Contrast Adaptation in Simple Cells by Changing the Transmitter Release Probability (original) (raw)
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A Synaptic Mechanism for Retinal Adaptation to Luminance and Contrast
Journal of Neuroscience, 2011
The gain of signaling in primary sensory circuits is matched to the stimulus intensity by the process of adaptation. Retinal neural circuits adapt to visual scene statistics, including the mean (background adaptation) and the temporal variance (contrast adaptation) of the light stimulus. The intrinsic properties of retinal bipolar cells and synapses contribute to background and contrast adaptation, but it is unclear whether both forms of adaptation depend on the same cellular mechanisms. Studies of bipolar cell synapses identified synaptic mechanisms of gain control, but the relevance of these mechanisms to visual processing is uncertain because of the historical focus on fast, phasic transmission rather than the tonic transmission evoked by ambient light. Here, we studied use-dependent regulation of bipolar cell synaptic transmission evoked by small, ongoing modulations of membrane potential (V M) in the physiological range. We made paired whole-cell recordings from rod bipolar (RB) and AII amacrine cells in a mouse retinal slice preparation. Quasi-white noise voltage commands modulated RB V M and evoked EPSCs in the AII. We mimicked changes in background luminance or contrast, respectively, by depolarizing the V M or increasing its variance. A linear systems analysis of synaptic transmission showed that increasing either the mean or the variance of the presynaptic V M reduced gain. Further electrophysiological and computational analyses demonstrated that adaptation to mean potential resulted from both Ca channel inactivation and vesicle depletion, whereas adaptation to variance resulted from vesicle depletion alone. Thus, background and contrast adaptation apparently depend in part on a common synaptic mechanism.
An intracellular study of the contrast-dependence of neuronal activity in cat visual cortex
Cerebral Cortex, 1997
Extracellular recordings indicate that mechanisms that control contrast gain of neuronal discharge are found in the retina, thalamus and cortex. In addition, the cortex is able to adapt its contrast response function to match the average local contrast. Here we examine the neuronal mechanism of contrast adaptation by direct intracellular recordings in vivo. Both simple (n = 3) and complex cells (n = 4) show contrast adaptation during intracellular recording. For simple cells, that the amplitude of fluctuations in membrane potential induced by a drifting grating stimulus follows a contrast response relation similar to lateral geniculate relay cells, and does not reflect the high gain and adaptive properties seen in the action potential discharge of the neurons. We found no evidence of significant shunting inhibition that could explain these results. In complex cells there was no change in the mean membrane potential for different contrast stimuli or different states of adaptation, despite marked changes in discharge rate. We use a simplified electronic model to discuss the central features of our results and to explain the disparity between the contrast response functions of the membrane potential and action potential discharge in simple cells.
The Journal of physiology, 2008
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 alpha/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 isola...
Response to Contrast of Electrophysiologically Defined Cell Classes in Primary Visual Cortex
The Journal of Neuroscience, 2003
Information processing in the visual cortex is critically dependent on the input-output relationships of its component neurons. The transformation of synaptic inputs into spike trains depends in turn on the host of intrinsic membrane properties expressed by neurons, which define established electrophysiological cell classes in the neocortex. Here we studied, with intracellular recordingsin vivo, how the electrophysiological cell classes in the primary visual cortex transform an increasing input, represented by stimulus contrast, into membrane depolarization and trains of action potentials. We used contrast as input because, regardless of their stimulus selectivity, primary visual cortical cells increase their firing rates in response to increases in luminance contrast. We found that both the spike rate response and the membrane potential response are best described by the hyperbolic ratio function when compared with linear, power, and logarithmic functions. In addition, both respons...
Presynaptic Mechanism for Slow Contrast Adaptation in Mammalian Retinal Ganglion Cells
Neuron, 2006
Visual neurons, from retina to cortex, adapt slowly to stimulus contrast. Following a switch from high to low contrast, a neuron rapidly decreases its responsiveness and recovers over 5-20 s. Cortical adaptation arises from an intrinsic cellular mechanism: a sodiumdependent potassium conductance that causes prolonged hyperpolarization. Spiking can drive this mechanism, raising the possibility that the same mechanism exists in retinal ganglion cells. We found that adaptation in ganglion cells corresponds to a slowly recovering afterhyperpolarization (AHP), but, unlike in cortical cells, this AHP is not primarily driven by an intrinsic cellular property: spiking was not sufficient to generate adaptation. Adaptation was strongest following spatial stimuli tuned to presynaptic bipolar cells rather than the ganglion cell; it was driven by a reduced excitatory conductance, and it persisted while blocking GABA and glycine receptors, K (Ca) channels, or mGluRs. Thus, slow adaptation arises from reduced glutamate release from presynaptic (nonspiking) bipolar cells.
Journal of Neuroscience, 2007
Simple cells in layer 4 of the primary visual cortex of the cat show contrast-invariant orientation tuning, in which the amplitude of the peak response is proportional to the stimulus contrast but the width of the tuning curve hardly changes with contrast. This study uses a detailed model of spiny stellate cells (SSCs) from cat area 17 to explain this property. The model integrates our experimental data, including morphological and intrinsic membrane properties and the number and spatial distribution of four major synaptic input sources of the SSC: the dorsal lateral geniculate nucleus (dLGN) and three cortical sources. The model also includes synaptic properties of these inputs. The cortical input served as sources of background activity, and visual stimuli was modeled as sinusoidal grating. For all contrasts, strong synaptic depression of the dLGN feedforward afferents compresses the firing rates in response to orthogonal stimuli, keeping these rates at practically the same low level. However, at preferred orientations, despite synaptic depression, firing rate changes as a function of contrast. Thus, when embedded in an active network, strong synaptic depression can explain contrast-invariant orientation tuning of simple cells. This is true also when the dLGN inputs are partially depressed as a result of their spontaneous activity and to some extent also when parameters were fitted to a more moderate level of synaptic depression. The model response is in close agreement with experimental results, in terms of both output spikes and membrane voltage (amplitude and fluctuations), with reasonable exceptions given that recurrent connections were not incorporated.
Contrast Adaptation in Subthreshold and Spiking Responses of Mammalian Y-Type Retinal Ganglion Cells
The Journal of Neuroscience, 2005
Retinal ganglion cells adapt their responses to the amplitude of fluctuations around the mean light level, or the “contrast.” But, in mammalian retina, it is not known whether adaptation arises exclusively at the level of synaptic inputs or whether there is also adaptation in the process of ganglion cell spike generation. Here, we made intracellular recordings from guinea pig Y-type ganglion cells and quantified changes in contrast sensitivity (gain) using a linear-nonlinear analysis. This analysis allowed us to measure adaptation in the presence of nonlinearities, such as the spike threshold, and to compare adaptation in subthreshold and spiking responses. At high contrast (0.30), relative to low contrast (0.10), gain reduced to 0.82 ± 0.016 (mean ± SEM) for the subthreshold response and to 0.61 ± 0.011 for the spiking response. Thus, there was an apparent reduction in gain between the subthreshold and spiking response of 0.74 ± 0.013. Control experiments suggested that the above e...
Journal of The Optical Society of America A-optics Image Science and Vision, 2007
When cat V1/V2 cells are adapted to contrast at their optimal orientation, a reduction in gain and/or a shift in the contrast response function is found. We investigated how these factors combine at the population level to affect the accuracy for detecting variations in contrast. Using the contrast response function parameters from a physiologically measured population, we model the population accuracy (using Fisher information) for contrast discrimination. Adaptation at 16%, 32%, and 100% contrast causes a shift in peak accuracy. Despite an overall drop in firing rate over the whole population, accuracy is enhanced around the adapted contrast and at higher contrasts, leading to greater efficiency of contrast coding at these levels. The estimated contrast discrimination threshold curve becomes elevated and shifted toward higher contrasts after adaptation, as has been found previously in human psychophysical experiments.
Benefits of Contrast Normalization Demonstrated in Neurons and Model Cells
Journal of Neuroscience, 2007
The large dynamic range of natural stimuli poses a challenge for neural coding: how is a neuron to encode large differences at high contrast while remaining sensitive to small differences at low contrast? Many sensory neurons exhibit contrast normalization: gain depends on the range of stimuli presented, such that firing-rate modulation is not proportional to contrast. However, coding depends strongly on the precision of spike timing and the reliability of spike number, neither of which can be predicted from neural gain. The presumption that contrast normalization is associated with maintained coding efficiency remained untested. We report that, as contrast decreases, responses are more variable and encode less information, as expected. Nevertheless, these changes can be small, and information transmission is even better preserved across contrasts than rate modulation. The extent of contrast normalization is correlated with the extent to which information transmission is preserved across contrasts. Specifically, normalization is associated with maintaining the bits of information per spike rather than bits per second. Finally, we show that a nonadapting model can exhibit both contrast normalization and the associated information preservation.
Contrast gain control in the visual cortex: monocular versus binocular mechanisms
The Journal of neuroscience : the official journal of the Society for Neuroscience, 2000
In this study, we compare binocular and monocular mechanisms underlying contrast encoding by binocular simple cells in primary visual cortex. At mid to high levels of stimulus contrast, contrast gain of cortical neurons typically decreases as stimulus contrast is increased (). We have devised a technique by which it is possible to determine the relative contributions of monocular and binocular processes to such reductions in contrast gain. First, we model the simple cell as an adjustable linear mechanism with a static output nonlinearity. For binocular cells, the linear mechanism is sensitive to inputs from both eyes. To constrain the parameters of the model, we record from binocular simple cells in striate cortex. To activate each cell, drifting sinusoidal gratings are presented dichoptically at various relative interocular phases. Stimulus contrast for one eye is varied over a large range whereas that for the other eye is fixed. We then determine the best-fitting parameters of the...