Changes in ocular hemodynamics may be indicative of pathological conditions in the eye (e.g. glaucoma, age-related macular degeneration), but also elsewhere in the body (e.g. systemic hypertension, diabetes, neurodegenerative disorders). Thanks to its transparent fluids and structures that allow the light to go through, the eye offers a unique window on the circulation from large to small vessels, and from arteries to veins. Deciphering the causes that lead to changes in ocular hemodynamics in a specific individual could help prevent vision loss as well as aid in the diagnosis and management of diseases beyond the eye. In this talk, we will discuss how mathematical and computational modelling can help in this regard. We will focus on two main factors, namely blood pressure (BP), which drives the blood flow through the vessels, and intraocular pressure (IOP), which compresses the vessels and may impede the flow. Mechanism-driven models translates fundamental principles of physics and physiology into computable equations that allow for identification of cause-to-effect relationships among interplaying factors (e.g. BP, IOP, blood flow). While invaluable for causality, mechanism-driven models are often based on simplifying assumptions to make them tractable for analysis and simulation; however, this often brings into question their relevance beyond theoretical explorations. Data-driven models offer a natural remedy to address these short-comings. Data-driven methods may be supervised (based on labelled training data) or unsupervised (clustering and other data analytics) and they include models based on statistics, machine learning, deep learning and neural networks. Data-driven models naturally thrive on large datasets, making them scalable to a plethora of applications. While invaluable for scalability, data-driven models are often perceived as black- boxes, as their outcomes are difficult to explain in terms of fundamental principles of physics and physiology and this limits the delivery of actionable insights. The combination of mechanism-driven and data-driven models allows us to harness the advantages of both, as mechanism-driven models excel at interpretability but suffer from a lack of scalability, while data-driven models are excellent at scale but suffer in terms of generalizability and insights for hypothesis generation. This combined, integrative approach represents the pillar of the interdisciplinary approach to data science that will be discussed in this talk, with application to ocular hemodynamics and specific examples in glaucoma research.

A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of representations in neural circuits. In this talk, we will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from the structure in neural populations and from biologically plausible neural networks. First, we will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes a perceptron’s capacity for linearly classifying object categories based on the underlying neural manifolds’ structural properties. Next, we will describe how such methods can, in fact, open the ‘black box’ of distributed neuronal circuits in a range of experimental neural datasets. In particular, our method overcomes the limitations of traditional dimensionality reduction techniques, as it operates directly on the high-dimensional representations, rather than relying on low-dimensionality assumptions for visualization. Furthermore, this method allows for simultaneous multi-level analysis, by measuring geometric properties in neural population data, and estimating the amount of task information embedded in the same population. These geometric frameworks are general and can be used across different brain areas and task modalities, as demonstrated in the work of ours and others, ranging from the visual cortex to parietal cortex to hippocampus, and from calcium imaging to electrophysiology to fMRI datasets. Finally, we will discuss our recent efforts to fully extend this multi-level description of neural populations, by (1) investigating how single neuron properties shape the representation geometry in early sensory areas, and by (2) understanding how task-efficient neural manifolds emerge in biologically-constrained neural networks. By extending our mathematical toolkit for analyzing representations underlying complex neuronal networks, we hope to contribute to the long-term challenge of understanding the neuronal basis of tasks and behaviors.

A central goal in neuroscience is to understand how orchestrated computations in the brain arise from the properties of single neurons and networks of such neurons. Answering this question requires theoretical advances that shine light into the ‘black box’ of neuronal networks. In this talk, I will demonstrate theoretical approaches that help describe how cognitive and behavioral task implementations emerge from structure in neural populations and from biologically plausible learning rules. First, I will introduce an analytic theory that connects geometric structures that arise from neural responses (i.e., neural manifolds) to the neural population’s efficiency in implementing a task. In particular, this theory describes how easy or hard it is to discriminate between object categories based on the underlying neural manifolds’ structural properties. Next, I will describe how such methods can, in fact, open the ‘black box’ of neuronal networks, by showing how we can understand a) the role of network motifs in task implementation in neural networks and b) the role of neural noise in adversarial robustness in vision and audition. Finally, I will discuss my recent efforts to develop biologically plausible learning rules for neuronal networks, inspired by recent experimental findings in synaptic plasticity. By extending our mathematical toolkit for analyzing representations and learning rules underlying complex neuronal networks, I hope to contribute toward the long-term challenge of understanding the neuronal basis of behaviors.

Among mammals, excellent color vision has evolved only in certain non-human primates. And yet, color is often assumed to be just a low-level stimulus feature with a modest role in encoding and recognizing objects. The rationale for this dogma is compelling: object recognition is excellent in grayscale images (consider black-and-white movies, where faces, places, objects, and story are readily apparent). In my talk I will discuss experiments in which we used color as a tool to uncover an organizational plan in inferior temporal cortex (parallel, multistage processing for places, faces, colors, and objects) and a visual-stimulus functional representation in prefrontal cortex (PFC). The discovery of an extensive network of color-biased domains within IT and PFC, regions implicated in high-level object vision and executive functions, compels a re-evaluation of the role of color in behavior. I will discuss behavioral studies prompted by the neurobiology that uncover a universal principle for color categorization across languages, the first systematic study of the color statistics of objects and a chromatic mechanism by which the brain may compute animacy, and a surprising paradoxical impact of memory on face color. Taken together, my talk will put forward the argument that color is not primarily for object recognition, but rather for the assessment of the likely behavioral relevance, or meaning, of the stuff we see.

Investigations of the neural systems mediating the processing of social groups defined by race, specifically Black and White race groups in American participants, reveals significant overlap with brain mechanisms involved in emotion. This talk will provide an overview of research on the neuroscience of race and emotion, focusing on implicit race attitudes. Implicit race attitudes are expressed without conscious effort and control, and contrast with explicit, conscious attitudes. In spite of sharp decline in the expression of explicit, negative attitudes towards outgroup race members over the last half century, negative implicit attitudes persist, even in the face of strong egalitarian goals and beliefs. Early research demonstrated that implicit, but not explicit, negative attitudes towards outgroup race members correlate with blood oxygenation level dependent (BOLD) signal in the amygdala – a region implicated in threat representations, as well as emotion’s influence on cognition. Building on this initial finding, we demonstrate how learning and decisions may be modulated by implicit race attitudes and involve neural systems mediating emotion, learning and choice. Finally, we discuss techniques that may diminish the unintentional expression of negative, implicit race attitudes.

Black Americans experience diverse racism-related stressors throughout the lifespan. Disproportionately high trauma exposure, economic disadvantage, explicit racism and inequitable treatment are stressors faced by many Black Americans. These experiences have a cumulative negative impact on psychological and physical health. However, little is understood about how experiences of racism, such as discrimination, can mediate health outcomes via their effects on neurobiology. I will present clinical, behavioral, physiological and neurobiological data from Black American participants in the Grady Trauma Project, a longstanding study of trauma conducted in inner-city Atlanta. These data will be discussed in the context of both risk and resilience/adaptation perspectives. Finally, recommendations for future clinical neuroscience research and targets for intervention in marginalized populations will be discussed.

The ALBA Network and Black in Neuro are partnering to bring the Black neuroscientific community in Europe together. Are you a Black neuroscientist based in Europe? If so, join us for this casual online networking event. We will share our experience, stories and knowledge about what it is to be black in Europe while working in brain research. We will also discuss potential actions ALBA and BiN could take to provide better visibility to the community. This is a time to get to know each other, share, network and relate. Please register to receive the link to the zoom meeting.

Over the past 50 years, sociolinguistic studies on black Americans have expanded in both theoretical and technical scope, and newer research has moved beyond seeing speakers, especially black speakers, as a monolithic sociolinguistic community (Wolfram 2007, Blake 2014). Yet there remains a dearth of critical work on complex identities existing within black American communities as well as how these identities are reflected and perceived in linguistic practice. At the same time, linguists have begun to take greater interest in the ways in which public figures, such as politicians, may illuminate the wider social meaning of specific linguistic variables. In this talk, I will present results from analyses of multiple aspects of ethnolinguistic variation in the speech of Vice President Kamala Harris during the 2019-2020 Democratic Party Primary debates. Together, these results show how VP Harris expertly employs both enregistered and subtle linguistic variables, including aspects of African American Language morphosyntax, vowels, and intonational phonology in the construction and performance of a highly specific sociolinguistic identity that reflects her unique positions politically, socially, and racially. The results of this study expand our knowledge about how the complexities of speaker identity are reflected in sociolinguistic variation, as well as press on the boundaries of what we know about how speakers in the public sphere use variation to reflect both who they are and who we want them to be.

Visual processing in the retina has been studied in great detail at all levels such that a comprehensive picture of the retina's cell types and the many neural circuits they form is emerging. However, the currently best performing models of retinal function are black-box CNN models which are agnostic to such biological knowledge. Here, I present two of our recent attempts to develop computational models of processing in the inner retina, which both respect biophysical and anatomical constraints yet provide accurate predictions of retinal activity