Temporal contrast adaptation in the input and output signals of salamander retinal ganglion cells

Adaptation to visual sparsity enhances responses to isolated stimuli

Sensory systems adapt their response properties to the statistics of their inputs. For instance, visual systems adapt to low-order statistics like mean and variance to encode stimuli efficiently or to facilitate specific downstream computations. However, it remains unclear how other statistical features affect sensory adaptation. Here, we explore how Drosophila's visual motion circuits adapt to stimulus sparsity, a measure of the signal's intermittency not captured by low-order statistics alone. Early visual neurons in both ON and OFF pathways alter their responses dramatically with stimulus sparsity, responding positively to both light and dark sparse stimuli but linearly to dense stimuli. These changes extend to downstream ON and OFF direction-selective neurons, which are activated by sparse stimuli of both polarities but respond with opposite signs to light and dark regions of dense stimuli. Thus, sparse stimuli activate both ON and OFF pathways, recruiting a larger fraction of the circuit and potentially enhancing the salience of isolated stimuli. Overall, our results reveal visual response properties that increase the fraction of the circuit responding to sparse, isolated stimuli.

Spike desensitisation as a mechanism for high-contrast selectivity in retinal ganglion cells

In the vertebrate retina, several dozens of parallel channels relay information about the visual world to the brain. These channels are represented by the different types of retinal ganglion cells (RGCs), whose responses are rendered selective for distinct sets of visual features by various mechanisms. These mechanisms can be roughly grouped into synaptic interactions and cell-intrinsic mechanisms, with the latter including dendritic morphology as well as ion channel complement and distribution. Here, we investigate how strongly ion channel complement can shape RGC output by comparing two mouse RGC types, the well-described ON alpha cell and a little-studied ON cell that is EGFP-labelled in the Igfbp5 mouse line and displays an unusual selectivity for stimuli with high contrast. Using patch-clamp recordings and computational modelling, we show that a higher activation threshold and a pronounced slow inactivation of the voltage-gated Na+ channels contribute to the distinct contrast tuning and transient responses in ON Igfbp5 RGCs, respectively. In contrast, such a mechanism could not be observed in ON alpha cells. This study provides an example for the powerful role that the last stage of retinal processing can play in shaping RGC responses.

Electrical synapses for a pooling layer of the convolutional neural network in retinas

We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry.

Two Sides of the Same Coin: Efficient and Predictive Neural Coding

Some visual properties are consistent across a wide range of environments, while other properties are more labile. The efficient coding hypothesis states that many of these regularities in the environment can be discarded from neural representations, thus allocating more of the brain's dynamic range to properties that are likely to vary. This paradigm is less clear about how the visual system prioritizes different pieces of information that vary across visual environments. One solution is to prioritize information that can be used to predict future events, particularly those that guide behavior. The relationship between the efficient coding and future prediction paradigms is an area of active investigation. In this review, we argue that these paradigms are complementary and often act on distinct components of the visual input. We also discuss how normative approaches to efficient coding and future prediction can be integrated.

Interpreting the retinal neural code for natural scenes: From computations to neurons

Understanding the circuit mechanisms of the visual code for natural scenes is a central goal of sensory neuroscience. We show that a three-layer network model predicts retinal natural scene responses with an accuracy nearing experimental limits. The model's internal structure is interpretable, as interneurons recorded separately and not modeled directly are highly correlated with model interneurons. Models fitted only to natural scenes reproduce a diverse set of phenomena related to motion encoding, adaptation, and predictive coding, establishing their ethological relevance to natural visual computation. A new approach decomposes the computations of model ganglion cells into the contributions of model interneurons, allowing automatic generation of new hypotheses for how interneurons with different spatiotemporal responses are combined to generate retinal computations, including predictive phenomena currently lacking an explanation. Our results demonstrate a unified and general approach to study the circuit mechanisms of ethological retinal computations under natural visual scenes.

Contrast normalization affects response time-course of visual interneurons

In natural environments, light intensities and visual contrasts vary widely, yet neurons have a limited response range for encoding them. Neurons accomplish that by flexibly adjusting their dynamic range to the statistics of the environment via contrast normalization. The effect of contrast normalization is usually measured as a reduction of neural signal amplitudes, but whether it influences response dynamics is unknown. Here, we show that contrast normalization in visual interneurons of Drosophila melanogaster not only suppresses the amplitude but also alters the dynamics of responses when a dynamic surround is present. We present a simple model that qualitatively reproduces the simultaneous effect of the visual surround on the response amplitude and temporal dynamics by altering the cells' input resistance and, thus, their membrane time constant. In conclusion, single-cell filtering properties as derived from artificial stimulus protocols like white-noise stimulation cannot be transferred one-to-one to predict responses under natural conditions.

Rapid adaptation of primate LGN neurons to drifting grating stimulation

The visual system needs to dynamically adapt to changing environments. Much is known about the adaptive effects of constant stimulation over prolonged periods. However, there are open questions regarding adaptation to stimuli that are changing over time, interrupted, or repeated. Feature-specific adaptation to repeating stimuli has been shown to occur as early as primary visual cortex (V1), but there is also evidence for more generalized, fatigue-like adaptation that might occur at an earlier stage of processing. Here, we show adaptation in the lateral geniculate nucleus (LGN) of awake, fixating monkeys following brief (1 s) exposure to repeated cycles of a 4-Hz drifting grating. We examined the relative change of each neuron's response across successive (repeated) grating cycles. We found that neurons from all cell classes (parvocellular, magnocellular, and koniocellular) showed significant adaptation. However, only magnocellular neurons showed adaptation when responses were averaged to a population response. In contrast to firing rates, response variability was largely unaffected. Finally, adaptation was comparable between monocular and binocular stimulation, suggesting that rapid LGN adaptation is monocular in nature.NEW & NOTEWORTHY Neural adaptation can be defined as reduction of spiking responses following repeated or prolonged stimulation. Adaptation helps adjust neural responsiveness to avoid saturation and has been suggested to improve perceptual selectivity, information transmission, and predictive coding. Here, we report rapid adaptation to repeated cycles of gratings drifting over the receptive field of neurons at the earliest site of postretinal processing, the lateral geniculate nucleus of the thalamus.

Multifaceted luminance gain control beyond photoreceptors in Drosophila

Animals navigating in natural environments must handle vast changes in their sensory input. Visual systems, for example, handle changes in luminance at many timescales, from slow changes across the day to rapid changes during active behavior. To maintain luminance-invariant perception, visual systems must adapt their sensitivity to changing luminance at different timescales. We demonstrate that luminance gain control in photoreceptors alone is insufficient to explain luminance invariance at both fast and slow timescales and reveal the algorithms that adjust gain past photoreceptors in the fly eye. We combined imaging and behavioral experiments with computational modeling to show that downstream of photoreceptors, circuitry taking input from the single luminance-sensitive neuron type L3 implements gain control at fast and slow timescales. This computation is bidirectional in that it prevents the underestimation of contrasts in low luminance and overestimation in high luminance. An algorithmic model disentangles these multifaceted contributions and shows that the bidirectional gain control occurs at both timescales. The model implements a nonlinear interaction of luminance and contrast to achieve gain correction at fast timescales and a dark-sensitive channel to improve the detection of dim stimuli at slow timescales. Together, our work demonstrates how a single neuronal channel performs diverse computations to implement gain control at multiple timescales that are together important for navigation in natural environments.

Calibrating Vision: Concepts and Questions

The idea that visual coding and perception are shaped by experience and adjust to changes in the environment or the observer is universally recognized as a cornerstone of visual processing, yet the functions and processes mediating these calibrations remain in many ways poorly understood. In this article we review a number of facets and issues surrounding the general notion of calibration, with a focus on plasticity within the encoding and representational stages of visual processing. These include how many types of calibrations there are - and how we decide; how plasticity for encoding is intertwined with other principles of sensory coding; how it is instantiated at the level of the dynamic networks mediating vision; how it varies with development or between individuals; and the factors that may limit the form or degree of the adjustments. Our goal is to give a small glimpse of an enormous and fundamental dimension of vision, and to point to some of the unresolved questions in our understanding of how and why ongoing calibrations are a pervasive and essential element of vision.

Visual Stimulation Induces Distinct Forms of Sensitization of On-Off Direction-Selective Ganglion Cell Responses in the Dorsal and Ventral Retina

Experience-dependent modulation of neuronal responses is a key attribute in sensory processing. In the mammalian retina, the On-Off direction-selective ganglion cell (DSGC) is well known for its robust direction selectivity. However, how the On-Off DSGC light responsiveness dynamically adjusts to the changing visual environment is underexplored. Here, we report that On-Off DSGCs tuned to posterior motion direction [i.e. posterior DSGCs (pDSGCs)] in mice of both sexes can be transiently sensitized by prior stimuli. Notably, distinct sensitization patterns are found in dorsal and ventral pDSGCs. Although responses of both dorsal and ventral pDSGCs to dark stimuli (Off responses) are sensitized, only dorsal cells show the sensitization of responses to bright stimuli (On responses). Visual stimulation to the dorsal retina potentiates a sustained excitatory input from Off bipolar cells, leading to tonic depolarization of pDSGCs. Such tonic depolarization propagates from the Off to the On dendritic arbor of the pDSGC to sensitize its On response. We also identified a previously overlooked feature of DSGC dendritic architecture that can support dendritic integration between On and Off dendritic layers bypassing the soma. By contrast, ventral pDSGCs lack a sensitized tonic depolarization and thus do not exhibit sensitization of their On responses. Our results highlight a topographic difference in Off bipolar cell inputs underlying divergent sensitization patterns of dorsal and ventral pDSGCs. Moreover, substantial crossovers between dendritic layers of On-Off DSGCs suggest an interactive dendritic algorithm for processing On and Off signals before they reach the soma.SIGNIFICANCE STATEMENT Visual neuronal responses are dynamically influenced by the prior visual experience. This form of plasticity reflects the efficient coding of the naturalistic environment by the visual system. We found that a class of retinal output neurons, On-Off direction-selective ganglion cells, transiently increase their responsiveness after visual stimulation. Cells located in dorsal and ventral retinas exhibit distinct sensitization patterns because of different adaptive properties of Off bipolar cell signaling. A previously overlooked dendritic morphologic feature of the On-Off direction-selective ganglion cell is implicated in the cross talk between On and Off pathways during sensitization. Together, these findings uncover a topographic difference in the adaptive encoding of upper and lower visual fields and the underlying neural mechanism in the dorsal and ventral retinas.

Inhibition, but not excitation, recovers from partial cone loss with greater spatiotemporal integration, synapse density, and frequency

Neural circuits function in the face of changing inputs, either caused by normal variation in stimuli or by cell death. To maintain their ability to perform essential computations with partial inputs, neural circuits make modifications. Here, we study the retinal circuit’s responses to changes in light stimuli or in photoreceptor inputs by inducing partial cone death in the mature mouse retina. Can the retina withstand or recover from input loss? We find that the excitatory pathways exhibit functional loss commensurate with cone death and with some aspects predicted by partial light stimulation. However, inhibitory pathways recover functionally from lost input by increasing spatiotemporal integration in a way that is not recapitulated by partially stimulating the control retina. Anatomically, inhibitory synapses are upregulated on secondary bipolar cells and output ganglion cells. These findings demonstrate the greater capacity for inhibition, compared with excitation, to modify spatiotemporal processing with fewer cone inputs.

Lee et al. find partial cone loss triggers inhibition, but not excitation, to increase spatiotemporal integration, recover contrast gain, and increase synaptic release onto retinal ganglion cells. Natural images filtered by cone-loss receptive fields perceptually match those of controls. Thus, inhibition compensates for fewer cones to potentially preserve perception.

Enhancing the dark side: asymmetric gain of cone photoreceptors underpins their discrimination of visual scenes based on skewness

Psychophysical data indicate that humans can discriminate visual scenes based on their skewness, i.e. the ratio of dark and bright patches within a visual scene. It has also been shown that at a phenomenological level this skew discrimination is described by the so-called blackshot mechanism, which accentuates strong negative contrasts within a scene. Here, we present a set of observations suggesting that the underlying computation might start as early as the cone phototransduction cascade, whose gain is higher for strong negative contrasts than for strong positive contrasts. We recorded from goldfish cone photoreceptors and found that the asymmetry in the phototransduction gain leads to responses with larger amplitudes when using negatively rather than positively skewed light stimuli. This asymmetry in amplitude was present in the cone photocurrent, voltage response and synaptic output. Given that the properties of the phototransduction cascade are universal across vertebrates, it is possible that the mechanism shown here gives rise to a general ability to discriminate between scenes based only on their skewness, which psychophysical studies have shown humans can do. Thus, our data suggest the importance of non-linearity of the early photoreceptor for perception. Additionally, we found that stimulus skewness leads to a subtle change in photoreceptor kinetics. For negatively skewed stimuli, the impulse response functions of the cone peak later than for positively skewed stimuli. However, stimulus skewness does not affect the overall integration time of the cone. KEY POINTS: Humans can discriminate visual scenes based on skewness, i.e. the relative prevalence of bright and dark patches within a scene. Here, we show that negatively skewed time-series stimuli induce larger responses in goldfish cone photoreceptors than comparable positively skewed stimuli. This response asymmetry originates from within the phototransduction cascade, where gain is higher for strong negative contrasts (dark patches) than for strong positive contrasts (bright patches). Unlike the implicit assumption often contained within models of downstream visual neurons, our data show that cone photoreceptors do not simply relay linearly filtered versions of visual stimuli to downstream circuitry, but that they also emphasize specific stimulus features. Given that the phototransduction cascade properties among vertebrate retinas are mostly universal, our data imply that the skew discrimination by human subjects reported in psychophysical studies might stem from the asymmetric gain function of the phototransduction cascade.

Activity-dependent alteration of early myelin ensheathment in a developing sensory circuit

Myelination allows for the regulation of conduction velocity, affecting the precise timing of neuronal inputs important for the development and function of brain circuits. In turn, myelination may be altered by changes in experience, neuronal activity, and vesicular release, but the links between sensory experience, corresponding neuronal activity, and resulting alterations in myelination require further investigation. We thus studied the development of myelination in the Xenopus laevis tadpole, a classic model for studies of visual system development and function because it is translucent and visually responsive throughout the formation of its retinotectal system. We begin with a systematic characterization of the timecourse of early myelin ensheathment in the Xenopus retinotectal system using immunohistochemistry of myelin basic protein (MBP) along with third harmonic generation (THG) microscopy, a label-free structural imaging technique. Based on the mid-larval developmental progression of MBP expression in Xenopus, we identified an appropriate developmental window in which to assess the effects of early temporally patterned visual experience on myelin ensheathment. We used calcium imaging of axon terminals in vivo to characterize the responses of retinal ganglion cells over a range of stroboscopic stimulation frequencies. Strobe frequencies that reliably elicited robust versus dampened calcium responses were then presented to animals for 7 d, and differences in the amount of early myelin ensheathment at the optic chiasm were subsequently quantified. This study provides evidence that it is not just the presence but also to the specific temporal properties of sensory stimuli that are important for myelin plasticity.

A precise and adaptive neural mechanism for predictive temporal processing in the frontal cortex

The theory of predictive processing posits that the brain computes expectations to process information predictively. Empirical evidence in support of this theory, however, is scarce and largely limited to sensory areas. Here, we report a precise and adaptive mechanism in the frontal cortex of non-human primates consistent with predictive processing of temporal events. We found that the speed of neural dynamics is precisely adjusted according to the average time of an expected stimulus. This speed adjustment, in turn, enables neurons to encode stimuli in terms of deviations from expectation. This lawful relationship was evident across multiple experiments and held true during learning: when temporal statistics underwent covert changes, neural responses underwent predictable changes that reflected the new mean. Together, these results highlight a precise mathematical relationship between temporal statistics in the environment and neural activity in the frontal cortex that may serve as a mechanism for predictive temporal processing.

Mature Retina Compensates Functionally for Partial Loss of Rod Photoreceptors

Loss of primary neuronal inputs inevitably strikes every neural circuit. The deafferented circuit could propagate, amplify, or mitigate input loss, thus affecting the circuit’s output. How the deafferented circuit contributes to the effect on the output is poorly understood because of lack of control over loss of and access to circuit elements. Here, we control the timing and degree of rod photoreceptor ablation in mature mouse retina and uncover compensation. Following loss of half of the rods, rod bipolar cells mitigate the loss by preserving voltage output. Such mitigation allows partial recovery of ganglion cell responses. We conclude that rod death is compensated for in the circuit because ganglion cell responses to stimulation of half of the rods in an unperturbed circuit are weaker than responses after death of half of the rods. The dominant mechanism of such compensation includes homeostatic regulation of inhibition to balance the loss of excitation.

Care et al. ablate half of the rods in mature mouse retina and find that primary neuron loss is functionally compensated for by balanced inhibition and excitation at the secondary neuron. Changes in cone-mediated, but not rod-mediated, output neuron spikes are recapitulated by half stimulation, demonstrating independent regulation of pathways.

GABAA presynaptic inhibition regulates the gain and kinetics of retinal output neurons

Output signals of neural circuits, including the retina, are shaped by a combination of excitatory and inhibitory signals. Inhibitory signals can act presynaptically on axon terminals to control neurotransmitter release and regulate circuit function. However, it has been difficult to study the role of presynaptic inhibition in most neural circuits due to lack of cell type-specific and receptor type-specific perturbations. In this study, we used a transgenic approach to selectively eliminate GABA_A inhibitory receptors from select types of second-order neurons - bipolar cells - in mouse retina and examined how this affects the light response properties of the well-characterized ON alpha ganglion cell retinal circuit. Selective loss of GABA_A receptor-mediated presynaptic inhibition causes an enhanced sensitivity and slower kinetics of light-evoked responses from ON alpha ganglion cells thus highlighting the role of presynaptic inhibition in gain control and temporal filtering of sensory signals in a key neural circuit in the mammalian retina.

High Contrast Allows the Retina to Compute More Than Just Contrast

The goal of sensory processing is to represent the environment of an animal. All sensory systems share a similar constraint: they need to encode a wide range of stimulus magnitudes within their narrow neuronal response range. The most efficient way, exploited by even the simplest nervous systems, is to encode relative changes in stimulus magnitude rather than the absolute magnitudes. For instance, the retina encodes contrast, which are the variations of light intensity occurring in time and in space. From this perspective, it is easy to understand why the bright plumage of a moving bird gains a lot of attention, while an octopus remains motionless and mimics its surroundings for concealment. Stronger contrasts simply cause stronger visual signals. However, the gains in retinal performance associated with higher contrast are far more than what can be attributed to just a trivial linear increase in signal strength. Here we discuss how this improvement in performance is reflected throughout different parts of the neural circuitry, within its neural code and how high contrast activates many non-linear mechanisms to unlock several sophisticated retinal computations that are virtually impossible in low contrast conditions.

Efficient measurements for the dynamic range of human lightness perception

PURPOSE

Patients with an eye disease often report nyctalopia, hemianopia, and/or photophobia. We hypothesized that such symptoms are related to the disease impacting the dynamic range of lightness perception (DRL). However, there is currently no standardized approach for measuring DRL for clinical use. We developed an efficient measurement method to estimate DRL.

STUDY DESIGN

Clinical trial METHODS: Fifty-five photophobic patients with eye disease and 46 controls participated. Each participant judged the appearance of visual stimuli, a thick bar with luminance that gradually changed from maximum to minimum was displayed on uniform background. On different trials the background luminance changed pseudo-randomly between three levels. The participants repeatedly tapped a border on the bar that divided the appearance of grayish white/black and perfect white/black. We defined the DRL as the ratio between the luminance values at the tapped point of the border between gray and white/black.

RESULTS

The mean DRL of the patients was approximately 15 dB, significantly smaller than that of the controls (20 dB). The center of each patient's DRL shift depending on background luminance, which we named index of contextual susceptibility (iCS), was significantly larger than controls. The DRL of retinitis pigmentosa was smaller than controls for every luminance condition. Only the iCS of glaucoma was significantly larger than controls.

CONCLUSIONS

This measurement technique detects an abnormality of the DRL. The results support our hypothesis that the DRL abnormality characterizes lightness-relevant symptoms that may elucidate the causes of nyctalopia, hemeralopia, and photophobia.

Plexus-specific effect of flicker-light stimulation on the retinal microvasculature assessed with optical coherence tomography angiography

In neural tissues, the coupling between neural activity and blood flow is a physiological key principle in blood flow regulation. We used optical coherence tomography angiography to investigate stimulus-evoked hemodynamic responses in different microvascular layers of the human retina. Twenty-two healthy subjects were included. Vessel density before and during light stimulation was measured using optical coherence tomography angiography and assessed for the superficial, intermediate, and deep capillary plexus of the retinal circulation. Volumetric blood flow was measured using a custom-built Doppler optical coherence tomography system. Our results show that flicker stimulation induced a significant increase in the vessel density of +9.9 ± 6.7% in the superficial capillary plexus, +6.6 ± 1.7% in the intermediate capillary plexus, and +4.9 ± 2.3% in the deep capillary plexus. The hyperemic response of the superficial capillary plexus was significantly higher compared to the intermediate capillary plexus (P = 0.02) and deep capillary plexus (P = 0.002). Volumetric retinal blood flow increased by +39.9 ± 34.9% in arteries and by +29.8 ± 16.8% in veins. In conclusion, we showed a strong increase in the retinal microvascular density in response to light stimulation, with the most pronounced effect in the superficial capillary plexus. This is compatible with the hypothesis that the microvasculature exerts an important function in mediating functional hyperemia in humans.NEW & NOTEWORTHY We present vessel density alterations in response to flicker stimulation using optical coherence tomography angiography and identified the superficial capillary plexus as the layer with the most pronounced effect. This points out the physiological importance of the microvasculature in mediating functional hyperemia and suggests a fine-tuned plexus-specific mechanism to meet cellular metabolic demands.

Receptoral Mechanisms for Fast Cholinergic Transmission in Direction-Selective Retinal Circuitry

Direction selectivity represents an elementary sensory computation that can be related to underlying synaptic mechanisms. In mammalian retina, direction-selective ganglion cells (DSGCs) respond strongly to visual motion in a "preferred" direction and weakly to motion in the opposite, "null" direction. The DS mechanism depends on starburst amacrine cells (SACs), which provide null direction-tuned GABAergic inhibition and untuned cholinergic excitation to DSGCs. GABAergic inhibition depends on conventional synaptic transmission, whereas cholinergic excitation apparently depends on paracrine (i.e., non-synaptic) transmission. Despite its paracrine mode of transmission, cholinergic excitation is more transient than GABAergic inhibition, yielding a temporal difference that contributes essentially to the DS computation. To isolate synaptic mechanisms that generate the distinct temporal properties of cholinergic and GABAergic transmission from SACs to DSGCs, we optogenetically stimulated SACs while recording postsynaptic currents (PSCs) from DSGCs in mouse retina. Direct recordings from channelrhodopsin-2-expressing (ChR2+) SACs during quasi-white noise (WN) (0-30 Hz) photostimulation demonstrated precise, graded optogenetic control of SAC membrane current and potential. Linear systems analysis of ChR2-evoked PSCs recorded in DSGCs revealed cholinergic transmission to be faster than GABAergic transmission. A deconvolution-based analysis showed that distinct postsynaptic receptor kinetics fully account for the temporal difference between cholinergic and GABAergic transmission. Furthermore, GABAA receptor blockade prolonged cholinergic transmission, identifying a new functional role for GABAergic inhibition of SACs. Thus, fast cholinergic transmission from SACs to DSGCs arises from at least two distinct mechanisms, yielding temporal properties consistent with conventional synapses despite its paracrine nature.

Functional-pathway-dominant contrast adaptation and sensitization in mouse retinal ganglion cells

Retinal ganglion cells (RGCs) reduce their light sensitivity during persistent high-contrast stimulation to prevent saturation to strong inputs and improve coding efficiency. This process is known as contrast adaptation. However, contrast adaptation also reduces RGCs' light response to weak inputs. On the other hand, some RGCs undergo contrast sensitization, and these RGCs respond to weak inputs following high contrast stimulation. In the present study, multi-electrode recordings were conducted on isolated mouse retinas under full-field visual stimulation with different contrast levels. Adaptation and sensitization were mainly observed in OFF and ON pathways, respectively. The results of linear-nonlinear analysis and stimulus reconstruction revealed that both the light sensitivity and encoded information were changed in opposite directions in adaptation and sensitization processes. Our work suggests that contrast adaptation and sensitization are two opposite dynamic processes. In mouse retina, OFF RGCs utilize adaptation to increase the discrimination of strong OFF inputs. On the other hand, ON RGCs use sensitization to increase the sensitivity to weak ON inputs. This functional differentiation might be meaningful for the mouse's survival as it lives in environments in which strong OFF stimuli often indicate potential predators while weak ON stimuli are usually related to movement and might be important for predation.

Computational challenges and opportunities for a bi-directional artificial retina

A future artificial retina that can restore high acuity vision in blind people will rely on the capability to both read (observe) and write (control) the spiking activity of neurons using an adaptive, bi-directional and high-resolution device. Although current research is focused on overcoming the technical challenges of building and implanting such a device, exploiting its capabilities to achieve more acute visual perception will also require substantial computational advances. Using high-density large-scale recording and stimulation in the primate retina with an ex vivo multi-electrode array lab prototype, we frame several of the major computational problems, and describe current progress and future opportunities in solving them. First, we identify cell types and locations from spontaneous activity in the blind retina, and then efficiently estimate their visual response properties by using a low-dimensional manifold of inter-retina variability learned from a large experimental dataset. Second, we estimate retinal responses to a large collection of relevant electrical stimuli by passing current patterns through an electrode array, spike sorting the resulting recordings and using the results to develop a model of evoked responses. Third, we reproduce the desired responses for a given visual target by temporally dithering a diverse collection of electrical stimuli within the integration time of the visual system. Together, these novel approaches may substantially enhance artificial vision in a next-generation device.

What the salamander eye has been telling the vision scientist's brain

Salamanders have been habitual residents of research laboratories for more than a century, and their history in science is tightly interwoven with vision research. Nevertheless, many vision scientists - even those working with salamanders - may be unaware of how much our knowledge about vision, and particularly the retina, has been shaped by studying salamanders. In this review, we take a tour through the salamander history in vision science, highlighting the main contributions of salamanders to our understanding of the vertebrate retina. We further point out specificities of the salamander visual system and discuss the perspectives of this animal system for future vision research.

A minimal synaptic model for direction selective neurons in Drosophila

Visual motion estimation is a canonical neural computation. In Drosophila, recent advances have identified anatomic and functional circuitry underlying direction-selective computations. Models with varying levels of abstraction have been proposed to explain specific experimental results but have rarely been compared across experiments. Here we use the wealth of available anatomical and physiological data to construct a minimal, biophysically inspired synaptic model for Drosophila’s first-order direction-selective T4 cells. We show how this model relates mathematically to classical models of motion detection, including the Hassenstein-Reichardt correlator model. We used numerical simulation to test how well this synaptic model could reproduce measurements of T4 cells across many datasets and stimulus modalities. These comparisons include responses to sinusoid gratings, to apparent motion stimuli, to stochastic stimuli, and to natural scenes. Without fine-tuning this model, it sufficed to reproduce many, but not all, response properties of T4 cells. Since this model is flexible and based on straightforward biophysical properties, it provides an extensible framework for developing a mechanistic understanding of T4 neural response properties. Moreover, it can be used to assess the sufficiency of simple biophysical mechanisms to describe features of the direction-selective computation and identify where our understanding must be improved.

Optimized Signal Flow through Photoreceptors Supports the High-Acuity Vision of Primates

The fovea is a neural specialization that endows humans and other primates with the sharpest vision among mammals. This performance originates in the foveal cones, which are extremely narrow and long to form a high-resolution pixel array. Puzzlingly, this form is predicted to impede electrical conduction to an extent that appears incompatible with vision. We observe the opposite: signal flow through even the longest cones (0.4-mm axons) is essentially lossless. Unlike in most neurons, amplification and impulse generation by voltage-gated channels are dispensable. Rather, sparse channel activity preserves intracellular current, which flows as if unobstructed by organelles. Despite these optimizations, signaling would degrade if cones were lengthier. Because cellular packing requires that cone elongation accompanies foveal expansion, this degradation helps explain why the fovea is a constant, miniscule size despite multiplicative changes in eye size through evolution. These observations reveal how biophysical mechanisms tailor form-function relationships for primate behavioral performance.

Accommodation responses following contrast adaptation

The current study explored the effects of contrast adaptation on the accommodation response (AR), using low- and high-pass filtered video clips as stimuli. Ten young myopic (mean ± standard deviation: -2.91 ± 1.36D) and 10 near emmetropic subjects (-0.19 ± 0.14D) participated in the study. The AR was monitored under monocular viewing conditions using an eccentric infrared photorefractor. A 2-stage procedure was used: (1) the minimum spatial frequency content necessary to produce a proper individual AR; and (2) the AR was compared before and after adaptation to low-pass (s = -0.5), control (s = 0) and high-pass (s = +0.5) filtered videos. We found that (1) the average threshold Sinc-blur of both myopes and emmetropes necessary to evoke accommodation was (mean ± standard deviation) λ = 7.40 ± 4.05 cpd. Myopes required a higher Sinc blur (average, 10.00 ± 4.05 cpd) compared to emmetropes (average, 4.80 ± 1.60 cpd). (2) Adaptation to low-pass filtered videos increased the AR by 0.41 ± 0.33D in the myopic group and reduced it in the emmetropic group by 0.31 ± 0.25D. Adaptation to high pass-filtered videos induced similar changes in both refractive groups (an increase of 0.41 ± 0.40D and 0.46 ± 0.29D for myopes and emmetropes, respectively). Our measurements show that the human AR can be modified by spatial frequency selective contrast adaptation although these were short-term effects. The perhaps most striking finding was that adaptation to low pass filtered videos had opposite effects on the AR in emmetropes and myopes. It remains to be studied whether these differences were a consequence of myopia or a contributing factor in myopia development.

Sparse identification of contrast gain control in the fruit fly photoreceptor and amacrine cell layer

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples.

Heterogeneous Temporal Contrast Adaptation in Drosophila Direction-Selective Circuits

In visual systems, neurons adapt both to the mean light level and to the range of light levels, or the contrast. Contrast adaptation has been studied extensively, but it remains unclear how it is distributed among neurons in connected circuits, and how early adaptation affects subsequent computations. Here, we investigated temporal contrast adaptation in neurons across Drosophila's visual motion circuitry. Several ON-pathway neurons showed strong adaptation to changes in contrast over time. One of these neurons, Mi1, showed almost complete adaptation on fast timescales, and experiments ruled out several potential mechanisms for its adaptive properties. When contrast adaptation reduced the gain in ON-pathway cells, it was accompanied by decreased motion responses in downstream direction-selective cells. Simulations show that contrast adaptation can substantially improve motion estimates in natural scenes. The benefits are larger for ON-pathway adaptation, which helps explain the heterogeneous distribution of contrast adaptation in these circuits.

Neural sensitization improves encoding fidelity in the primate retina

An animal’s motion through the environment can induce large and frequent fluctuations in light intensity on the retina. These fluctuations pose a major challenge to neural circuits tasked with encoding visual information, as they can cause cells to adapt and lose sensitivity. Here, we report that sensitization, a short-term plasticity mechanism, solves this difficult computational problem by maintaining neuronal sensitivity in the face of these fluctuations. The numerically dominant output pathway in the macaque monkey retina, the midget (parvocellular-projecting) pathway, undergoes sensitization under specific conditions, including simulated eye movements. Sensitization is present in the excitatory synaptic inputs from midget bipolar cells and is mediated by presynaptic disinhibition from a wide-field mechanism extending >0.5 mm along the retinal surface. Direct physiological recordings and a computational model indicate that sensitization in the midget pathway supports accurate sensory encoding and prevents a loss of responsiveness during dynamic visual processing.

Light intensity on the retina can fluctuate rapidly during natural vision, posing a challenge for encoding visual information. Here, the authors report that mechanisms of sensitization/facilitation maintain the sensitivity of the numerically dominant neural pathway in the primate retina during dynamic vision.

Coding Principles in Adaptation

Adaptation is a common principle that recurs throughout the nervous system at all stages of processing. This principle manifests in a variety of phenomena, from spike frequency adaptation, to apparent changes in receptive fields with changes in stimulus statistics, to enhanced responses to unexpected stimuli. The ubiquity of adaptation leads naturally to the question: What purpose do these different types of adaptation serve? A diverse set of theories, often highly overlapping, has been proposed to explain the functional role of adaptive phenomena. In this review, we discuss several of these theoretical frameworks, highlighting relationships among them and clarifying distinctions. We summarize observations of the varied manifestations of adaptation, particularly as they relate to these theoretical frameworks, focusing throughout on the visual system and making connections to other sensory systems.

Functional characterization of retinal ganglion cells using tailored nonlinear modeling

The mammalian retina encodes the visual world in action potentials generated by 20-50 functionally and anatomically-distinct types of retinal ganglion cell (RGC). Individual RGC types receive synaptic input from distinct presynaptic circuits; therefore, their responsiveness to specific features in the visual scene arises from the information encoded in synaptic input and shaped by postsynaptic signal integration and spike generation. Unfortunately, there is a dearth of tools for characterizing the computations reflected in RGC spike output. Therefore, we developed a statistical model, the separable Nonlinear Input Model, to characterize the excitatory and suppressive components of RGC receptive fields. We recorded RGC responses to a correlated noise ("cloud") stimulus in an in vitro preparation of mouse retina and found that our model accurately predicted RGC responses at high spatiotemporal resolution. It identified multiple receptive fields reflecting the main excitatory and suppressive components of the response of each neuron. Significantly, our model accurately identified ON-OFF cells and distinguished their distinct ON and OFF receptive fields, and it demonstrated a diversity of suppressive receptive fields in the RGC population. In total, our method offers a rich description of RGC computation and sets a foundation for relating it to retinal circuitry.

Variance adaptation in navigational decision making

Sensory systems relay information about the world to the brain, which enacts behaviors through motor outputs. To maximize information transmission, sensory systems discard redundant information through adaptation to the mean and variance of the environment. The behavioral consequences of sensory adaptation to environmental variance have been largely unexplored. Here, we study how larval fruit flies adapt sensory-motor computations underlying navigation to changes in the variance of visual and olfactory inputs. We show that variance adaptation can be characterized by rescaling of the sensory input and that for both visual and olfactory inputs, the temporal dynamics of adaptation are consistent with optimal variance estimation. In multisensory contexts, larvae adapt independently to variance in each sense, and portions of the navigational pathway encoding mixed odor and light signals are also capable of variance adaptation. Our results suggest multiplication as a mechanism for odor-light integration.

Adaptive feature detection from differential processing in parallel retinal pathways

To transmit information efficiently in a changing environment, the retina adapts to visual contrast by adjusting its gain, latency and mean response. Additionally, the temporal frequency selectivity, or bandwidth changes to encode the absolute intensity when the stimulus environment is noisy, and intensity differences when noise is low. We show that the On pathway of On-Off retinal amacrine and ganglion cells is required to change temporal bandwidth but not other adaptive properties. This remarkably specific adaptive mechanism arises from differential effects of contrast on the On and Off pathways. We analyzed a biophysical model fit only to a cell’s membrane potential, and verified pharmacologically that it accurately revealed the two pathways. We conclude that changes in bandwidth arise mostly from differences in synaptic threshold in the two pathways, rather than synaptic release dynamics as has previously been proposed to underlie contrast adaptation. Different efficient codes are selected by different thresholds in two independently adapting neural pathways.

Contrast gain control in mouse auditory cortex

The neocortex is thought to employ a number of canonical computations, but little is known about whether these computations rely on shared mechanisms across different neural populations. In recent years, the mouse has emerged as a powerful model organism for the dissection of the circuits and mechanisms underlying various aspects of neural processing and therefore provides an important avenue for research into putative canonical computations. One such computation is contrast gain control, the systematic adjustment of neural gain in accordance with the contrast of sensory input, which helps to construct neural representations that are robust to the presence of background stimuli. Here, we characterized contrast gain control in the mouse auditory cortex. We performed laminar extracellular recordings in the auditory cortex of the anesthetized mouse while varying the contrast of the sensory input. We observed that an increase in stimulus contrast resulted in a compensatory reduction in the gain of neural responses, leading to representations in the mouse auditory cortex that are largely contrast invariant. Contrast gain control was present in all cortical layers but was found to be strongest in deep layers, indicating that intracortical mechanisms may contribute to these gain changes. These results lay a foundation for investigations into the mechanisms underlying contrast adaptation in the mouse auditory cortex. NEW & NOTEWORTHY We investigated whether contrast gain control, the systematic reduction in neural gain in response to an increase in sensory contrast, exists in the mouse auditory cortex. We performed extracellular recordings in the mouse auditory cortex while presenting sensory stimuli with varying contrasts and found this form of processing was widespread. This finding provides evidence that contrast gain control may represent a canonical cortical computation and lays a foundation for investigations into the underlying mechanisms.

A single retinal circuit model for multiple computations

Vision is dependent on extracting intricate features of the visual information from the outside world, and complex visual computations begin to take place as soon as at the retinal level. In multiple studies on salamander retinas, the responses of a subtype of retinal ganglion cells, i.e., fast/biphasic-OFF ganglion cells, have been shown to be able to realize multiple functions, such as the segregation of a moving object from its background, motion anticipation, and rapid encoding of the spatial features of a new visual scene. For each of these visual functions, modeling approaches using extended linear-nonlinear cascade models suggest specific preceding retinal circuitries merging onto fast/biphasic-OFF ganglion cells. However, whether multiple visual functions can be accommodated together in a certain retinal circuitry and how specific mechanisms for each visual function interact with each other have not been investigated. Here, we propose a physiologically consistent, detailed computational model of the retinal circuit based on the spatiotemporal dynamics and connections of each class of retinal neurons to implement object motion sensitivity, motion anticipation, and rapid coding in the same circuit. Simulations suggest that multiple computations can be accommodated together, thereby implying that the fast/biphasic-OFF ganglion cell has potential to output a train of spikes carrying multiple pieces of information on distinct features of the visual stimuli.

Inferring hidden structure in multilayered neural circuits

A central challenge in sensory neuroscience involves understanding how neural circuits shape computations across cascaded cell layers. Here we attempt to reconstruct the response properties of experimentally unobserved neurons in the interior of a multilayered neural circuit, using cascaded linear-nonlinear (LN-LN) models. We combine non-smooth regularization with proximal consensus algorithms to overcome difficulties in fitting such models that arise from the high dimensionality of their parameter space. We apply this framework to retinal ganglion cell processing, learning LN-LN models of retinal circuitry consisting of thousands of parameters, using 40 minutes of responses to white noise. Our models demonstrate a 53% improvement in predicting ganglion cell spikes over classical linear-nonlinear (LN) models. Internal nonlinear subunits of the model match properties of retinal bipolar cells in both receptive field structure and number. Subunits have consistently high thresholds, supressing all but a small fraction of inputs, leading to sparse activity patterns in which only one subunit drives ganglion cell spiking at any time. From the model’s parameters, we predict that the removal of visual redundancies through stimulus decorrelation across space, a central tenet of efficient coding theory, originates primarily from bipolar cell synapses. Furthermore, the composite nonlinear computation performed by retinal circuitry corresponds to a boolean OR function applied to bipolar cell feature detectors. Our methods are statistically and computationally efficient, enabling us to rapidly learn hierarchical non-linear models as well as efficiently compute widely used descriptive statistics such as the spike triggered average (STA) and covariance (STC) for high dimensional stimuli. This general computational framework may aid in extracting principles of nonlinear hierarchical sensory processing across diverse modalities from limited data.

Computation in neural circuits arises from the cascaded processing of inputs through multiple cell layers. Each of these cell layers performs operations such as filtering and thresholding in order to shape a circuit’s output. It remains a challenge to describe both the computations and the mechanisms that mediate them given limited data recorded from a neural circuit. A standard approach to describing circuit computation involves building quantitative encoding models that predict the circuit response given its input, but these often fail to map in an interpretable way onto mechanisms within the circuit. In this work, we build two layer linear-nonlinear cascade models (LN-LN) in order to describe how the retinal output is shaped by nonlinear mechanisms in the inner retina. We find that these LN-LN models, fit to ganglion cell recordings alone, identify filters and nonlinearities that are readily mapped onto individual circuit components inside the retina, namely bipolar cells and the bipolar-to-ganglion cell synaptic threshold. This work demonstrates how combining simple prior knowledge of circuit properties with partial experimental recordings of a neural circuit’s output can yield interpretable models of the entire circuit computation, including parts of the circuit that are hidden or not directly observed in neural recordings.

Brightening and Dimming Aftereffects at Low and High Luminance

Adaptation to a spatially uniform field that increases or decreases in luminance over time yields a “ramp aftereffect”, whereby a steady, uniform luminance appears to dim or brighten, and an appropriate non-uniform test field appears to move. We measured the duration of this aftereffect of adaptation to ascending and descending luminance for a wide range of temporal frequencies and luminance amplitudes. Three types of luminance ramp profiles were used: linear, logarithmic, and exponential. The duration of the motion aftereffect increased as amplitude increased, regardless of the frequency, slope, or ramp profile of the adapting pattern. At low luminance, this result held for ascending luminance adaptation, but the duration of the aftereffect was significantly reduced for descending luminance adaptation. This reduction in the duration of the aftereffect at low luminance is consistent with differential recruitment of temporally tuned cells of the ON and OFF pathways, but the relative independence of the effect from temporal frequency is not.

Parallel Computations in Insect and Mammalian Visual Motion Processing

Sensory systems use receptors to extract information from the environment and neural circuits to perform subsequent computations. These computations may be described as algorithms composed of sequential mathematical operations. Comparing these operations across taxa reveals how different neural circuits have evolved to solve the same problem, even when using different mechanisms to implement the underlying math. In this review, we compare how insect and mammalian neural circuits have solved the problem of motion estimation, focusing on the fruit fly Drosophila and the mouse retina. Although the two systems implement computations with grossly different anatomy and molecular mechanisms, the underlying circuits transform light into motion signals with strikingly similar processing steps. These similarities run from photoreceptor gain control and spatiotemporal tuning to ON and OFF pathway structures, motion detection, and computed motion signals. The parallels between the two systems suggest that a limited set of algorithms for estimating motion satisfies both the needs of sighted creatures and the constraints imposed on them by metabolism, anatomy, and the structure and regularities of the visual world.

Diversity in spatial scope of contrast adaptation among mouse retinal ganglion cells

Retinal ganglion cells adapt to changes in visual contrast by adjusting their response kinetics and sensitivity. While much work has focused on the time scales of these adaptation processes, less is known about the spatial scale of contrast adaptation. For example, do small, localized contrast changes affect a cell's signal processing across its entire receptive field? Previous investigations have provided conflicting evidence, suggesting that contrast adaptation occurs either locally within subregions of a ganglion cell's receptive field or globally over the receptive field in its entirety. Here, we investigated the spatial extent of contrast adaptation in ganglion cells of the isolated mouse retina through multielectrode-array recordings. We applied visual stimuli so that ganglion cell receptive fields contained regions where the average contrast level changed periodically as well as regions with constant average contrast level. This allowed us to analyze temporal stimulus integration and sensitivity separately for stimulus regions with and without contrast changes. We found that the spatial scope of contrast adaptation depends strongly on cell identity, with some ganglion cells displaying clear local adaptation, whereas others, in particular large transient ganglion cells, adapted globally to contrast changes. Thus, the spatial scope of contrast adaptation in mouse retinal ganglion cells appears to be cell-type specific. This could reflect differences in mechanisms of contrast adaptation and may contribute to the functional diversity of different ganglion cell types.NEW & NOTEWORTHY Understanding whether adaptation of a neuron in a sensory system can occur locally inside the receptive field or whether it always globally affects the entire receptive field is important for understanding how the neuron processes complex sensory stimuli. For mouse retinal ganglion cells, we here show that both local and global contrast adaptation exist and that this diversity in spatial scope can contribute to the functional diversity of retinal ganglion cell types.

Olfactory receptor neurons use gain control and complementary kinetics to encode intermittent odorant stimuli

Insects find food and mates by navigating odorant plumes that can be highly intermittent, with intensities and durations that vary rapidly over orders of magnitude. Much is known about olfactory responses to pulses and steps, but it remains unclear how olfactory receptor neurons (ORNs) detect the intensity and timing of natural stimuli, where the absence of scale in the signal makes detection a formidable olfactory task. By stimulating Drosophila ORNs in vivo with naturalistic and Gaussian stimuli, we show that ORNs adapt to stimulus mean and variance, and that adaptation and saturation contribute to naturalistic sensing. Mean-dependent gain control followed the Weber-Fechner relation and occurred primarily at odor transduction, while variance-dependent gain control occurred at both transduction and spiking. Transduction and spike generation possessed complementary kinetic properties, that together preserved the timing of odorant encounters in ORN spiking, regardless of intensity. Such scale-invariance could be critical during odor plume navigation.

Cellular and Circuit Mechanisms Shaping the Perceptual Properties of the Primate Fovea

The fovea is a specialized region of the retina that dominates the visual perception of primates by providing high chromatic and spatial acuity. While the foveal and peripheral retina share a similar core circuit architecture, they exhibit profound functional differences whose mechanisms are unknown. Using intracellular recordings and structure-function analyses, we examined the cellular and synaptic underpinnings of the primate fovea. Compared to peripheral vision, the fovea displays decreased sensitivity to rapid variations in light inputs; this difference is reflected in the responses of ganglion cells, the output cells of the retina. Surprisingly, and unlike in the periphery, synaptic inhibition minimally shaped the responses of foveal midget ganglion cells. This difference in inhibition cannot however, explain the differences in the temporal sensitivity of foveal and peripheral midget ganglion cells. Instead, foveal cone photoreceptors themselves exhibited slower light responses than peripheral cones, unexpectedly linking cone signals to perceptual sensitivity.

Divisive suppression explains high-precision firing and contrast adaptation in retinal ganglion cells

Visual processing depends on specific computations implemented by complex neural circuits. Here, we present a circuit-inspired model of retinal ganglion cell computation, targeted to explain their temporal dynamics and adaptation to contrast. To localize the sources of such processing, we used recordings at the levels of synaptic input and spiking output in the in vitro mouse retina. We found that an ON-Alpha ganglion cell's excitatory synaptic inputs were described by a divisive interaction between excitation and delayed suppression, which explained nonlinear processing that was already present in ganglion cell inputs. Ganglion cell output was further shaped by spike generation mechanisms. The full model accurately predicted spike responses with unprecedented millisecond precision, and accurately described contrast adaptation of the spike train. These results demonstrate how circuit and cell-intrinsic mechanisms interact for ganglion cell function and, more generally, illustrate the power of circuit-inspired modeling of sensory processing.

[Recovery time as a potential new progression parameter for patients with advanced glaucomatous optic atrophy]

BACKGROUND

Functional tests in glaucoma diagnosis can monitor a potential perimetric progression. However white-on-white perimetry is limited in advanced glaucoma, whereas contrasts were detected reproducibly. Especially when stressing (i. e. adapting) the visual system, subsequent measurements yielded different results-the visual system needs a "recovery time". In the present study the recovery time was investigated in patients with advanced glaucoma. Additionally correlation analysis was done with standard perimetric parameters.

MATERIAL AND METHODS

Temporal contrast sensitivity (TCS) and recovery time (RT) were measured using the Erlanger Flicker Test in 61 probands (15 normals, 25 primary open-angle glaucomas, 17 secondary open-angle glaucomas, 4 narrow-angle glaucoma). Additionally, ophthalmological examinations and perimetry (Octopus G1) was done.

RESULTS

(1) TCS was significantly reduced in patients with advanced glaucoma (p < 0.001). (2) Test stimuli with 3 and 5 % contrast showed a large variability in contrast to higher contrasts (12 %, 25 %, 35 %). (3) RT_12%, RT_25% and RT_35% were significantly prolonged in advanced glaucoma (p < 0.001). (4) RT_25% correlated significantly with mean defect (p = 0.015).

CONCLUSION

Recovery time seems to be a potential parameter in advanced glaucoma follow-up, as it is reproducible, independently of cataract and fixation.

Efficient sensory cortical coding optimizes pursuit eye movements

In the natural world, the statistics of sensory stimuli fluctuate across a wide range. In theory, the brain could maximize information recovery if sensory neurons adaptively rescale their sensitivity to the current range of inputs. Such adaptive coding has been observed in a variety of systems, but the premise that adaptation optimizes behaviour has not been tested. Here we show that adaptation in cortical sensory neurons maximizes information about visual motion in pursuit eye movements guided by that cortical activity. We find that gain adaptation drives a rapid (<100 ms) recovery of information after shifts in motion variance, because the neurons and behaviour rescale their sensitivity to motion fluctuations. Both neurons and pursuit rapidly adopt a response gain that maximizes motion information and minimizes tracking errors. Thus, efficient sensory coding is not simply an ideal standard but a description of real sensory computation that manifests in improved behavioural performance.

A Computational Framework for Realistic Retina Modeling

Computational simulations of the retina have led to valuable insights about the biophysics of its neuronal activity and processing principles. A great number of retina models have been proposed to reproduce the behavioral diversity of the different visual processing pathways. While many of these models share common computational stages, previous efforts have been more focused on fitting specific retina functions rather than generalizing them beyond a particular model. Here, we define a set of computational retinal microcircuits that can be used as basic building blocks for the modeling of different retina mechanisms. To validate the hypothesis that similar processing structures may be repeatedly found in different retina functions, we implemented a series of retina models simply by combining these computational retinal microcircuits. Accuracy of the retina models for capturing neural behavior was assessed by fitting published electrophysiological recordings that characterize some of the best-known phenomena observed in the retina: adaptation to the mean light intensity and temporal contrast, and differential motion sensitivity. The retinal microcircuits are part of a new software platform for efficient computational retina modeling from single-cell to large-scale levels. It includes an interface with spiking neural networks that allows simulation of the spiking response of ganglion cells and integration with models of higher visual areas.

Glutamatergic Monopolar Interneurons Provide a Novel Pathway of Excitation in the Mouse Retina

Excitatory and inhibitory neurons in the CNS are distinguished by several features, including morphology, transmitter content, and synapse architecture [1]. Such distinctions are exemplified in the vertebrate retina. Retinal bipolar cells are polarized glutamatergic neurons receiving direct photoreceptor input, whereas amacrine cells are usually monopolar inhibitory interneurons with synapses almost exclusively in the inner retina [2]. Bipolar but not amacrine cell synapses have presynaptic ribbon-like structures at their transmitter release sites. We identified a monopolar interneuron in the mouse retina that resembles amacrine cells morphologically but is glutamatergic and, unexpectedly, makes ribbon synapses. These glutamatergic monopolar interneurons (GluMIs) do not receive direct photoreceptor input, and their light responses are strongly shaped by both ON and OFF pathway-derived inhibitory input. GluMIs contact and make almost as many synapses as type 2 OFF bipolar cells onto OFF-sustained A-type (AOFF-S) retinal ganglion cells (RGCs). However, GluMIs and type 2 OFF bipolar cells possess functionally distinct light-driven responses and may therefore mediate separate components of the excitatory synaptic input to AOFF-S RGCs. The identification of GluMIs thus unveils a novel cellular component of excitatory circuits in the vertebrate retina, underscoring the complexity in defining cell types even in this well-characterized region of the CNS.

Visual dysfunction and its correlation with retinal changes in patients with Parkinson's disease: an observational cross-sectional study

OBJECTIVES

To evaluate visual dysfunction and its correlation with structural changes in the retina in patients with Parkinson's disease (PD).

METHODS

Patients with PD (n=37) and controls (n=37) were included in an observational cross-sectional study, and underwent visual acuity (VA), colour vision (using the Farnsworth and Lanthony desaturated D15 colour tests) and contrast sensitivity vision (CSV; using the Pelli-Robson chart and CSV 1000E test) evaluation to measure visual dysfunction. Structural measurements of the retinal nerve fibre layer (RNFL), and macular and ganglion cell layer (GCL) thicknesses, were obtained using spectral domain optical coherence tomography (SD-OCT). Comparison of obtained data, and correlation analysis between functional and structural results were performed.

RESULTS

VA (in all different contrast levels) and all CSV spatial frequencies were significantly worse in patients with PD than in controls. Colour vision was significantly affected based on the Lanthony colour test. Significant GCL loss was observed in the minimum GCL+inner plexiform layer. A clear tendency towards a reduction in several macular sectors (central, outer inferior, outer temporal and superior (inner and outer)) and in the temporal quadrant of the RNFL thickness was observed, although the difference was not significant. CSV was the functional parameter most strongly correlated with structural measurements in PD. Colour vision was associated with most GCL measurements. Macular thickness was strongly correlated with macular volume and functional parameters (r>0.70, p<0.05).

CONCLUSIONS

Patients with PD had visual dysfunction that correlated with structural changes evaluated by SD-OCT. GCL measurements may be reliable indicators of visual impairment in patients with PD.

Color responses and their adaptation in human superior colliculus and lateral geniculate nucleus

We use an fMRI adaptation paradigm to explore the selectivity of human responses in the lateral geniculate nucleus (LGN) and superior colliculus (SC) to red-green color and achromatic contrast. We measured responses to red-green (RG) and achromatic (ACH) high contrast sinewave counter-phasing rings with and without adaptation, within a block design. The signal for the RG test stimulus was reduced following both RG and ACH adaptation, whereas the signal for the ACH test was unaffected by either adaptor. These results provide compelling evidence that the human LGN and SC have significant capacity for color adaptation. Since in the LGN red-green responses are mediated by P cells, these findings are in contrast to earlier neurophysiological data from non-human primates that have shown weak or no contrast adaptation in the P pathway. Cross-adaptation of the red-green color response by achromatic contrast suggests unselective response adaptation and points to a dual role for P cells in responding to both color and achromatic contrast. We further show that subcortical adaptation is not restricted to the geniculostriate system, but is also present in the superior colliculus (SC), an oculomotor region that until recently, has been thought to be color-blind. Our data show that the human SC not only responds to red-green color contrast, but like the LGN, shows reliable but unselective adaptation.

Contrast adaptation in the Limulus lateral eye

Luminance and contrast adaptation are neuronal mechanisms employed by the visual system to adjust our sensitivity to light. They are mediated by an assortment of cellular and network processes distributed across the retina and visual cortex. Both have been demonstrated in the eyes of many vertebrates, but only luminance adaptation has been shown in invertebrate eyes to date. Since the computational benefits of contrast adaptation should apply to all visual systems, we investigated whether this mechanism operates in horseshoe crab eyes, one of the best-understood neural networks in the animal kingdom. The spike trains of optic nerve fibers were recorded in response to light stimuli modulated randomly in time and delivered to single ommatidia or the whole eye. We found that the retina adapts to both the mean luminance and contrast of a white-noise stimulus, that luminance- and contrast-adaptive processes are largely independent, and that they originate within an ommatidium. Network interactions are not involved. A published computer model that simulates existing knowledge of the horseshoe crab eye did not show contrast adaptation, suggesting that a heretofore unknown mechanism may underlie the phenomenon. This mechanism does not appear to reside in photoreceptors because white-noise analysis of electroretinogram recordings did not show contrast adaptation. The likely site of origin is therefore the spike discharge mechanism of optic nerve fibers. The finding of contrast adaption in a retinal network as simple as the horseshoe crab eye underscores the broader importance of this image processing strategy to vision.