How Retinal Ganglion Cells Prevent Synaptic Noise From Reaching the Spike Output

Directional summation in non-direction selective retinal ganglion cells

Retinal ganglion cells receive inputs from multiple bipolar cells which must be integrated before a decision to fire is made. Theoretical studies have provided clues about how this integration is accomplished but have not directly determined the rules regulating summation of closely timed inputs along single or multiple dendrites. Here we have examined dendritic summation of multiple inputs along On ganglion cell dendrites in whole mount rat retina. We activated inputs at targeted locations by uncaging glutamate sequentially to generate apparent motion along On ganglion cell dendrites in whole mount retina. Summation was directional and dependent13 on input sequence. Input moving away from the soma (centrifugal) resulted in supralinear summation, while activation sequences moving toward the soma (centripetal) were linear. Enhanced summation for centrifugal activation was robust as it was also observed in cultured retinal ganglion cells. This directional summation was dependent on hyperpolarization activated cyclic nucleotide-gated (HCN) channels as blockade with ZD7288 eliminated directionality. A computational model confirms that activation of HCN channels can override a preference for centripetal summation expected from cell anatomy. This type of direction selectivity could play a role in coding movement similar to the axial selectivity seen in locust ganglion cells which detect looming stimuli. More generally, these results suggest that non-directional retinal ganglion cells can discriminate between input sequences independent of the retina network.

Visual information is coded by the output of retinal ganglion cells. Through evolution retinal ganglion cells acquired unique properties that allowed them to transmit to the brain such signals as direction of movement. The quest for the cellular mechanism of the detection of movement by retinal ganglion cells has been the holy grail of research on direction selectivity. In this study we have found a mechanism that allows individual non-direction selective On retinal ganglion cells to code sequences of excitatory inputs moving either away or toward the soma. We observed that inputs moving away from the soma resulted in enhanced, supralinear EPSP summation. Evidence from computational modeling suggests that expression of a specific set of voltage-dependent channels in dendrites introduces nonlinearities that could give a ganglion cell the ability to code looming motion. We predict that in a retinal network, such a directional tuning mechanism, together with asymmetric presynaptic inhibition, could be the building block for the development of more complex detection of visual motion.

NMDA receptor contributions to visual contrast coding

In the retina, it is not well understood how visual processing depends on AMPA- and NMDA-type glutamate receptors. Here we investigated how these receptors contribute to contrast coding in identified guinea pig ganglion cell types in vitro. NMDA-mediated responses were negligible in ON alpha cells but substantial in OFF alpha and delta cells. OFF delta cell NMDA receptors were composed of GluN2B subunits. Using a novel deconvolution method, we determined the individual contributions of AMPA, NMDA, and inhibitory currents to light responses of each cell type. OFF alpha and delta cells used NMDA receptors for encoding either the full contrast range (alpha), including near-threshold responses, or only a high range (delta). However, contrast sensitivity depended substantially on NMDA receptors only in OFF alpha cells. NMDA receptors contribute to visual contrast coding in a cell-type-specific manner. Certain cell types generate excitatory responses using primarily AMPA receptors or disinhibition.

Ideal observer analysis of signal quality in retinal circuits

The function of the retina is crucial, for it must encode visual signals so the brain can detect objects in the visual world. However, the biological mechanisms of the retina add noise to the visual signal and therefore reduce its quality and capacity to inform about the world. Because an organism's survival depends on its ability to unambiguously detect visual stimuli in the presence of noise, its retinal circuits must have evolved to maximize signal quality, suggesting that each retinal circuit has a specific functional role. Here we explain how an ideal observer can measure signal quality to determine the functional roles of retinal circuits. In a visual discrimination task the ideal observer can measure from a neural response the increment threshold, the number of distinguishable response levels, and the neural code, which are fundamental measures of signal quality relevant to behavior. It can compare the signal quality in stimulus and response to determine the optimal stimulus, and can measure the specific loss of signal quality by a neuron's receptive field for non-optimal stimuli. Taking into account noise correlations, the ideal observer can track the signal-to-noise ratio available from one stage to the next, allowing one to determine each stage's role in preserving signal quality. A comparison between the ideal performance of the photon flux absorbed from the stimulus and actual performance of a retinal ganglion cell shows that in daylight a ganglion cell and its presynaptic circuit loses a factor of approximately 10-fold in contrast sensitivity, suggesting specific signal-processing roles for synaptic connections and other neural circuit elements. The ideal observer is a powerful tool for characterizing signal processing in single neurons and arrays along a neural pathway.

The maintained discharge of rat retinal ganglion cells

Action potentials were recorded from rat retinal ganglion cell fibers in the presence of a uniform field, and the maintained discharge pattern was characterized. Spike trains recorded under ketaminexylazine. The majority of cells had multimodal interval distributions, with the first peak in the range of 25.00.97). Both ON and OFF cells show serial correlations between adjacent interspike intervals, while ON cells also showed second-order correlations. Cells with multimodal interval distribution showed a strong peak at high frequencies in the power spectra in the range of 28.9-41.4 Hz. Oscillations were present under both anesthetic conditions and persisted in the dark at a slightly lower frequency, implying that the oscillations are generated independent of any light stimulus but can be modulated by light level. The oscillation frequency varied slightly between cells of the same type and in the same eye, suggesting that multiple oscillatory generating mechanisms exist within the retina. Cells with high-frequency oscillations were described well by an integrate-and-fire model with the input consisting of Gaussian noise plus a sinusoid where the phase was jittered randomly to account for the bandwidth present in the oscillations.

Signals and noise in an inhibitory interneuron diverge to control activity in nearby retinal ganglion cells

Information about sensory stimuli is represented by spatiotemporal patterns of neural activity. The complexity of the central nervous system, however, frequently obscures the origin and properties of signals and noise that underlie these activity patterns. We minimized this constraint by examining mechanisms governing correlated activity in mouse retinal ganglion cells (RGCs) under conditions in which light-evoked responses traverse a specific circuit, the rod bipolar pathway. Signals and noise in this circuit produced correlated synaptic input to neighboring On and Off RGCs. Temporal modulation of light intensity did not alter the degree to which noise in the input to nearby RGCs was correlated, and action potential generation in individual RGCs was largely insensitive to differences in network noise generated by dynamic and static light stimuli. Together, these features enable noise in shared circuitry to diminish simultaneous action potential generation in neighboring On and Off RGCs under a variety of conditions.

Benefits of contrast normalization demonstrated in neurons and model cells

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 adaptation in a nonadapting LGN model

Sensory neurons appear to adapt their gain to match the variance of signals along the dimension they encode, a property we shall call "contrast normalization." Contrast normalization has been the subject of extensive physiological and theoretical study. We previously found that neurons in the lateral geniculate nucleus (LGN) exhibit contrast normalization in their responses to full-field flickering white-noise stimuli, and that neurons with the strongest contrast normalization best preserved information transmission across a range of contrasts. We have also shown that both of these properties could be reproduced by nonadapting model cells. Here we present a detailed comparison of this nonadapting model to physiological data from the LGN. First, the model cells recapitulated other contrast dependencies of LGN responses: decreasing stimulus contrast resulted in an increase in spike-timing jitter and spike-number variability. Second, we find that the extent of contrast normalization in this model depends on model parameters related to refractoriness and to noise. Third, we show that the model cells exhibit rapid, transient changes in firing rate just after changes in contrast, and that this is sufficient to produce the transient changes in information transmission that have been reported in other neurons. It is known that intrinsic properties of neurons change during contrast adaptation. Nevertheless the model demonstrates that the spiking nonlinearity of neurons can produce many of the temporal aspects of contrast gain control, including normalization to input variance and transient effects of contrast change.

A model biological neural network: the cephalopod vestibular system

Artificial neural networks (ANNs) have become increasingly sophisticated and are widely used for the extraction of patterns or meaning from complicated or imprecise datasets. At the same time, our knowledge of the biological systems that inspired these ANNs has also progressed and a range of model systems are emerging where there is detailed information not only on the architecture and components of the system but also on their ontogeny, plasticity and the adaptive characteristics of their interconnections. We describe here a biological neural network contained in the cephalopod statocysts; the statocysts are analogous to the vertebrae vestibular system and provide the animal with sensory information on its orientation and movements in space. The statocyst network comprises only a small number of cells, made up of just three classes of neurons but, in combination with the large efferent innervation from the brain, forms an 'active' sense organs that uses feedback and feed-forward mechanisms to alter and dynamically modulate the activity within cells and how the various components are interconnected. The neurons are fully accessible to physiological investigation and the system provides an excellent model for describing the mechanisms underlying the operation of a sophisticated neural network.

Microcircuitry for two types of achromatic ganglion cell in primate fovea

Synaptic circuits in primate fovea have been quantified for midget/parvocellular ganglion cells. Here, based on partial reconstructions from serial electron micrographs, we quantify synaptic circuits for two other types of ganglion cell: the familiar parasol/magnocellular cell and a smaller type, termed "garland." The excitatory circuits both derive from two types of OFF diffuse cone bipolar cell, DB3 and DB2, which collected unselectively from at least 6 +/- 1 cones, including the S type. Cone contacts to DB3 dendrites were usually located between neighboring triads, whereas half of the cone contacts to DB2 were triad associated. Ribbon outputs were as follows: DB3, 69 +/- 5; DB2, 48 +/- 4. A complete parasol cell (30 microm dendritic field diameter) would collect from approximately 50 cones via approximately 120 bipolar and approximately 85 amacrine contacts; a complete garland cell (25 microm dendritic field) would collect from approximately 40 cones via approximately 75 bipolar and approximately 145 amacrine contacts. The bipolar types contributed differently: the parasol cell received most contacts (60%) from DB3, whereas the garland cell received most contacts (67%) from DB2. We hypothesize that DB3 is a transient bipolar cell and that DB2 is sustained. This would be consistent with their relative inputs to the brisk-transient (parasol) ganglion cell. The garland cell, with its high proportion of DB2 inputs plus its high proportion of amacrine synapses (70%) and dense mosaic, might correspond to the local-edge cell in nonprimate retinas, which serves finer acuity at low temporal frequencies. The convergence of S cones onto both types could contribute S-cone input for cortical areas primary visual cortex and the middle temporal area.

Response variability of marmoset parvocellular neurons

This study concerns the properties of neurons carrying signals for colour vision in primates. We investigated the variability of responses of individual parvocellular lateral geniculate neurons of dichromatic and trichromatic marmosets to drifting sinusoidal luminance and chromatic gratings. Response variability was quantified by the cycle-to-cycle variation in Fourier components of the response. Averaged across the population, the variability at low contrasts was greater than predicted by a Poisson process, and at high contrasts the responses were approximately 40% more variable than responses at low contrasts. The contrast-dependent increase in variability was nevertheless below that expected from the increase in firing rate. Variability falls below the Poisson prediction at high contrast, and intrinsic variability of the spike train decreases as contrast increases. Thus, while deeply modulated responses in parvocellular cells have a larger absolute variability than weakly modulated ones, they have a more favourable signal: noise ratio than predicted by a Poisson process. Similar results were obtained from a small sample of magnocellular and koniocellular ('blue-on') neurons. For parvocellular neurons with pronounced colour opponency, chromatic responses were, on average, less variable (10-15%, p<0.01) than luminance responses of equal magnitude. Conversely, non-opponent parvocellular neurons showed the opposite tendency. This is consistent with a supra-additive noise source prior to combination of cone signals. In summary, though variability of parvocellular neurons is largely independent of the way in which they combine cone signals, the noise characteristics of retinal circuitry may augment specialization of parvocellular neurons to signal luminance or chromatic contrast.

Presynaptic Mechanism for Slow Contrast Adaptation in Mammalian Retinal Ganglion Cells

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 sodium-dependent 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.

Encoding light intensity by the cone photoreceptor synapse

How cone synapses encode light intensity determines the precision of information transmission at the first synapse on the visual pathway. Although it is known that cone photoreceptors hyperpolarize to light over 4-5 log units of intensity, the relationship between light intensity and transmitter release at the cone synapse has not been determined. Here, we use two-photon microscopy to visualize release of the synaptic vesicle dye FM1-43 from cone terminals in the intact lizard retina, in response to different stimulus light intensities. We then employ electron microscopy to translate these measurements into vesicle release rates. We find that from darkness to bright light, release decreases from 49 to approximately 2 vesicles per 200 ms; therefore, cones compress their 10,000-fold operating range for phototransduction into a 25-fold range for synaptic vesicle release. Tonic release encodes ten distinguishable intensity levels, skewed to most finely represent bright light, assuming release obeys Poisson statistics.

Do we know what the early visual system does?

We can claim that we know what the visual system does once we can predict neural responses to arbitrary stimuli, including those seen in nature. In the early visual system, models based on one or more linear receptive fields hold promise to achieve this goal as long as the models include nonlinear mechanisms that control responsiveness, based on stimulus context and history, and take into account the nonlinearity of spike generation. These linear and nonlinear mechanisms might be the only essential determinants of the response, or alternatively, there may be additional fundamental determinants yet to be identified. Research is progressing with the goals of defining a single "standard model" for each stage of the visual pathway and testing the predictive power of these models on the responses to movies of natural scenes. These predictive models represent, at a given stage of the visual pathway, a compact description of visual computation. They would be an invaluable guide for understanding the underlying biophysical and anatomical mechanisms and relating neural responses to visual perception.

High Response Reliability of Neurons in Primary Visual Cortex (V1) of Alert, Trained Monkeys

The reliability of neuronal responses determines the resources needed to represent the external world and constrains the nature of the neural code. Studies of anesthetized animals have indicated that neuronal responses become progressively more variable as information travels from the retina to the cortex. These results have been interpreted to indicate that perception must be based on pooling across relatively large numbers of cells. However, we find that in alert monkeys, responses in primary visual cortex (V1) are as reliable as the inputs from the retina and the thalamus. Moreover, when the effects of fixational eye movements were minimized, response variability (variance/mean - Fano factor, FF) in all V1 layers was low. When presenting optimal stimuli, the median FF was 0.3. High variability, FF approximately 1, was found only near threshold. Our results suggest that in natural vision, suprathreshold perception can be based on small numbers of optimally stimulated cells.

Contrast adaptation in subthreshold and spiking responses of mammalian Y-type retinal ganglion cells

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 effects could not be explained by an artifact of the intracellular recording conditions: extracellular recordings showed a gain change of 0.58 +/- 0.022. For intracellular recordings, negative current reduced the spike output but did not affect the gain change in the subthreshold response: 0.80 +/- 0.051. Thus, adaptation in the subthreshold response did not require spike-dependent conductances. We conclude that the contrast-dependent gain change in the spiking response can be explained by both a synaptic mechanism, as reflected by responses in the subthreshold potential, and an intrinsic mechanism in the ganglion cell related to spike generation.

Sluggish and brisk ganglion cells detect contrast with similar sensitivity

Roughly half of all ganglion cells in mammalian retina belong to the broad class, termed "sluggish." Many of these cells have small receptive fields and project via lateral geniculate nuclei to visual cortex. However, their possible contributions to perception have been largely ignored because sluggish cells seem to respond weakly compared with the more easily studied "brisk" cells. By selecting small somas under infrared DIC optics and recording with a loose seal, we could routinely isolate sluggish cells. When a spot was matched spatially and temporally to the receptive field center, most sluggish cells could detect the same low contrasts as brisk cells. Detection thresholds for the two groups determined by an "ideal observer" were similar: threshold contrast for sluggish cells was 4.7 +/- 0.5% (mean +/- SE), and for brisk cells was 3.4 +/- 0.3% (Mann-Whitney test: P > 0.05). Signal-to-noise ratios for the two classes were also similar at low contrast. However, sluggish cells saturated at somewhat lower contrasts (contrast for half-maximum response was 14 +/- 1 vs. 19 +/- 2% for brisk cells) and were less sensitive to higher temporal frequencies (when the stimulus frequency was increased from 2 to 4 Hz, the response rate fell by 1.6-fold). Thus the sluggish cells covered a narrower dynamic range and a narrower temporal bandwidth, consistent with their reported lower information rates. Because information per spike is greater at lower firing rates, sluggish cells may represent "cheaper" channels that convey less urgent visual information at a lower energy cost.