Vision Science seminars

The research in my lab focuses on sensory signal processing, particularly in cases where sensory systems perform at or near the limits imposed by physics. Photon counting in the visual system is a beautiful example. At its peak sensitivity, the performance of the visual system is limited largely by the division of light into discrete photons. This observation has several implications for phototransduction and signal processing in the retina: rod photoreceptors must transduce single photon absorptions with high fidelity, single photon signals in photoreceptors, which are only 0.03 – 0.1 mV, must be reliably transmitted to second-order cells in the retina, and absorption of a single photon by a single rod must produce a noticeable change in the pattern of action potentials sent from the eye to the brain. My approach is to combine quantitative physiological experiments and theory to understand photon counting in terms of basic biophysical mechanisms. Fortunately there is more to visual perception than counting photons. The visual system is very adept at operating over a wide range of light intensities (about 12 orders of magnitude). Over most of this range, vision is mediated by cone photoreceptors. Thus adaptation is paramount to cone vision. Again one would like to understand quantitatively how the biophysical mechanisms involved in phototransduction, synaptic transmission, and neural coding contribute to adaptation.

Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In addition, we have shown that this bias is stable over periods of up to 8 s. At the end, I will show ongoing analysis of a recent experiment and argue that serial dependence may rely on distributed memory representations of stimuli and task relevant features.

Visual restoration is certainly the greatest challenge for brain-machine interfaces with the high pixel number and high refreshing rate. In the recent year, we brought retinal prostheses and optogenetic therapy up to successful clinical trials. Concerning visual restoration at the cortical level, prostheses have shown efficacy for limited periods of time and limited pixel numbers. We are investigating the potential of sonogenetics to develop a non-contact brain machine interface allowing long-lasting activation of the visual cortex. The presentation will introduce our genetic-based brain machine interfaces for visual restoration at the retinal and cortical levels.

Research in our lab focuses on the circuit mechanisms underlying sensory computation. We use the mouse retina as a model system because it allows us to stimulate the circuit precisely with its natural input, patterns of light, and record its natural output, the spike trains of retinal ganglion cells. We harness the power of genetic manipulations and detailed information about cell types to uncover new circuits and discover their role in visual processing. Our methods include electrophysiology, computational modeling, and circuit tracing using a variety of imaging techniques.

A central goal of vision science is to understand the principles underlying the perception and neural coding of the complex visual environment of our everyday experience. In the visual cortex, foundational work with artificial stimuli, and more recent work combining natural images and deep convolutional neural networks, have revealed much about the tuning of cortical neurons to specific image features. However, a major limitation of this existing work is its focus on single-neuron response strength to isolated images. First, during natural vision, the inputs to cortical neurons are not isolated but rather embedded in a rich spatial and temporal context. Second, the full structure of population activity—including the substantial trial-to-trial variability that is shared among neurons—determines encoded information and, ultimately, perception. In the first part of this talk, I will argue for a normative approach to study encoding of natural images in primary visual cortex (V1), which combines a detailed understanding of the sensory inputs with a theory of how those inputs should be represented. Specifically, we hypothesize that V1 response structure serves to approximate a probabilistic representation optimized to the statistics of natural visual inputs, and that contextual modulation is an integral aspect of achieving this goal. I will present a concrete computational framework that instantiates this hypothesis, and data recorded using multielectrode arrays in macaque V1 to test its predictions. In the second part, I will discuss how we are leveraging this framework to develop deep probabilistic algorithms for natural image and video segmentation.

My research uses electrophysiological techniques to evaluate normal retinal function, dysfunction caused by blinding retinal diseases and the restoration of function using a variety of therapeutic strategies. We can use our understanding or normal retinal function and disease-related changes to construct optimal therapeutic strategies and evaluate how they ameliorate the effects of disease. Retinitis pigmentosa (RP) is a family of blinding eye diseases caused by photoreceptor degeneration. The absence of the cells that for this primary signal leads to blindness. My interest in RP involves the evaluation of therapies to restore vision: replacing degenerated photoreceptors either with: (1) new stem or other embryonic cells, manipulated to become photoreceptors or (2) prosthetics devices that replace the photoreceptor signal with an electronic signal to light. Glaucoma is caused by increased intraocular pressure and leads to ganglion cell death, which eliminates the link between the retinal output and central visual processing. We are parsing out of the effects of increased intraocular pressure and aging on ganglion cells. Congenital Stationary Night Blindness (CSNB) is a family of diseases in which signaling is eliminated between rod photoreceptors and their postsynaptic targets, rod bipolar cells. This deafferents the retinal circuit that is responsible for vision under dim lighting. My interest in CSNB involves understanding the basic interplay between excitation and inhibition in the retinal circuit and its normal development. Because of the targeted nature of this disease, we are hopeful that a gene therapy approach can be developed to restore night vision. My work utilizes rodent disease models whose mutations mimic those found in human patients. While molecular manipulation of rodents is a fairly common approach, we have recently developed a mutant NIH miniature swine model of a common form of autosomal dominant RP (Pro23His rhodopsin mutation) in collaboration with the National Swine Resource Research Center at University of Missouri. More genetically modified mini-swine models are in the pipeline to examine other retinal diseases.

The brain and its inherent functions can be modified by various forms of learning. Learning-induced changes are seen even in basic perceptual functions. In particular, repeated training in a perceptual task can lead to a significant improvement in the trained task—a phenomenon known as perceptual learning. There has been a long-standing debate about the mechanisms of perceptual learning. In this talk, I will present results from our series of electrophysiological studies. These studies have consistently shown that perceptual learning is mediated by concerted changes in both perceptual and cognitive processes, resulting in improved sensory representation, enhanced top-down influences, and refined readout process.

How does the visual system of insects function in vastly different light intensities, process separate parts of the visual field in parallel, and cope with eye sizes that differ between individuals? This talk will give you the answers we receive from our unique(ly adorable) model system: hawkmoths.

How does the brain interpret signals from the outside world? Walking through a park, you might take for granted the ease with which you can understand what you see. Rather than seeing a series of still snapshots, you are able to see simple, fluid movement — of dogs running, squirrels foraging, or kids playing basketball. You can track their paths and know where they are headed without much thought. “How does this process take place?” asks Rudy Behnia, PhD, a principal investigator at Columbia’s Mortimer B. Zuckerman Mind Brain Behavior Institute. “For most of us, it’s hard to imagine a world where we can’t see motion, shapes, and color; where we can’t have a representation of the physical world in our head.” And yet this representation does not happen automatically — our brain has no direct connection with the outside world. Instead, it interprets information taken in by our senses. Dr. Behnia is studying how the brain builds these representations. As a starting point, she focuses on how we see motion

Sleep is crucial for the continuity and development of life. Sleep-related problems can alter brain function, and cause potentially severe psychological and behavioral consequences. However, the role of sleep in our mind and behavior is far from clear. In this talk, I will present our research on how sleep may play a role in visual perceptual learning (VPL) by using simultaneous magnetic resonance spectroscopy and polysomnography in human subjects. We measured the concentrations of neurotransmitters in the early visual areas during sleep and obtained the excitation/inhibition (E/I) ratio which represents the amount of plasticity in the visual system. We found that the E/I ratio significantly increased during NREM sleep while it decreased during REM sleep. The E/I ratio during NREM sleep was correlated with offline performance gains by sleep, while the E/I ratio during REM sleep was correlated with the amount of learning stabilization. These suggest that NREM sleep increases plasticity, while REM sleep decreases it to solidify once enhanced learning. NREM and REM sleep may play complementary roles, reflected by significantly different neurochemical processing, in VPL.

Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. By deploying an efficient coding argument, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. To test this experimentally, developed a simple, novel imaging system that is indispensable for studies at this scale. In agreement with our predictions, we could show that receptive fields of retinal ganglion cells change their shape along the dorsoventral axis, with a marked surround asymmetry at the visual horizon. 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.

Evolution has endowed primates, including humans, with a powerful visual system, seemingly providing us with a detailed perception of our surroundings. But in reality the underlying process is one of active filtering, enhancement and reshaping. For visual motion perception, the dorsal pathway in primate visual cortex and in particular area MT/V5 is considered to be of critical importance. Combining physiological and psychophysical approaches we have used the processing and perception of visual motion and area MT/V5 as a model for the interaction of sensory (bottom-up) signals with cognitive (top-down) modulatory influences that characterizes visual perception. Our findings document how this interaction enables visual cortex to actively generate a neural representation of the environment that combines the high-performance sensory periphery with selective modulatory influences for producing an “integrated saliency map’ of the environment.