Vision Science seminars

Visual processing begins in the retina: within only two synaptic layers, multiple parallel feature channels emerge, which relay highly processed visual information to different parts of the brain. To functionally characterize these feature channels we perform calcium and glutamate population activity recordings at different levels of the mouse retina. This allows following the complete visual signal across consecutive processing stages in a systematic way. In my talk, I will summarize our recent findings on the functional diversity of retinal output channels and how they arise within the retinal network. Specifically, I will talk about the role of inhibition and cell-type specific dendritic processing in generating diverse visual channels. Then, I will focus on how color – a single visual feature – emerges across all retinal processing layers and link our results to behavioral output and the statistics of mouse natural scenes. With our approach, we hope to identify general computational principles of retinal signaling, thereby increasing our understanding of what the eye tells the brain.

I will introduce a web portal for the retinal neuroscience community to explore the catalog of mouse retinal ganglion cell (RGC) types, including data on light responses, correspondences with morphological types in EyeWire, and gene expression data from single-cell transcriptomics. Our current classification includes 43 types, accounting for 90% of the cells in EyeWire. Many of these cell types have new stories to tell, and I will cover two of them that represent opposite ends of the spectrum of levels of analysis in my lab. First, I will introduce the “Bursty Suppressed-by-Contrast” RGC and show how its intrinsic properties rather than its synaptic inputs differentiate its function from that of a different well-known RGC type. Second, I will present the histogram of cell types that project to the Olivary Pretectal Nucleus, focusing on the recently discovered M6 ipRGC.

The development of neural circuits is profoundly impacted by both spontaneous and sensory experience. This is perhaps most well studied in the visual system, where disruption of early spontaneous activity called retinal waves prior to eye opening and visual deprivation after eye opening leads to alterations in the response properties and connectivity in several visual centers in the brain. We address this question in the retina, which comprises multiple circuits that encode different features of the visual scene, culminating in over 40 different types of retinal ganglion cells. Direction-selective ganglion cells respond strongly to an image moving in the preferred direction and weakly to an image moving in the opposite, or null, direction. Moreover, as recently described (Sabbah et al, 2017) the preferred directions of direction selective ganglion cells cluster along four directions that align along two optic flow axes, causing variation of the relative orientation of preferred directions along the retinal surface. I will provide recent progress in the lab that addresses the role of visual experience and spontaneous retinal waves in the establishment of direction selective tuning and direction selectivity maps in the retina.

Electronic interfaces to the retina represent an exciting development in science, engineering, and medicine – an opportunity to exploit our knowledge of neural circuitry and function to restore or even enhance vision. However, although existing devices demonstrate proof of principle in treating incurable blindness, they produce limited visual function. Some of the reasons for this can be understood based on the precise and specific neural circuitry that mediates visual signaling in the retina. Consideration of this circuitry suggests that future devices may need to operate at single-cell, single-spike resolution in order to mediate naturalistic visual function. I will show large-scale multi-electrode recording and stimulation data from the primate retina indicating that, in some cases, such resolution is possible. I will also discuss cases in which it fails, and propose that we can improve artificial vision in such conditions by incorporating our knowledge of the visual system in bi-directional devices that adapt to the host neural circuitry. Finally, I will introduce the Stanford Artificial Retina Project, aimed at developing a retinal implant that more faithfully reproduces the neural code of the retina, and briefly discuss the implications for scientific investigation and for other neural interfaces of the future.

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside examples of how the associated empirical results can be combined with targeted models to shape our understanding of the underlying perceptual mechanisms. Although the emerging view is far from complete, it challenges compartmentalized notions of bottom-up/top-down object segmentation, and suggests instead that these two modes are best viewed as an integrated perceptual mechanism.

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviourally in mice. The discrepancy between neural and behavioural discrimination could not be explained by the types of stimuli we used, by behavioural states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

Axons in the optic nerve are arranged in bundles and conducting action potential with resistance related to their membrane. Optic nerve axons do not form absolutely independent conductive channels. They are directly coupled by gap junctions formed in majority by neuronal Cx45. Coupling of axons, except known transpassing functions, allows to reduce axonal membrane resistance of optic nerve and accelerates transduction of visual signal. This novel finding have substantial implications for understanding of the pathogenesis of various optic neuropathies and identifies a new potential target for a therapeutic approach.

The last quarter century has provided extensive evidence that some regions of the human cortex are selectively engaged in processing a single specific domain of information, from faces, places, and bodies to language, music, and other people’s thoughts. This work dovetails with earlier theories in cognitive science highlighting domain specificity in human cognition, development, and evolution. But many questions remain unanswered about even the clearest cases of domain specificity in the brain, the selective engagement of the FFA, PPA, and EBA in the perception of faces, places, and bodies, respectively. First, these claims lack precision, saying little about what is computed and how, and relying on human judgements to decide what counts as a face, place, or body. Second, they provide no account of the reliably varying responses of these regions across different “preferred” images, or across different “nonpreferred” images for each category. Third, the category selectivity of each region is vulnerable to refutation if any of the vast set of as-yet-untested nonpreferred images turns out to produce a stronger response than preferred images for that region. Fourth, and most fundamentally, they provide no account of why, from a computational point of view, brains should exhibit this striking degree of functional specificity in the first place, and why we should have the particular visual specializations we do, for faces, places, and bodies, but not (apparently) for food or snakes. The advent of convolutional neural networks (CNNs) to model visual processing in the ventral pathway has opened up many opportunities to address these long-standing questions in new ways. I will describe ongoing efforts in our lab to harness CNNs to do just that.

Electrophysiology of eye and visual pathway is useful tool in ophthalmology and neurology. It covers a few examinations to find out if defect of vision is peripheral or central. Visual evoked potentials (VEP) are most frequently used in neurology and neuroophthalmology. VEP are evoked by flash or pattern stimulations. The combination of these both examinations gives more information about the visual pathway. It is very important to remember that VEP originate in the retina and reflect its function as well. In many cases not only VEP but also electroretinography (ERG) is essential for diagnosis. The seminar presents basic electrophysiological procedures used for diagnosis and follow-up of optic neuropathies and some of central nervous system diseases which affect vision (mostly multiple sclerosis, CNS tumors, stroke, traumas, intracranial hypertension).

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.

How nervous systems control behaviour is the main question we seek to answer in neuroscience. Although visual systems have been a popular entry point into the brain, we don’t understand—in any deep sense—how visual perception guides navigation in flies (or any organism). I will present recent progress towards this goal from our lab. We are using anatomical insights from connectomics, genetic methods for labelling and manipulating identified cell types, neurophysiology, behaviour, and computational modeling to explain how the fly brain processes visual motion to regulate behaviour.

Many visual systems can process information in dynamically changing environments. In general, visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. This is achieved by adaptation. However, visual perception is challenged when adaptation is not fast enough to deal with sudden changes in overall illumination, for example when gaze follows a moving object from bright sunlight into a shaded area. We have recently shown that the visual system of the fly found a solution by propagating a corrective luminance-sensitive signal to higher processing stages. Using in vivo two-photon imaging and behavioural analyses we showed that distinct OFF-pathway inputs encode contrast and luminance. The luminance-sensitive pathway is particularly required when processing visual motion in contextual dim light, when pure contrast sensitivity underestimates the salience of a stimulus. Recent work in the lab has addressed the question how two visual pathways obtain such fundamentally different sensitivities, given common photoreceptor input. We are furthermore currently working out the network-based strategies by which luminance- and contrast-sensitive signals are combined to guide appropriate visual behaviour. Together, I will discuss the molecular, cellular, and circuit mechanisms that ensure contrast computation, and therefore robust vision, in fast changing visual scenes.

The design of neural circuits, with large numbers of neurons interconnected in vast networks, strongly suggest that they are specifically build to generate emergent functional properties (1). To explore this hypothesis, we have developed two-photon holographic methods to selective image and manipulate the activity of neuronal populations in 3D in vivo (2). Using them we find that groups of synchronous neurons (neuronal ensembles) dominate the evoked and spontaneous activity of mouse primary visual cortex (3). Ensembles can be optogenetically imprinted for several days and some of their neurons trigger the entire ensemble (4). By activating these pattern completion cells in ensembles involved in visual discrimination paradigms, we can bi-directionally alter behavioural choices (5). Our results demonstrate that ensembles are necessary and sufficient for visual perception and are consistent with the possibility that neuronal ensembles are the functional building blocks of cortical circuits. 1. R. Yuste, From the neuron doctrine to neural networks. Nat Rev Neurosci 16, 487-497 (2015). 2. L. Carrillo-Reid, W. Yang, J. E. Kang Miller, D. S. Peterka, R. Yuste, Imaging and Optically Manipulating Neuronal Ensembles. Annu Rev Biophys, 46: 271-293 (2017). 3. J. E. Miller, I. Ayzenshtat, L. Carrillo-Reid, R. Yuste, Visual stimuli recruit intrinsically generated cortical ensembles. Proceedings of the National Academy of Sciences of the United States of America 111, E4053-4061 (2014). 4. L. Carrillo-Reid, W. Yang, Y. Bando, D. S. Peterka, R. Yuste, Imprinting and recalling cortical ensembles. Science 353, 691-694 (2016). 5. L. Carrillo-Reid, S. Han, W. Yang, A. Akrouh, R. Yuste, (2019). Controlling visually-guided behaviour by holographic recalling of cortical ensembles. Cell 178, 447-457. DOI:https://doi.org/10.1016/j.cell.2019.05.045.

Understanding how neural circuits in the brain compute information not only requires determining how individual inhibitory and excitatory elements of circuits are wired together, but also a detailed knowledge of their functional interactions. Recent advances in optogenetic techniques and mouse genetics now offer ways to specifically probe the functional properties of neural circuits with unprecedented specificity. Perhaps one of the most heavily interrogated circuits in the mouse brain is one in the retina that is involved in coding direction (reviewed by Mauss et al., 2017; Vaney et al., 2012). In this circuit, direction is encoded by specialized direction-selective (DS) ganglion cells (DSGCs), which respond robustly to objects moving in a ‘preferred’ direction but not in the opposite or ‘null’ direction (Barlow and Levick, 1965). We now know this computation relies on the coordination of three transmitter systems: glutamate, GABA and acetylcholine (ACh). In this talk, I will discuss the synaptic mechanisms that produce the spatiotemporal patterns of inhibition and excitation that are crucial for shaping directional selectivity. Special emphasis will be placed on the role of ACh, as it is unclear whether it is mediated by synaptic or non-synaptic mechanisms, which is in fact a central issue in the CNS. Barlow, H.B., and Levick, W.R. (1965). The mechanism of directionally selective units in rabbit's retina. J Physiol 178, 477-504. Mauss, A.S., Vlasits, A., Borst, A., and Feller, M. (2017). Visual Circuits for Direction Selectivity. Annu Rev Neurosci 40, 211-230. Vaney, D.I., Sivyer, B., and Taylor, W.R. (2012). Direction selectivity in the retina: symmetry and asymmetry in structure and function. Nat Rev Neurosci 13, 194-208

Our laboratory seeks to understand how neural circuits receive, compute, encode and transmit information. More specifically, we’d like to learn what biophysical and morphological features equip synapses, neurons and networks to perform these tasks. The retina is a model system for the study of neuronal information processing: We can deliver precisely defined physiological stimuli and record responses from many different cell types at various points within the network; in addition, retinal circuitry is particularly well understood, enabling us to interpret more directly the impact of synaptic and cellular mechanisms on circuit function. I will present recent experiments in the lab that exploit these advantages to examine how synapses and neurons within retinal amacrine cell circuits perform specific visual computations.