Vision Science seminars

Animals are able to perceive self-motion and navigate in their environment using optic flow information. They often perform visually guided stabilization behaviors like the optokinetic (OKR) or optomotor response (OMR) in order to maintain their eye and body position relative to the moving surround. But how does the animal manage to perform appropriate behavioral response and how are processing tasks divided between the various non-cortical visual brain areas? Experiments have shown that the zebrafish pretectum, which is homologous to the mammalian accessory optic system, is involved in the OKR and OMR. The optic tectum (superior colliculus in mammals) is involved in processing of small stimuli, e.g. during prey capture. We have previously shown that many pretectal neurons respond selectively to rotational or translational motion. These neurons are likely detectors for specific optic flow patterns and mediate behavioral choices of the animal based on optic flow information. We investigate the motion feature extraction of brain structures that receive input from retinal ganglion cells to identify the visual computations that underlie behavioral decisions during prey capture, OKR, OMR and other visually mediate behaviors. Our study of receptive fields shows that receptive field sizes in pretectum (large) and tectum (small) are very different and that pretectal responses are diverse and anatomically organized. Since calcium indicators are slow and receptive fields for motion stimuli are difficult to measure, we also develop novel stimuli and statistical methods to infer the neuronal computations of visual brain areas.

Proper alignment of the circadian system the environmental light/dark cycle is central to human health and well-being, and occurs exclusively via light input from the melanopsin-expressing, intrinsically photosensitive retinal ganglion cells (ipRGCs). I will discuss our lab’s recent work uncovering a new inhibitory signaling pathway from the eye to the brain that dampens the sensitivity of our circadian and pupil systems to light.

How does a circuit of neurons process sensory information? And how are transformations of neural signals altered by changes in synaptic strength? We investigate these questions in the context of the visual system and the lateral line of fish. A distinguishing feature of our approach is the imaging of activity across populations of synapses – the fundamental elements of signal transfer within all brain circuits. A guiding hypothesis is that the plasticity of neurotransmission plays a major part in controlling the input-output relation of sensory circuits, regulating the tuning and sensitivity of neurons to allow adaptation or sensitization to particular features of the input. Sensory systems continuously adjust their input-output relation according to the recent history of the stimulus. A common alteration is a decrease in the gain of the response to a constant feature of the input, termed adaptation. For instance, in the retina, many of the ganglion cells (RGCs) providing the output produce their strongest responses just after the temporal contrast of the stimulus increases, but the response declines if this input is maintained. The advantage of adaptation is that it prevents saturation of the response to strong stimuli and allows for continued signaling of future increases in stimulus strength. But adaptation comes at a cost: a reduced sensitivity to a future decrease in stimulus strength. The retina compensates for this loss of information through an intriguing strategy: while some RGCs adapt following a strong stimulus, a second population gradually becomes sensitized. We found that the underlying circuit mechanisms involve two opposing forms of synaptic plasticity in bipolar cells: synaptic depression causes adaptation and facilitation causes sensitization. Facilitation is in turn caused by depression in inhibitory synapses providing negative feedback. These opposing forms of plasticity can cause simultaneous increases and decreases in contrast-sensitivity of different RGCs, which suggests a general framework for understanding the function of sensory circuits: plasticity of both excitatory and inhibitory synapses control dynamic changes in tuning and gain.

Cases with visuospatial abnormalities provide opportunities for understanding the underlying cognitive mechanisms. Three cases of visual mirror-reversal have been reported: AH (McCloskey, 2009), TM (McCloskey, Valtonen, & Sherman, 2006) and PR (Pflugshaupt et al., 2007). This research reports a fourth case, BS -- with focal occipital cortical dysgenesis -- who displays highly unusual visuospatial abnormalities. They initially produced mirror reversal errors similar to those of AH, who -- like the patient in question -- showed a selective developmental deficit. Extensive examination of BS revealed phenomena such as: mirror reversal errors (sometimes affecting only parts of the visual fields) in both horizontal and vertical planes; subjective representation of visual objects and words in distinct left and right visual fields; subjective duplication of objects of visual attention (not due to diplopia); uncertainty regarding the canonical upright orientation of everyday objects; mirror reversals during saccadic eye movements on oculomotor tasks; and failure to integrate visual with other sensory inputs (e.g., they feel themself moving backwards when visual information shows they are moving forward). Fewer errors are produced under conditions of certain visual variables. These and other findings have led the researchers to conclude that BS draws upon a subjective representation of visual space that is structured phenomenally much as it is anatomically in early visual cortex (i.e., rotated through 180 degrees, split into left and right fields, etc.). Despite this, BS functions remarkably well in their everyday life, apparently due to extensive compensatory mechanisms deployed at higher (executive) processing levels beyond the visual modality.

Organisms sense light for purposes that range from recognizing objects to synchronizing activity with environmental cycles. What mechanisms serve these diverse tasks? This seminar will examine the specializations of two cell types. First are the foveal cone photoreceptors. These neurons are used by primates to see far greater detail than other mammals, which lack them. How do the biophysical properties of foveal cones support high-acuity vision? Second are the melanopsin retinal ganglion cells, which are conserved among mammals and essential for processes that include regulation of the circadian clock, sleep, and hormone levels. How do these neurons encode light, and is encoding customized for animals of different niches? In pursuing these questions, a broad goal is to learn how various levels of biological organization are shaped to behavioural needs.

Over the past decade, the domain of face identity processing has seen a surging interest in inter-individual differences, with a focus on individuals with superior skills, so-called Super-Recognizers (SRs; Ramon et al., 2019; Russell et al., 2009). Their study can provide valuable insights into brain-behavior relationships and advance our understanding of neural functioning. Despite a decade of research, and similarly to the field of developmental prosopagnosia, a consensus on diagnostic criteria for SR identification is lacking. Consequently, SRs are currently identified either inconsistently, via suboptimal individual tests, or via undocumented collections of tests. This state of the field has two major implications. Firstly, our scientific understanding of SRs will remain at best limited. Secondly, the needs of government agencies interested in deploying SRs for real-life identity verification (e.g., policing) are unlikely to be met. To counteract these issues, I suggest the following action points. Firstly, based on our and others’ work suggesting novel and challenging tests of face cognition (Bobak et al., 2019; Fysh et al., in press; Stacchi et al., 2019), and my collaborations with international security agencies, I recommend novel diagnostic criteria for SR identification. These are currently being used to screen the Berlin State Police’s >25K employees before identifying SRs via bespoke testing procedures we have collaboratively developed over the past years. Secondly, I introduce a cohort of SRs identified using these criteria, which is being studied in-depth using behavioral methods, psychophysics, eye-tracking, and neuroimaging. Finally, I suggest data acquired for these individuals should be curated to develop and share best practices with researchers and practitioners, and to gain an accurate and transparent description of SR cases to exploit their informative value.

During the course of evolution, a species’ environment shapes its sensory abilities, as individuals with more optimized sensory abilities are more likely survive and procreate. Adaptations to the statistics of the natural environment can be observed along the early visual pathway and across species. Therefore, characterising the properties of natural environments and studying the representation of natural scenes along the visual pathway is crucial for advancing our understanding of the structure and function of the visual system. In the past 20 years, mice have become an important model in vision research, but the fact that they live in a different environment than primates and have different visual needs is rarely considered. One particular challenge for characterising the mouse’s visual environment is that they are dichromats with photoreceptors that detect UV light, which the typical camera does not record. This also has consequences for experimental visual stimulation, as the blue channel of computer screens fails to excite mouse UV cone photoreceptors. In my talk, I will describe our approach to recording “colour” footage of the habitat of mice – from the mouse’s perspective – and to studying retinal circuits in the ex vivo retina with natural movies.

Zebrafish and cichlids are popular models in visual neuroscience, due to their amenability to advanced research tools and their diverse set of visually guided behaviours. It is often asserted that animals’ neural systems are adapted to the statistical regularities in their natural environments, but relatively little is known about the visual spatiotemporal features in the underwater habitats that nurtured these fish. To address this gap, we have embarked on an examination of underwater habitats in northeastern India and Lake Tanganyika (Zambia), where zebrafish and cichlids are native. In this talk, we will describe the methods used to conduct a series of field measurements and generate a large and diverse dataset of these underwater habitats. We will present preliminary results suggesting that the demands for visually-guided navigation differ between these underwater habitats and the terrestrial habitats characteristic of other model species.

Learning depends on both the learning mechanism and the regularities in the training material, yet most research on human and machine learning focus on the discovering the mechanisms that underlie powerful learning. I will present evidence from our research focusing on the statistical structure of infant visual learning environments. The findings suggest that the statistical structure of those learning environments are not like those used in laboratory experiments on visual learning, in machine learning, or in our adult assumptions about how teach visual categories. The data derive from our use of head cameras and head-mounted eye trackers capturing FOV experiences in the home as well as in simulated home environments in the laboratory. The participants range from 1 month of age to 24 months. The observed statistical structure offers new insights into the developmental foundations of visual object recognition and suggest a computational rethinking of the problem of visual category formation. The observed environmental statistics also have direct implications for understanding the development of cortical visual systems.

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.