Signal transmission in the catfish retina. V. Sensitivity and circuit

Abstract

1. We analyzed the light-evoked responses of retinal neurons by means of a white-noise technique. Horizontal and bipolar cells produced a modulation response that was linearly related to a modulation of the mean luminance of a large field of light. The first-order kernels were capable of reproducing the cells' modulation response with a fair degree of accuracy. The amplitude as well as the waveform of the kernels changed with the change in the mean luminance. This is a parametric change and is a form of field adaptation. As the time constant of the parametric change was much longer than that of the modulation response (memory), neurons were assumed to be at a dynamic steady state at a given mean luminance. 2. With the presence of a steady annular illumination, the first-order kernel derived from stimulation with a small spot of light became faster in peak response time and larger in amplitude. For horizontal-cell somas and bipolar cells, the surround also linearized their modulation response. This surround enhancement has been seen in all the cone-driven retinal cells except the receptor and horizontal cell axon, in which a steady surround decreased the amplitude of the spot-evoked kernel but shortened the peak response time. 3. A change in the modulation depth did not affect either the amplitude or the wave-form of the first-order kernels from the horizontal and bipolar cells. In the amacrine and ganglion cells, on the other hand, the amplitude of kernels was related inversely to the depth of modulation. These cells were more sensitive to the modulation of a small modulation depth. 4. A static nonlinearity appeared when signals were transmitted to the amacrine cells. The nonlinearity was first produced in the type-C amacrine cells by a process, which could be modeled by squaring the bipolar cell response. A gamut of more complex second-order nonlinearities found in type-N amacrine cells could be modeled by a band-pass filtering of the type-C cell response. Linear components in the bipolar cells and nonlinear components in the amacrine cells are encoded into spike trains in the ganglion cells. Thus, under our simple stimulus regimen, the ganglion cells transformed the results of the preganglionic signal processing into a spike train without much modification. 5. We propose a tentative diagram of the signal flow in the cone-driven catfish retinal neurons based on this and previous studies.(ABSTRACT TRUNCATED AT 400 WORDS)

A retinal circuit model accounting for wide-field amacrine cells

“…The model covers the response dynamics of all major retinal neurons in the cellular level, together with their spatio-temporal interactions. For each type of the neurons in our model, the first-order Wiener kernel of the cellular response was calculated by the white-noise analysis (Marmarelis and Naka 1973;Naka et al 1987;Sakai and Naka 1987), showing a reasonable consistency with the kernels previously measured in real retinal neurons in situ. The present model could realize the OMS retinal output, and was useful to examine the role of each neuron in such a higher-order visual computation in the retinal circuit.…”

Synaptic transmission at retinal ribbon synapses

“…Linearity in the transmission of small signals from rod photoreceptors to their post-synaptic targets has long been known (Tranchina et al, 1981;Sakai and Naka, 1987;Naka et al, 1987;Wu, 1985); it allows a rod to translate a small light-evoked hyperpolarization from the dark potential into a proportional small change in the magnitude of I Ca and exocytosis. These studies of synaptic gain, however, do not establish an underlying linear mechanism because the predictions using models based either on linear or higher-order relations between Ca 2+ influx and transmitter release do not differ greatly for small signals.…”

Reverse Correlation and the VESPA Method

“…In particular, a large number of studies of the visual system have utilized quasiwhite light stimulation in order to derive a model of the behavior of cells, from photoreceptors through to simple V1 cells (Marmarelis and McCann 1977;Sakai andNaka 1987, Meister et al 1994;Jones and Palmer 1987;DeAngelis et al 1993). At a more macroscopic scale, the Volterra-Wiener approach has been applied to EEG and, in particular, the visual evoked potential (Coppola 1979).…”