Receptive fields and functional architecture in the retina

Hierarchies in Visual Pathway: Functions and Inspired Artificial Vision

The development of artificial intelligence has posed a challenge to machine vision based on conventional complementary metal-oxide semiconductor (CMOS) circuits owing to its high latency and inefficient power consumption originating from the data shuffling between memory and computation units. Gaining more insights into the function of every part of the visual pathway for visual perception can bring the capabilities of machine vision in terms of robustness and generality. Hardware acceleration of more energy-efficient and biorealistic artificial vision highly necessitates neuromorphic devices and circuits that are able to mimic the function of each part of the visual pathway. In this paper, we review the structure and function of the entire class of visual neurons from the retina to the primate visual cortex within reach (Chapter 2) are reviewed. Based on the extraction of biological principles, the recent hardware-implemented visual neurons located in different parts of the visual pathway are discussed in detail in Chapters 3 and 4. Furthermore, valuable applications of inspired artificial vision in different scenarios (Chapter 5) are provided. The functional description of the visual pathway and its inspired neuromorphic devices/circuits are expected to provide valuable insights for the design of next-generation artificial visual perception systems.

Interleukin-6 in retinal diseases: From pathogenesis to therapy

Interleukin-6 (IL-6) is a pleiotropic cytokine that participates in immunomodulation, inflammation, increases vascular permeability, hematopoiesis, and stimulates cell proliferation, among other biological processes. It exerts effects primarily through the classic and trans-signaling pathways. Many studies have demonstrated that IL-6 plays a critical role in the development of retinal diseases including diabetic retinopathy, uveitis, age-related macular degeneration, glaucoma, retinal vein occlusion, central serous chorioretinopathy and proliferative vitreoretinopathy. Thus, the progressive development of drugs targeting IL-6 and IL-6 receptor may play a role in the treatment of multiple retinal diseases. In this article, we comprehensively review the IL-6's biological functions of and its mechanisms in the pathogenesis of various retinal diseases. Furthermore, we summarize the drugs targeting IL-6 and its receptor and prospect their potential application in retinal diseases, hoping to provide new ideas for the treatment of retinal diseases.

Contrast Increment and Decrement Processing in Individuals With and Without Diabetes

Purpose

Animal models suggest that ON retinal ganglion cells (RGCs) may be more vulnerable to diabetic insult than OFF cells. Using three psychophysical tasks to infer the function of ON and OFF RGCs, we hypothesized that functional responses to contrast increments will be preferentially affected in early diabetes mellitus (DM) compared to contrast decrement responses.

Methods

Fifty-two people with DM (type 1 or type 2) (mean age = 34.8 years, range = 18-60 years) and 48 age-matched controls (mean age = 35.4 years, range = 18-60 years) participated. Experiment 1 measured contrast sensitivity to increments and decrements at four visual field locations. Experiments 2 and 3 measured visual temporal processing using (i) a response time (RT) task, and (ii) a temporal order judgment task. Mean RT and accuracy were collected for experiment 2, whereas experiment 3 measured temporal thresholds.

Results

For experiment 1, the DM group showed reduced increment and decrement contrast sensitivity (F (1, 97) = 4.04, P = 0.047) especially for the central location. For experiment 2, those with DM demonstrated slower RT and lower response accuracies to increments and decrements (increments: U = 780, P = 0.01, decrements: U = 749, P = 0.005). For experiment 3, performance was similar between groups (F (1, 91) = 2.52, P = 0.137).

Conclusions

When assessed cross-sectionally, nonselective functional consequences of retinal neuron damage are present in early DM, particularly for foveal testing. Whether increment-decrement functional indices relate to diabetic retinopathy (DR) progression or poorer visual prognosis in DM requires further study.

Panoramic visual statistics shape retina-wide organization of receptive fields

Statistics of natural scenes are not uniform-their structure varies dramatically from ground to sky. It remains unknown whether these nonuniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. Using the mouse (Mus musculus) as a model species, we show that receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon, in agreement with our predictions. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell types.

Discrimination of textures with spatial correlations and multiple gray levels

Analysis of visual texture is important for many key steps in early vision. We study visual sensitivity to image statistics in three families of textures that include multiple gray levels and correlations in two spatial dimensions. Sensitivities to positive and negative correlations are approximately independent of correlation sign, and signals from different kinds of correlations combine quadratically. We build a computational model, fully constrained by prior studies of sensitivity to uncorrelated textures and black-and-white textures with spatial correlations. The model accounts for many features of the new data, including sign-independence, quadratic combination, and the dependence on gray-level distribution.

Efficient processing of natural scenes in visual cortex

Neural circuits in the periphery of the visual, auditory, and olfactory systems are believed to use limited resources efficiently to represent sensory information by adapting to the statistical structure of the natural environment. This "efficient coding" principle has been used to explain many aspects of early visual circuits including the distribution of photoreceptors, the mosaic geometry and center-surround structure of retinal receptive fields, the excess OFF pathways relative to ON pathways, saccade statistics, and the structure of simple cell receptive fields in V1. We know less about the extent to which such adaptations may occur in deeper areas of cortex beyond V1. We thus review recent developments showing that the perception of visual textures, which depends on processing in V2 and beyond in mammals, is adapted in rats and humans to the multi-point statistics of luminance in natural scenes. These results suggest that central circuits in the visual brain are adapted for seeing key aspects of natural scenes. We conclude by discussing how adaptation to natural temporal statistics may aid in learning and representing visual objects, and propose two challenges for the future: (1) explaining the distribution of shape sensitivity in the ventral visual stream from the statistics of object shape in natural images, and (2) explaining cell types of the vertebrate retina in terms of feature detectors that are adapted to the spatio-temporal structures of natural stimuli. We also discuss how new methods based on machine learning may complement the normative, principles-based approach to theoretical neuroscience.

Simulating the impact of photoreceptor loss and inner retinal network changes on electrical activity of the retina

Objective.A major reason for poor visual outcomes provided by existing retinal prostheses is the limited knowledge of the impact of photoreceptor loss on retinal remodelling and its subsequent impact on neural responses to electrical stimulation. Computational network models of the neural retina assist in the understanding of normal retinal function but can be also useful for investigating diseased retinal responses to electrical stimulation.Approach.We developed and validated a biophysically detailed discrete neuronal network model of the retina in the software package NEURON. The model includes rod and cone photoreceptors, ON and OFF bipolar cell pathways, amacrine and horizontal cells and finally, ON and OFF retinal ganglion cells with detailed network connectivity and neural intrinsic properties. By accurately controlling the network parameters, we simulated the impact of varying levels of degeneration on retinal electrical function.Main results.Our model was able to reproduce characteristic monophasic and biphasic oscillatory patterns seen in ON and OFF neurons during retinal degeneration (RD). Oscillatory activity occurred at 3 Hz with partial photoreceptor loss and at 6 Hz when all photoreceptor input to the retina was removed. Oscillations were found to gradually weaken, then disappear when synapses and gap junctions were destroyed in the inner retina. Without requiring any changes to intrinsic cellular properties of individual inner retinal neurons, our results suggest that changes in connectivity alone were sufficient to give rise to neural oscillations during photoreceptor degeneration, and significant network connectivity destruction in the inner retina terminated the oscillations.Significance.Our results provide a platform for further understanding physiological retinal changes with progressive photoreceptor and inner RD. Furthermore, our model can be used to guide future stimulation strategies for retinal prostheses to benefit patients at different stages of disease progression, particularly in the early and mid-stages of RD.

Eukaryotic gene regulation at equilibrium, or non?

Models of transcriptional regulation that assume equilibrium binding of transcription factors have been less successful at predicting gene expression from sequence in eukaryotes than in bacteria. This could be due to the non-equilibrium nature of eukaryotic regulation. Unfortunately, the space of possible non-equilibrium mechanisms is vast and predominantly uninteresting. The key question is therefore how this space can be navigated efficiently, to focus on mechanisms and models that are biologically relevant. In this review, we advocate for the normative role of theory-theory that prescribes rather than just describes-in providing such a focus. Theory should expand its remit beyond inferring mechanistic models from data, towards identifying non-equilibrium gene regulatory schemes that may have been evolutionarily selected, despite their energy consumption, because they are precise, reliable, fast, or otherwise outperform regulation at equilibrium. We illustrate our reasoning by toy examples for which we provide simulation code.

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

Retinal Encoding of Natural Scenes

An ultimate goal in retina science is to understand how the neural circuit of the retina processes natural visual scenes. Yet most studies in laboratories have long been performed with simple, artificial visual stimuli such as full-field illumination, spots of light, or gratings. The underlying assumption is that the features of the retina thus identified carry over to the more complex scenario of natural scenes. As the application of corresponding natural settings is becoming more commonplace in experimental investigations, this assumption is being put to the test and opportunities arise to discover processing features that are triggered by specific aspects of natural scenes. Here, we review how natural stimuli have been used to probe, refine, and complement knowledge accumulated under simplified stimuli, and we discuss challenges and opportunities along the way toward a comprehensive understanding of the encoding of natural scenes. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Age-related decline in function of ON and OFF visual pathways

Purpose

A simple psychophysical paradigm is available as a digital application in iOS devices such as iPad to measure the function of ON and OFF visual pathways. However, an age-matched normative database is not readily available. The purpose of the study is to evaluate the response of ON and OFF visual pathways as a function of age.

Methods

158 normal healthy adults (84 males and 74 females) whose age ranged 18–80 years participated in the study. None of them had any ocular disease (except cataract of grade II or less) and visual acuity of ≤ 20/25. Monocular testing (only one eye) was performed on the ‘EyeSpeed’ application on an iPad at 40cm distance. The targets ranged between 1 to 3 light or dark squares presented randomly in a noise background and participants responded by indicating the number of squares by touching the screen as fast as possible. The main outcome variables are reaction time, accuracy and performance index (1 / speed * accuracy).

Results

The median reaction time was shorter (Median (IQR): 1.53s (0.49) [dark] Vs 1.76s (0.58) [light], p < 0.001) and accuracy was higher (97.21% (3.30) [dark] Vs 95.15% (5.10) [light], p < 0.001) for dark targets than the light targets. Performance index and reaction time for both target types significantly correlated with age (ρ = -0.41 to -0.43; p < 0.001).

Conclusions

This normative database will be useful to quantify disease-specific defects. More importantly, the ON pathway function can potentially serve as a surrogate for rod photoreceptor function.

Adaptation in cone photoreceptors contributes to an unexpected insensitivity of primate On parasol retinal ganglion cells to spatial structure in natural images

Neural circuits are constructed from nonlinear building blocks, and not surprisingly overall circuit behavior is often strongly nonlinear. But neural circuits can also behave near linearly, and some circuits shift from linear to nonlinear behavior depending on stimulus conditions. Such control of nonlinear circuit behavior is fundamental to neural computation. Here, we study a surprising stimulus dependence of the responses of macaque On (but not Off) parasol retinal ganglion cells: these cells respond nonlinearly to spatial structure in some stimuli but near linearly to spatial structure in others, including natural inputs. We show that these differences in the linearity of the integration of spatial inputs can be explained by a shift in the balance of excitatory and inhibitory synaptic inputs that originates at least partially from adaptation in the cone photoreceptors. More generally, this highlights how subtle asymmetries in signaling - here in the cone signals - can qualitatively alter circuit computation.

A flexible capacitive photoreceptor for the biomimetic retina

Neuromorphic vision sensors have been extremely beneficial in developing energy-efficient intelligent systems for robotics and privacy-preserving security applications. There is a dire need for devices to mimic the retina's photoreceptors that encode the light illumination into a sequence of spikes to develop such sensors. Herein, we develop a hybrid perovskite-based flexible photoreceptor whose capacitance changes proportionally to the light intensity mimicking the retina's rod cells, paving the way for developing an efficient artificial retina network. The proposed device constitutes a hybrid nanocomposite of perovskites (methyl-ammonium lead bromide) and the ferroelectric terpolymer (polyvinylidene fluoride trifluoroethylene-chlorofluoroethylene). A metal-insulator-metal type capacitor with the prepared composite exhibits the unique and photosensitive capacitive behavior at various light intensities in the visible light spectrum. The proposed photoreceptor mimics the spectral sensitivity curve of human photopic vision. The hybrid nanocomposite is stable in ambient air for 129 weeks, with no observable degradation of the composite due to the encapsulation of hybrid perovskites in the hydrophobic polymer. The functionality of the proposed photoreceptor to recognize handwritten digits (MNIST) dataset using an unsupervised trained spiking neural network with 72.05% recognition accuracy is demonstrated. This demonstration proves the potential of the proposed sensor for neuromorphic vision applications.

The Changes of Irisin and Inflammatory Cytokines in the Age-Related Macular Degeneration and Retinal Vein Occlusion

PURPOSE

Age-related macular degeneration (AMD) and retinal vein occlusion (RVO) are irreversible chorioretinal diseases, which might induce severe damage in visual function. The metabolic factor and inflammatory factors might play important roles in the pathogenesis of AMD and RVO. The levels of irisin and 14 cytokines were analyzed in aqueous humor of AMD and RVO eyes to evaluate the roles of irisin and inflammatory factors.

METHODS

We collected aqueous humor samples from patients with AMD (n = 27), RVO (n = 30), and cataract (as control, n = 23) eyes. Samples were assayed using ELISA kit for irisin and a multiplex immunoassay kit for 14 cytokines. The macular thickness (MT) was measured with OCT in all included eyes.

RESULTS

MT in the RVO group is significantly higher than that in the AMD or control group. Irisin levels in the aqueous samples of AMD and RVO eyes were both significantly lower than that in the control. Furthermore, a positive correlation was found between irisin and MT in the RVO. Compared with the controls, AMD eyes had significantly higher levels of BDNF, VEGF-A, VEGF-R1, VEGF-R2, IL-10, TNF-α, VCAM-1, IP-10, and MCP-1. Similarly, RVO eyes had significantly higher levels of BDNF, VEGF-A, VEGF-R1, VEGF-R2, IL-6, IL-8, IL-10, TNF-α, ICAM-1, VCAM-1, IP-10, and MCP-1. However, there was no significant difference between the levels of PDGF-BB or TNF-β in these three groups. A negative correlation was found between VEGF-A and MT in AMD, as well as in control. Furthermore, a positive correlation was found between IL-6 and MT in the 80 included eyes, as well as in RVO. A positive correlation was found between ICAM-1 and MT in the 80 included eyes, as well as in RVO.

CONCLUSIONS

The metabolic factor, irisin levels in the aqueous humor are decreased in AMD and RVO eyes and show a positive correlation between irisin and MT in RVO eyes, prompting researchers to explore the relationship between irisin and macular edema. We also identified the higher expression of vascular growth factors (VEGF-A, VEGF-R1, and PDGF-BB), inflammatory cytokines (IL-6, IL-8, IL-10, and TNF-α), and chemokines (ICAM-1, VCAM-1, IP-10, and MCP-1) in AMD and RVO eyes.

Novel Cognitive Functions Arise at the Convergence of Macroscale Gradients

Functions in higher-order brain regions are the source of extensive debate. Although past trends have been to describe the brain-especially posterior cortical areas-in terms of a set of functional modules, a new emerging paradigm focuses on the integration of proximal functions. In this review, we synthesize emerging evidence that a variety of novel functions in the higher-order brain regions are due to convergence: convergence of macroscale gradients brings feature-rich representations into close proximity, presenting an opportunity for novel functions to arise. Using the TPJ as an example, we demonstrate that convergence is enabled via three properties of the brain: (1) hierarchical organization, (2) abstraction, and (3) equidistance. As gradients travel from primary sensory cortices to higher-order brain regions, information becomes abstracted and hierarchical, and eventually, gradients meet at a point maximally and equally distant from their sensory origins. This convergence, which produces multifaceted combinations, such as mentalizing another person's thought or projecting into a future space, parallels evolutionary and developmental characteristics in such regions, resulting in new cognitive and affective faculties.

Dietary Antioxidants in Age-Related Macular Degeneration and Glaucoma

Age-related macular degeneration (AMD) and glaucoma are ophthalmic neurodegenerative diseases responsible for irreversible vision loss in the world population. Only a few therapies can be used to slow down the progression of these diseases and there are no available treatment strategies for reversing the degeneration of the neural retina. In AMD, the pathological process causes the malfunction and damage of the retinal pigmented epithelium and photoreceptors in the macula. In glaucoma, damage of the retinal ganglion cells and their axons is observed and treatment strategies are limited to intraocular pressure lowering. Therefore, other prophylactic and/or therapeutic methods are needed. Oxidative stress is involved in the neurodegenerative process accompanying both AMD and glaucoma; therefore, the use of antioxidant agents would clearly be beneficial, which is supported by the decreased prevalence and progression of AMD in patients adherent to a diet naturally rich in antioxidants. Dietary antioxidants are easily available and their use is based on the natural route of administration. Many preclinical studies both in vitro and using animal models of retinal degeneration showed the efficacy of dietary antioxidants, which was further proved in clinical trials. Resveratrol is beneficial both in AMD and glaucoma animal models, but confirmed only among AMD patients. For AMD, carotenoids and omega-3 fatty acids were also proved to be sufficient in preventing neurodegeneration. For glaucoma, coenzyme Q10 and alpha-lipoic acid showed efficacy for decreasing retinal ganglion cell loss and inhibiting the accompanying destructive processes. Interestingly, the benefits of vitamins, especially vitamin E was not confirmed, neither in preclinical nor in clinical studies.

Visual processing: Systematic variation in light-dark bias across visual space

Detecting changes in luminance is a fundamental property of the visual system. A new study shows that lights and darks are represented differently across visual space, with strong OFF bias in central vision and balanced ON/OFF in the periphery.

Retinal Responses to Simulated Optical Blur Using a Novel Dead Leaves ERG Stimulus

Purpose

The purpose of this study was to evaluate retinal responses to different types and magnitudes of simulated optical blur presented at specific retinal eccentricities using naturalistic images.

Methods

Electroretinograms (ERGs) were recorded from 27 adults using 30-degree dead leaves naturalistic images, digitally blurred with one of three types of optical blur (defocus, astigmatism, and spherical aberrations), and one of three magnitudes (0.1, 0.3, or 0.5 µm) of blur. Digitally computed blur was applied to the entire image, or on an area outside the central 6 degrees or 12 degrees of retinal eccentricity.

Results

ERGs were significantly affected by blur type, magnitude, and retinal eccentricity. ERGs were differentially affected by defocus and spherical aberrations; however, astigmatism had no effect on the ERGs. When blur was applied only beyond the central 12 degrees eccentricity, the ERGs were unaffected. However, when blur was applied outside the central 6 degrees, the ERG responses were significantly reduced and were no different from the ERGs recorded with entirely blurred images.

Conclusions

Blur type, magnitude, and location all affect the retinal responses. Our data indicate that the retinal area between 6 and 12 degrees eccentricity has the largest effect on the retinal responses to blur. In addition, certain optical blur types appear to have a more detrimental effect on the ERGs than others. These results cannot be solely explained by changes to image contrast and spatial frequency content, suggesting that retinal neurons might be sensitive to spatial cues in order to differentiate between different blur types.

How inhibitory neurons increase information transmission under threshold modulation

Modulation of neuronal thresholds is ubiquitous in the brain. Phenomena such as figure-ground segmentation, motion detection, stimulus anticipation, and shifts in attention all involve changes in a neuron’s threshold based on signals from larger scales than its primary inputs. However, this modulation reduces the accuracy with which neurons can represent their primary inputs, creating a mystery as to why threshold modulation is so widespread in the brain. We find that modulation is less detrimental than other forms of neuronal variability and that its negative effects can be nearly completely eliminated if modulation is applied selectively to sparsely responding neurons in a circuit by inhibitory neurons. We verify these predictions in the retina where we find that inhibitory amacrine cells selectively deliver modulation signals to sparsely responding ganglion cell types. Our findings elucidate the central role that inhibitory neurons play in maximizing information transmission under modulation.

Modulation of neuronal thresholds is ubiquitous in the brain but reduces the accuracy of neural signaling. Hsu et al. show that the negative impact of threshold modulation can be almost completely eliminated when modulation is not delivered uniformly to all neurons but only to a subset and via inhibitory neurons.

Efficient population coding depends on stimulus convergence and source of noise

Sensory organs transmit information to downstream brain circuits using a neural code comprised of spikes from multiple neurons. According to the prominent efficient coding framework, the properties of sensory populations have evolved to encode maximum information about stimuli given biophysical constraints. How information coding depends on the way sensory signals from multiple channels converge downstream is still unknown, especially in the presence of noise which corrupts the signal at different points along the pathway. Here, we calculated the optimal information transfer of a population of nonlinear neurons under two scenarios. First, a lumped-coding channel where the information from different inputs converges to a single channel, thus reducing the number of neurons. Second, an independent-coding channel when different inputs contribute independent information without convergence. In each case, we investigated information loss when the sensory signal was corrupted by two sources of noise. We determined critical noise levels at which the optimal number of distinct thresholds of individual neurons in the population changes. Comparing our system to classical physical systems, these changes correspond to first- or second-order phase transitions for the lumped- or the independent-coding channel, respectively. We relate our theoretical predictions to coding in a population of auditory nerve fibers recorded experimentally, and find signatures of efficient coding. Our results yield important insights into the diverse coding strategies used by neural populations to optimally integrate sensory stimuli in the presence of distinct sources of noise.

Retino-Cortical Mapping Ratio Predicts Columnar and Salt-and-Pepper Organization in Mammalian Visual Cortex

In the mammalian primary visual cortex, neural tuning to stimulus orientation is organized in either columnar or salt-and-pepper patterns across species. For decades, this sharp contrast has spawned fundamental questions about the origin of functional architectures in visual cortex. However, it is unknown whether these patterns reflect disparate developmental mechanisms across mammalian taxa or simply originate from variation of biological parameters under a universal development process. In this work, after the analysis of data from eight mammalian species, we show that cortical organization is predictable by a single factor, the retino-cortical mapping ratio. Groups of species with or without columnar clustering are distinguished by the feedforward sampling ratio, and model simulations with controlled mapping conditions reproduce both types of organization. Prediction from the Nyquist theorem explains this parametric division of the patterns with high accuracy. Our results imply that evolutionary variation of physical parameters may induce development of distinct functional circuitry.

Nonlinear convergence boosts information coding in circuits with parallel outputs

Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.

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.

Electric Stimulation Elicits Heterogeneous Responses in ON but Not OFF Retinal Ganglion Cells to Transmit Rich Neural Information

Retinal implants electrically stimulate surviving retinal neurons to restore vision in people blinded by outer retinal degeneration. Although the healthy retina is known to transmit a vast amount of visual information to the brain, it has not been studied whether prosthetic vision contains a similar amount of information. Here, we assessed the neural information transmitted by population responses arising in brisk transient (BT) and brisk sustained (BS) subtypes of ON and OFF retinal ganglion cells (RGCs) in the rabbit retina. To correlate the response heterogeneity and the information transmission, we first quantified the cell-to-cell heterogeneity by calculating the spike time tiling coefficient (STTC) across spiking patterns of RGCs in each type. Then, we computed the neural information encoded by the RGC population in a given type. In responses to light stimulation, spiking activities were more heterogeneous in OFF than ON RGCs (STTC_AVG = 0.36, 0.45, 0.77 and 0.55 for OFF BT, OFF BS, ON BT, and ON BS, respectively). Interestingly, however, in responses to electric stimulation, both BT and BS subtypes of OFF RGCs showed remarkably homogeneous spiking patterns across cells (STTC_AVG = 0.93 and 0.82 for BT and BS, respectively), whereas the two subtypes of ON RGCs showed slightly increased populational heterogeneity compared to light-evoked responses (STTC_AVG = 0.71 and 0.63 for BT and BS, respectively). Consequently, the neural information encoded by the electrically-evoked responses of a population of 15 RGCs was substantially lower in the OFF than the ON pathway: OFF BT and BS cells transmit only ~23% and ~53% of the neural information transmitted by their ON counterparts. Together with previously-reported natural spiking activities in ON RGCs, the higher neural information may make ON responses more recognizable, eliciting the biased percepts of bright phosphenes.

Projection of Orthogonal Tiling from the Retina to the Visual Cortex

In higher mammals, the primary visual cortex (V1) is organized into diverse tuning maps of visual features. The topography of these maps intersects orthogonally, but it remains unclear how such a systematic relationship can develop. Here, we show that the orthogonal organization already exists in retinal ganglion cell (RGC) mosaics, providing a blueprint of the organization in V1. From analysis of the RGC mosaics data in monkeys and cats, we find that the ON-OFF RGC distance and ON-OFF angle of neighboring RGCs are organized into a topographic tiling across mosaics, analogous to the orthogonal intersection of cortical tuning maps. Our model simulation shows that the ON-OFF distance and angle in RGC mosaics correspondingly initiate ocular dominance/spatial frequency tuning and orientation tuning, resulting in the orthogonal intersection of cortical tuning maps. These findings suggest that the regularly structured ON-OFF patterns mirrored from the retina initiate the uniform representation of combinations of map features over the visual space.

Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition

In contrast to the previous artificial neural networks (ANNs), spiking neural networks (SNNs) work based on temporal coding approaches. In the proposed SNN, the number of neurons, neuron models, encoding method, and learning algorithm design are described in a correct and pellucid fashion. It is also discussed that optimizing the SNN parameters based on physiology, and maximizing the information they pass leads to a more robust network. In this paper, inspired by the "center-surround" structure of the receptive fields in the retina, and the amount of overlap that they have, a robust SNN is implemented. It is based on the Integrate-and-Fire (IF) neuron model and uses the time-to-first-spike coding to train the network by a newly proposed method. The Iris and MNIST datasets were employed to evaluate the performance of the proposed network whose accuracy, with 60 input neurons, was 96.33% on the Iris dataset. The network was trained in only 45 iterations indicating its reasonable convergence rate. For the MNIST dataset, when the gray level of each pixel was considered as input to the network, 600 input neurons were required, and the accuracy of the network was 90.5%. Next, 14 structural features were used as input. Therefore, the number of input neurons decreased to 210, and accuracy increased up to 95%, meaning that an SNN with fewer input neurons and good skill was implemented. Also, the ABIDE1 dataset is applied to the proposed SNN. Of the 184 data, 79 are used for healthy people and 105 for people with autism. One of the characteristics that can differentiate between these two classes is the entropy of the existing data. Therefore, Shannon entropy is used for feature extraction. Applying these values to the proposed SNN, an accuracy of 84.42% was achieved by only 120 iterations, which is a good result compared to the recent results.

Idiosyncratic preferences in transparent motion and binocular rivalry are dissociable

Previous studies revealed that there are idiosyncratic preferences to perceive certain motion directions in front during motion transparency depth rivalry (Mamassian & Wallace, 2010; Schütz, 2014). Meanwhile, other studies reported idiosyncratic preferences in binocular rivalry during the onset stage (Carter & Cavanagh, 2007; Stanley, Carter, & Forte, 2011). Here we investigated the relationship of idiosyncratic preferences in transparent motion and binocular rivalry. We presented two dot clouds that were moving in opposite directions. In the transparent motion condition, both dot clouds were presented to both eyes and participants had to report the dot cloud they perceived in front. In the binocular rivalry condition, the dot clouds were presented to different eyes and participants had to report the dominant dot cloud. There were strong idiosyncratic directional preferences in transparent motion and rather weak directional preferences in binocular rivalry. In general, binocular rivalry was dominated by biases in contrast polarity, whereas transparent motion was dominated by biases in motion direction. A circular correlation analysis showed no correlation between directional preferences in transparent motion and binocular rivalry. These findings show that idiosyncratic preferences in a visual feature can be dissociated at different stages of processing.

Unveiling the Secrets of Escher's Lithographs

An impossible structure gives us the impression of looking at a three-dimensional object, even though this object cannot exist, since it possesses parts that are spatially non-connectable, and are characterized by misleading geometrical properties not instantly evident. Therefore, impossible artworks appeal to our intellect and challenge our perceptive capacities. We analyzed lithographs containing impossible structures (e.g., the Necker cube), created by the famous Dutch painter Maurits Cornelis Escher (1898-1972), and used one of them (The Belvedere, 1958) to unveil the artist's hidden secrets by means of a discrete model of the human retina based on a non-uniform distribution of receptive fields. We demonstrated that the ability of Escher in composing his lithographs by connecting spatial coherent details into an impossible whole lies in drawing these incoherent fragments just outside the zone in which 3D coherence can be perceived during a single fixation pause. The main aspects of our paper from the point of view of image processing and image understanding are the following: (1) the peculiar and original digital filter to process the image, which simulates the human vision process, by producing a space-variant sampling of the image; (2) the software for the filter, which is homemade and created for our purposes. The filtered images resulting from the processing are used to understand impossible figures. As an example, we demonstrate how the impossible figures hidden in Escher's paintings can be understood.

Enhancer-Driven Gene Expression (EDGE) enables the generation of cell type specific tools for the analysis of neural circuits

As in all circuits, fully understanding how neural circuits operate requires the ability to specifically manipulate individual circuit elements, i.e. particular neuronal cell types. While recent years saw the development of molecular genetic tools allowing one to control and monitor neuronal activity, progress is limited by the ability to express such transgenes specifically enough. This goal is complicated by the fact that we are only beginning to understand how many cell types exist in the mammalian brain. Obtaining neuronal cell type-specific expression requires co-opting the genetic machinery which specifies their striking diversity, typically done by making transgenic animals using promoters expressing in neurons. However, while the vast majority of genes express in the brain, they almost always express in multiple cell types, meaning native promoters are not specific enough. We have recently taken a new approach to increase the specificity of transgene expression based upon identifying the distal cis-regulatory genomic elements (i.e. enhancers) uniquely active in a brain region and combining them with a heterologous minimal promoter. Termed Enhancer-Driven Gene Expression (EDGE), it allows for the generation of transgenic animals targeting the cell types of any brain region with far greater specificity than can be obtained with native promoters. Moreover, their small size allows for the generation of cell-specific viral vectors, conceivably enabling circuit-specific manipulations to any species.

Asymmetric ON-OFF processing of visual motion cancels variability induced by the structure of natural scenes

Animals detect motion using a variety of visual cues that reflect regularities in the natural world. Experiments in animals across phyla have shown that motion percepts incorporate both pairwise and triplet spatiotemporal correlations that could theoretically benefit motion computation. However, it remains unclear how visual systems assemble these cues to build accurate motion estimates. Here, we used systematic behavioral measurements of fruit fly motion perception to show how flies combine local pairwise and triplet correlations to reduce variability in motion estimates across natural scenes. By generating synthetic images with statistics controlled by maximum entropy distributions, we show that the triplet correlations are useful only when images have light-dark asymmetries that mimic natural ones. This suggests that asymmetric ON-OFF processing is tuned to the particular statistics of natural scenes. Since all animals encounter the world's light-dark asymmetries, many visual systems are likely to use asymmetric ON-OFF processing to improve motion estimation.

At least two distinct mechanisms control binocular luster, rivalry, and perceived rotation with contrast and average luminance disparities

When one views a square-wave grating and dichoptically changes the average luminance or contrast of the monocular images, at least three perceptual phenomena might occur. These are the Venetian blind effect, or a perceived rotation of the bars around individual vertical axes; binocular luster, or a perceived shimmering; and binocular rivalry, or an alternating perception between the views of the two eyes. Perception of luster and rivalry occur when the "light bars" in the grating dichoptically straddle the background luminance (one eye's image has a higher luminance than the background and the other eye's image has a lower luminance than the background), with little impact from the "dark bars." Perception of rotation, on the other hand, is related to average luminance or contrast disparity, independent of whether or not the "light bars" straddle the background luminance. The patterns for perceived rotation versus binocular luster and binocular rivalry suggest at least two separate mechanisms in the visual system for processing luminance and contrast information over and above their differing physiological states suggested by their different appearances. While luster and rivalry depend directly on the relation between stimuli and the background, perceived rotation depends on the magnitude of the luminance or contrast disparity, as described by the generalized difference model.

Intranasal administration of erythropoietin rescues the photoreceptors in degenerative retina: a noninvasive method to deliver drugs to the eye

Inherited retinopathies typically lead to photoreceptor loss and severe visual impairments in the subjects. Intranasal administration is an efficient approach to deliver therapeutic agents to the targeted tissue. The present study is designed to deliver the erythropoietin (EPO) into the N-methyl-N-nitrosourea (MNU) induced mice, a pharmacological retinopathy model via intranasal or intravenous route. The mice were then subjected to bioavailability assay and therapeutic effects evaluation. Our results showed that the intranasal delivery of EPO is effective to alleviate the morphological disruptions in the MNU induced mice. The intranasal delivery of EPO also ameliorated the visual impairments in the MNU induced mice. Immunostaining experiment showed that both the M-cone and S-cone populations in the degenerative retinas are rescued by the intranasal delivery of EPO. In particular, the M-cone photoreceptors in dorsal-temporal (DT) quadrant and the S-cone photoreceptors in ventral-nasal (VN) quadrant were preferentially preserved by the intranasal delivery of EPO. Mechanism studies showed that the intranasal delivery of EPO could the modulate apoptosis and restrict oxidation in the degenerative retina. Compared with intravenous delivery, the intranasal delivery led to the significantly higher EPO concentration in the retina. The intranasal delivery resulted in more potent protection and had less erythropoiesis-stimulating activity than the intravenous delivery. Our results suggest that the intranasal administration is a noninvasive and efficient approach to deliver EPO into the retinas. These findings lay the groundwork for further intranasal administration of EPO in ophthalmological practice.

Curcumin: A new candidate for retinal disease therapy?

The retina is the neural portion and light-sensitive layer of the eye, which has been observed in most of the vertebrates. The retina is composed of light-sensitive cells that absorb light and convert it into neural signals. These signals are sent to the brain for visual recognition. It has been shown that many pathogenesis conditions, including inflammation, angiogenesis, oxidative stress, and imbalanced histone modifications in the retina are associated with initiation and progression of retinal diseases (ie, glaucoma, diabetic retinopathy, and age-related macular degeneration). Currently available treatments include laser surgery, freezing, stem-cell therapy, shrinking abnormal blood vessels. It has some limitations, such as invasive methods, high costs, and many side effects. Hence, finding a new therapeutic platform for stopping or slowing of the disease progression is required. Curcumin is a natural product, which is associated with a wide range of properties, such as antioxidant, anti-inflammatory, antiangiogenic, and antitumor activates. It exerts therapeutic effects via activation/inhibition cellular and molecular targets involved in various diseases, such as retinal diseases. Increasing evidence revealed that curcumin can be used as a therapeutic option in the treatment of different retinal diseases. Here, we summarized various clinical and preclinical studies that used curcumin as a therapeutic agent in the treatment of retinal disorders.

A computational study of the role of spatial receptive field structure in processing natural and non-natural scenes

The center-surround receptive field structure, ubiquitous in the visual system, is hypothesized to be evolutionarily advantageous in image processing tasks. We address the potential functional benefits and shortcomings of spatial localization and center-surround antagonism in the context of an integrate-and-fire neuronal network model with image-based forcing. Utilizing the sparsity of natural scenes, we derive a compressive-sensing framework for input image reconstruction utilizing evoked neuronal firing rates. We investigate how the accuracy of input encoding depends on the receptive field architecture, and demonstrate that spatial localization in visual stimulus sampling facilitates marked improvements in natural scene processing beyond uniformly-random excitatory connectivity. However, for specific classes of images, we show that spatial localization inherent in physiological receptive fields combined with information loss through nonlinear neuronal network dynamics may underlie common optical illusions, giving a novel explanation for their manifestation. In the context of signal processing, we expect this work may suggest new sampling protocols useful for extending conventional compressive sensing theory.

Artificial Shape Perception Retina Network Based on Tunable Memristive Neurons

Retina shows an extremely high signal processing efficiency because of its specific signal processing strategy which called computing in sensor. In retina, photoreceptor cells encode light signals into spikes and ganglion cells finish the shape perception process. In order to realize the neuromorphic vision sensor, the one-transistor-one-memristor (1T1M) structure which formed by one memristor and one MOSFET in serial is used to construct photoreceptor cell and ganglion cell. The voltage changes between two terminals of memristor and MOSFET can mimic the changes of membrane potential caused by spikes and illumination respectively. In this paper, the tunable memristive neurons with 1T1M structures are built. According to the concept of receptive field of ganglion cells (GCs) in the retina, the artificial shape perception retina network is constructed with these memristive neurons. The final results show that the artificial retina can extract shape information from the image and transfer it into spike frequency realizing the function of computing in sensor.

Finding numbers in the brain

After listing functional constraints on what numbers in the brain must do, I sketch the two's complement fixed-point representation of numbers because it has stood the test of time and because it illustrates the non-obvious ways in which an effective coding scheme may operate. I briefly consider its neurobiological implementation. It is easier to imagine its implementation at the cell-intrinsic molecular level, with thermodynamically stable, volumetrically minimal polynucleotides encoding the remembered numbers, than at the circuit level, with plastic synapses encoding them.This article is part of a discussion meeting issue 'The origins of numerical abilities'.

Toward a unified theory of efficient, predictive, and sparse coding

A central goal in theoretical neuroscience is to predict the response properties of sensory neurons from first principles. To this end, "efficient coding" posits that sensory neurons encode maximal information about their inputs given internal constraints. There exist, however, many variants of efficient coding (e.g., redundancy reduction, different formulations of predictive coding, robust coding, sparse coding, etc.), differing in their regimes of applicability, in the relevance of signals to be encoded, and in the choice of constraints. It is unclear how these types of efficient coding relate or what is expected when different coding objectives are combined. Here we present a unified framework that encompasses previously proposed efficient coding models and extends to unique regimes. We show that optimizing neural responses to encode predictive information can lead them to either correlate or decorrelate their inputs, depending on the stimulus statistics; in contrast, at low noise, efficiently encoding the past always predicts decorrelation. Later, we investigate coding of naturalistic movies and show that qualitatively different types of visual motion tuning and levels of response sparsity are predicted, depending on whether the objective is to recover the past or predict the future. Our approach promises a way to explain the observed diversity of sensory neural responses, as due to multiple functional goals and constraints fulfilled by different cell types and/or circuits.

Interlayer Repulsion of Retinal Ganglion Cell Mosaics Regulates Spatial Organization of Functional Maps in the Visual Cortex

In higher mammals, orientation tuning of neurons is organized into a quasi-periodic pattern in the primary visual cortex. Our previous model studies suggested that the topography of cortical orientation maps may originate from moiré interference of ON and OFF retinal ganglion cell (RGC) mosaics, but did not account for how the consistent spatial period of maps could be achieved. Here we address this issue with two crucial findings on the development of RGC mosaics: first, homotypic local repulsion between RGCs can develop a long-range hexagonal periodicity. Second, heterotypic interaction restrains the alignment of ON and OFF mosaics, and generates a periodic interference pattern map with consistent spatial frequency. To validate our model, we quantitatively analyzed the RGC mosaics in cat data, and confirmed that the observed retinal mosaics showed evidence of heterotypic interactions, contrary to the previous view that ON and OFF mosaics are developed independently.SIGNIFICANCE STATEMENT Orientation map is one of the most studied functional maps in the brain, but it has remained unanswered how the consistent spatial periodicity of maps could be developed. In the current study, we address this issue with our developmental model for the retinal origin of orientation map. We showed that local repulsive interactions between retinal ganglion cells (RGCs) can develop a hexagonal periodicity in the RGC mosaics and restrict the alignment between ON and OFF mosaics, so that they generate a periodic pattern with consistent spatial frequency for both the RGC mosaics and the cortical orientation maps. Our results demonstrate that the organization of functional maps in visual cortex, including its structural consistency, may be constrained by a retinal blueprint.

Textures as Probes of Visual Processing

Visual textures are a class of stimuli with properties that make them well suited for addressing general questions about visual function at the levels of behavior and neural mechanism. They have structure across multiple spatial scales, they put the focus on the inferential nature of visual processing, and they help bridge the gap between stimuli that are analytically convenient and the complex, naturalistic stimuli that have the greatest biological relevance. Key questions that are well suited for analysis via visual textures include the nature and structure of perceptual spaces, modulation of early visual processing by task, and the transformation of sensory stimuli into patterns of population activity that are relevant to perception.

Energy-efficient neural information processing in individual neurons and neuronal networks

Brains are composed of networks of an enormous number of neurons interconnected with synapses. Neural information is carried by the electrical signals within neurons and the chemical signals among neurons. Generating these electrical and chemical signals is metabolically expensive. The fundamental issue raised here is whether brains have evolved efficient ways of developing an energy-efficient neural code from the molecular level to the circuit level. Here, we summarize the factors and biophysical mechanisms that could contribute to the energy-efficient neural code for processing input signals. The factors range from ion channel kinetics, body temperature, axonal propagation of action potentials, low-probability release of synaptic neurotransmitters, optimal input and noise, the size of neurons and neuronal clusters, excitation/inhibition balance, coding strategy, cortical wiring, and the organization of functional connectivity. Both experimental and computational evidence suggests that neural systems may use these factors to maximize the efficiency of energy consumption in processing neural signals. Studies indicate that efficient energy utilization may be universal in neuronal systems as an evolutionary consequence of the pressure of limited energy. As a result, neuronal connections may be wired in a highly economical manner to lower energy costs and space. Individual neurons within a network may encode independent stimulus components to allow a minimal number of neurons to represent whole stimulus characteristics efficiently. This basic principle may fundamentally change our view of how billions of neurons organize themselves into complex circuits to operate and generate the most powerful intelligent cognition in nature. © 2017 Wiley Periodicals, Inc.

Optimizing Neural Information Capacity through Discretization

Discretization in neural circuits occurs on many levels, from the generation of action potentials and dendritic integration, to neuropeptide signaling and processing of signals from multiple neurons, to behavioral decisions. It is clear that discretization, when implemented properly, can convey many benefits. However, the optimal solutions depend on both the level of noise and how it impacts a particular computation. This Perspective discusses how current physiological data could potentially be integrated into one theoretical framework based on maximizing information. Key experiments for testing that framework are discussed.

Electrosensory neural responses to natural electro-communication stimuli are distributed along a continuum

Neural heterogeneities are seen ubiquitously within the brain and greatly complicate classification efforts. Here we tested whether the responses of an anatomically well-characterized sensory neuron population to natural stimuli could be used for functional classification. To do so, we recorded from pyramidal cells within the electrosensory lateral line lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus in response to natural electro-communication stimuli as these cells can be anatomically classified into six different types. We then used two independent methodologies to functionally classify responses: one relies of reducing the dimensionality of a feature space while the other directly compares the responses themselves. Both methodologies gave rise to qualitatively similar results: while ON and OFF-type cells could easily be distinguished from one another, ELL pyramidal neuron responses are actually distributed along a continuum rather than forming distinct clusters due to heterogeneities. We discuss the implications of our results for neural coding and highlight some potential advantages.

Sensitivity of the avian motion system to light and dark stimuli

Global motion perception is important for mobile organisms. In laterally eyed birds, global motion appears to be processed in the entopallium, a neural structure that is part of the tectofugal pathway. Electrophysiological research has shown that motion selective cells in the entopallium are most responsive to small dark moving targets. Here, we investigated whether this bias toward dark targets of entopallial cells is mirrored by perceptual performance in a motion detection task in pigeons. We measured the detection thresholds of pigeons using random dot stimuli that consisted of either black or white dots on a gray background. We found that thresholds were significantly lower when using black dots as opposed to white dots. This heightened sensitivity is also noted in the learning rates of the pigeons. That is, we found that the pigeons learned the detection task significantly faster when the stimuli consisted of black dots. We believe that our results have important implications for the understanding of the functional role of the entopallium and the ON and OFF pathways in the avian motion system.

Neurons in primary visual cortex represent distribution of luminance

To efficiently detect a wide range of light-intensity changes, visual neurons must adapt to ambient luminance. However, how neurons in the primary visual cortex (V1) code the distribution of luminance remains unknown. We designed stimuli that represent rapid changes in luminance under different luminance distributions and investigated V1 neuron responses to these novel stimuli. We demonstrate that V1 neurons represent luminance changes by dynamically adjusting their responses when the luminance distribution changes. Many cells (35%) detected luminance changes by responding to dark stimuli when the distribution was dominated by bright stimuli, bright stimuli when dominated by dark stimuli, and both dark and bright stimuli when dominated by intermediate luminance stimuli; 13% of cells signaled the mean luminance that was varied with different distributions; the remaining 52% of cells gradually shifted the responses that were most sensitive to luminance changes when the luminance distribution varied. The remarkable response changes of the former two cell groups suggest their crucial roles in detecting luminance changes. These response characteristics demonstrate that V1 neurons are not only sensitive to luminance change, but also luminance distribution change. They encode luminance changes according to the luminance distribution. Mean cells represent the prevailing luminance and reversal cells represent the salient stimuli in the environment.

Improved Compressive Sensing of Natural Scenes Using Localized Random Sampling

Compressive sensing (CS) theory demonstrates that by using uniformly-random sampling, rather than uniformly-spaced sampling, higher quality image reconstructions are often achievable. Considering that the structure of sampling protocols has such a profound impact on the quality of image reconstructions, we formulate a new sampling scheme motivated by physiological receptive field structure, localized random sampling, which yields significantly improved CS image reconstructions. For each set of localized image measurements, our sampling method first randomly selects an image pixel and then measures its nearby pixels with probability depending on their distance from the initially selected pixel. We compare the uniformly-random and localized random sampling methods over a large space of sampling parameters, and show that, for the optimal parameter choices, higher quality image reconstructions can be consistently obtained by using localized random sampling. In addition, we argue that the localized random CS optimal parameter choice is stable with respect to diverse natural images, and scales with the number of samples used for reconstruction. We expect that the localized random sampling protocol helps to explain the evolutionarily advantageous nature of receptive field structure in visual systems and suggests several future research areas in CS theory and its application to brain imaging.

Ribbon Synapses and Visual Processing in the Retina

The first synapses transmitting visual information contain an unusual organelle, the ribbon, which is involved in the transport and priming of vesicles to be released at the active zone. The ribbon is one of many design features that allow efficient refilling of the active zone, which in turn enables graded changes in membrane potential to be transmitted using a continuous mode of neurotransmitter release. The ribbon also plays a key role in supplying vesicles for rapid and transient bursts of release that signal fast changes, such as the onset of light. We increasingly understand how the physiological properties of ribbon synapses determine basic transformations of the visual signal and, in particular, how the process of refilling the active zone regulates the gain and adaptive properties of the retinal circuit. The molecular basis of ribbon function is, however, far from clear.

Effect of Age and Glaucoma on the Detection of Darks and Lights

PURPOSE

We have shown previously that normal observers detect dark targets faster and more accurately than light targets, when presented in noisy backgrounds. We investigated how these differences in detection time and accuracy are affected by age and ganglion cell pathology associated with glaucoma.

METHODS

We asked 21 glaucoma patients, 21 age-similar controls, and 5 young control observers to report as fast as possible the number of 1 to 3 light or dark targets. The targets were positioned at random in a binary noise background, within the central 30° of the visual field.

RESULTS

We replicate previous findings that darks are detected faster and more accurately than lights. We extend these findings by demonstrating that differences in detection of darks and lights are found reliably across different ages and in observers with glaucoma. We show that differences in detection time increase at a rate of approximately 55 msec/dB at early stages of glaucoma and then remain constant at later stages at approximately 800 msec. In normal subjects, differences in detection time increase with age at a rate of approximately 8 msec/y. We also demonstrate that the accuracy to detect lights and darks is significantly correlated with the severity of glaucoma and that the mean detection time is significantly longer for subjects with glaucoma than age-similar controls.

CONCLUSIONS

We conclude that differences in detection of darks and lights can be demonstrated over a wide range of ages, and asymmetries in dark/light detection increase with age and early stages of glaucoma.