Distinct expressions of contrast gain control in parallel synaptic pathways converging on a retinal ganglion cell

Abstract

Visual neurons adapt to increases in stimulus contrast by reducing their response sensitivity and decreasing their integration time, a collective process known as 'contrast gain control.' In retinal ganglion cells, gain control arises at two stages: an intrinsic mechanism related to spike generation, and a synaptic mechanism in retinal pathways. Here, we tested whether gain control is expressed similarly by three synaptic pathways that converge on an OFF α/Y-type ganglion cell: excitatory inputs driven by OFF cone bipolar cells; inhibitory inputs driven by ON cone bipolar cells; and inhibitory inputs driven by rod bipolar cells. We made whole-cell recordings of membrane current in guinea pig ganglion cells in vitro. At high contrast, OFF bipolar cell-mediated excitatory input reduced gain and shortened integration time. Inhibitory input was measured by clamping voltage near 0 mV or by recording in the presence of ionotropic glutamate receptor (iGluR) antagonists to isolate the following circuit: cone → ON cone bipolar cell → AII amacrine cell → OFF ganglion cell. At high contrast, this input reduced gain with no effect on integration time. Mean luminance was reduced 1000-fold to recruit the rod bipolar pathway: rod → rod bipolar cell → AII cell → OFF ganglion cell. The spiking response, measured with loose-patch recording, adapted despite essentially no gain control in synaptic currents. Thus, cone bipolar-driven pathways adapt differently, with kinetic effects confined to the excitatory OFF pathway. The ON bipolar-mediated inhibition reduced gain at high contrast by a mechanism that did not require an iGluR. Under rod bipolar-driven conditions, ganglion cell firing showed gain control that was explained primarily by an intrinsic property.

Adaptation to Background Light Enables Contrast Coding at Rod Bipolar Cell Synapses

“…Using a linear-nonlinear (L-N) cascade analysis, we extracted a linear filter and a static nonlinearity (Beaudoin et al, 2008; Chichilnisky, 2001; Kim and Rieke, 2001). The filter reflects temporal processing by the presynaptic circuit and postsynaptic ligand-gated receptor channels (Figure 5I); the nonlinearity shows the relationship between the filtered stimulus and synaptic transmission to the recorded cell (Figure 5I, inset).…”

“…The white-noise stimuli presented with the were programmed in Matlab (Psychophysics Toolbox; frame rate = 60 Hz); in this case, the mean luminance of the background was equal to the mean of the spot (300 R*/rod/s). The stimulus included periods for building and validating a linear-nonlinear cascade model (Beaudoin et al, 2008; Wang et al, 2011). Photoisomerization rates were calculated based on a collecting area of 0.85 μm 2 for rods (Lyubarsky et al, 2004; Naarendorp et al, 2010; Wang et al, 2011).…”

Integrative properties of retinal ganglion cell electrical responsiveness depend on neurotrophic support and genotype in the mouse

“…Thus, it is possible to investigate whether the integrative properties of inner retina neurons and their response dynamics depend on the stimulus contrast without the confounding effects of outer retina activity. Contrast gain control mechanisms are known to play a major role at sites where there is a large convergence of neural inputs to target neurons (e.g., (Beaudoin et al, 2008)) as in the case of mouse RGCs (Jeon et al, 1998), which may adjust their responsiveness at different contrasts to allow more efficient use of their dynamic range.…”

“…The relationship relating PERG amplitude with contrast may be non-linear (Porciatti et al, 2010). Departure from linearity implies that mechanisms of contrast gain control are at play in the PERG generators (Beaudoin et al, 2008; Carandini and Heeger, 2012; Demb, 2008; Demb et al, 1999; Shapley and Enroth-Cugell, 1984), which this study seeks to quantify. Quantitative analysis of non-linearities in contrast response functions is a laborious procedure that requires recording a series of PERG waveforms at close-spaced contrasts and fitting corresponding amplitude/latency data with suitable mathematical functions (e.g., (Albrecht and Hamilton, 1982)).…”

“…Also, the response gain (strength of response relative to the strength of the stimulus) may change for different levels of stimulus strengths to adapt the limited dynamic range of neurons to the spatio-temporal statistics of the stimulus. Changes in both response latency and gain with contrast have been explained within neural gain control modeling frameworks (Beaudoin et al, 2008; Carandini and Heeger, 2012; Demb, 2008; Demb et al, 1999; Shapley and Enroth-Cugell, 1984). We show that the integrative properties of the PERG recorded in the standard C57BL/6J inbred mouse strain are modifiable in response to alterations of neurotrophic support.…”