings may be used for an empirical estimate of the first-order modulation transfer function of the human visual system, the contrast sensitivity to difference harmonics may be used as an empirical estimate of the second-order modulation transfer function. A difference harmonic arises from a mixture of two sine-wave gratings resulting from the nonlinearity of the visual system. Difference harmonic, experienced as some periodic beatlike structure, may still be observed if frequencies of the component gratings are higher than the maximum visual acuity. The visibility of the low-frequency beatlike pattern produced by pairs of sine-wave gratings, which themselves are of spatial frequencies too high to be resolved, could be accounted for either by a difference frequency distortion product (Burton, 1973) or by a special beat detector (Derrington & Badcock, 1985). We found that increasing the contrast of one component grating may be compensated for by reducing the contrast of the other component grating, the beatlike pattern being at threshold. This is exactly what would be expected if the beatlike pattern is detected because of the difference harmonics produced by nonlinearity of the visual system. We have determined contrast thresholds for the difference harmonics which occur between two unresolved different spatial frequencies. The contrast sensitivity function for difference harmonics was found to have a marked similarity both in the shape and position of peak sensitivity to the contrast sensitivity function for single sine-wave gratings. Another important characteristic of the contrast sensitivity function for difference harmonics is that it depends only on the frequency difference, delta f = f1 - f2, rather than on the value of either f1 or f2. All this indicates that a difference harmonic arises from local nonlinearities in the visual system. More specifically, the visual system may be represented as a cascade system, composed of a linear system with transfer function O (f) followed by a nonlinear element, r(.), without spatial spread in cascade with another linear system with transfer function P (f).(ABSTRACT TRUNCATED AT 400 WORDS)

A model for spatiotemporal frequency responses in the X cell pathway of the cat's retina

A linear model is described for the cat eye's signal-processing pathway, from the visual stimulus at the cornea, to cones, to X-type ganglion cells. The model contains elements representing the eye's optics, phototransduction, gain control, spatiotemporal processing by cell layers, and pure delay. Centre-surround antagonism in the model arises through the presence of a centre element producing a small spatial spread of signals, and an antagonistic element producing a larger spread. Two arrangements were tried, feedforward and feedback, in which the antagonistic element's output was subtracted from the centre element's output, and input, respectively. The model was fitted to empirical spatial and temporal frequency responses collected by Frishman et al. (1987), and accounted qualitatively for these data in the feedback, but not the feedforward, arrangement. The model's centre pathway comprises a cascade of low-pass spatial filters, as does the surround pathway. As a consequence, the spatial frequency responses for these two pathways closely approximate Gaussian functions of spatial frequency, and the spatial frequency response of the complete model at low temporal frequency closely matches that of the difference of Gaussians model.