SURF Program Lab Opportunities

The SURF Program provides funds for undergraduate research assistants to work in SCGB-funded laboratories during the academic year under the mentorship of postdoctoral fellows or graduate students. This page lists available SCGB lab opportunities for the 2021 SURF RFA. Applicants may select and rank up to three opportunities of interest.

Bay Area, CA

  • Arnaldo Carreira-Rosario (Stanford, Clandinin Lab)plus--large

    Mentor: Arnaldo Carreira-Rosario, Postdoctoral Fellow
    Principal Investigator: Thomas R. Clandinin
    Institution: Stanford University, Stanford, CA

    Project: The central question of my research is how innate behaviors form. In contrast to learned behaviors such as riding a bike, a baby knows how to suckle and feed as soon as being born. Such innate behaviors occur intrinsically, without learning by observing another animal. It turns out that embryos practice innate behaviors before they are born. This practice occurs spontaneously and is generated by a spontaneous, yet patterned, neuronal network activity (PaSNA). I am using a powerful genetic model organism, the fruit fly, to study how PaSNA emerges and how it shapes innate behaviors. I live-image and manipulate neuronal activity in the embryo and assess how this impacts innate behaviors using behavioral assays and computational analyses.

    Biography: I grew up in Puerto Rico. As a teenager, I spent my time skateboarding and playing soccer. I obtained a bachelor’s degree in Industrial Biotechnology at the University of Puerto Rico. At college, I had my first research experience and decided to pursue a career in biological research. I went to UT-Southwestern in Texas to obtain a Ph.D. studying stem cell differentiation. I learned a bit about neuroscience in graduate school and got fascinated by neurodevelopment. Currently, I am particularly interested in how neuronal activity during nervous system development shapes circuit function and behavior for the entire life of the organism. My favorite aspect of research is to observe something in nature that no one else in humanity has seen before. Outside the lab, I continue to play soccer and like visiting friends and family around the U.S., Puerto Rico and Turkey.

  • Tyler Benster (Stanford, Druckmann Lab)plus--large

    Mentor: Tyler Benster, Graduate Student
    Principal Investigator: Shaul Druckmann
    Institution: Stanford University, Stanford, CA

    Project: Calcium imaging and optogenetics enable measuring the projective field of a neuron: the set of neurons that are activated by stimulating a neuron. The principles of how projective fields interact is central to both systems and computational neuroscience. This project will provide a personalized research experience for a motivated undergraduate fellow, who will choose between one of three questions:
    1) How do projective fields compose? We will compare how well different artificial neural networks explain an experimental dataset where we vary the number of neurons stimulated concurrently.
    2) Are correlations predictive of projective fields? We will do a correlation analysis to predict connectivity between neurons, and subsequently compare this prediction to the experimentally-measured projective-field.
    3) How do projective fields vary with the magnitude of neural response? We control the magnitude of neural response and compare how the projective field varies.

    Biography: Tyler Benster received his Sc.B. from Brown University in Applied Mathematics-Economics and is now a fourth year Ph.D. candidate in the neurosciences program at Stanford University, co-advised by Shaul Druckmann and Karl Deisseroth. He created a new transgenic zebrafish that co-expresses a green calcium indicator and a red channelrhodopsin across the entire brain. Tyler uses two photon microscopy and holographic optogenetic stimulation for brain-wide observation and control of neural activity with cellular resolution. Tyler collected preliminary data to begin answering one of the above questions. Because we have the capability to close the loop between theory and experiments, work during the first half of the SCCB fellowship may inform collection of a new dataset for analysis in the second half of the fellowship. Tyler and Shaul are excited to collaborate with an SURF Fellow, and are committed to providing both the mentorship and experimental data to support an exciting scientific experience.

  • Alexander Fanning (Stanford, Raymond Lab)plus--large

    Mentor: Alexander Fanning, Postdoctoral Fellow
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University, Stanford, CA

    Project: A key function of the brain is to make predictions that can guide behavior. Dr. Fanning has developed a set of behavioral paradigms in mice for distinguishing different kinds of predictions—predictions about external events in the world and predictions about the consequences of one’s own actions. The SURF fellow will work closely with Dr. Fanning to record and manipulate neural activity during these behaviors, to analyze how different kinds of predictions are generated by neural circuits, and how prediction errors are used to guide learning.

    Biography: Alex Fanning is a postdoctoral fellow in Dr. Jennifer Raymond’s lab. He received his Bachelor of Science in psychology at the University of Iowa, while he worked as a research assistant studying state-dependent sensory and motor processing. He received his Doctor of Philosophy degree at the University of Texas at Austin where he studied the physiology of prediction-error signaling in the cerebellum. As a postdoctoral fellow, he has continued his studies of how predictions and prediction errors are computed by the cerebellum and related brain structures. Alex feels a deep sense of commitment to getting people relatively new to basic research the exposure and training they need to continue on a trajectory of being a scientist or to just have a better understanding of how they interact with the STEM environment. He is happy to help someone along as he was once helped when he was an undergraduate.

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  • Sriram Jayabal (Stanford, Raymond Lab)plus--large

    Mentor: Sriram Jayabal, Postdoctoral Fellow
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University, Stanford, CA

    Project: Identifying the neurobiological underpinnings of meta-learning. Meta-learning, an old concept in psychology and a current hot topic in machine learning, is the improvement of learning with experience. Our previous experience learning a skill or set of skills makes us better at learning another, related skill. For instance, an accomplished athlete will learn a new sport faster than someone without the same experience learning similar athletic skills. How the brain accomplishes this powerful process of learning to learn has been an enigma. However, our lab has discovered a new property of synapses, which seems to provide at least part of the neural mechanism. The SURF fellow will contribute to our analysis of whether and how this new property of synapses supports meta-learning in mice. A motivated fellow will have the opportunity to contribute to all phases of the research, including data collection and analysis, the design and conduct of follow-up experiments, and oral and written communication of results.

    Biography: Sriram Jayabal is a postdoctoral fellow in the laboratory of Professor Jennifer Raymond in the Department of Neurobiology at the Stanford University School of Medicine. He received his Bachelor of Technology in biotechnology degree in India and his Ph.D. in neuroscience from McGill University in Canada. At McGill, he studied the cellular pathophysiology of spinocerebellar ataxia (impaired coordination of movements) under the supervision of Dr. Alanna Watt. Fascinated with the cerebellum, an analytically tractable brain area that supports cognition as well as movement, he joined the Raymond lab at Stanford to study how cerebellum implements learning. His focus is the neurobiological underpinnings of meta-learning, the ability to learn to learn. His pioneering work on this topic integrates molecular, cellular, and systems-level experimental approaches, and the computational analyses of SCGB collaborators.

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  • Trace Stay (Stanford, Raymond Lab)plus--large

    Mentor: Trace Stay, Postdoctoral Fellow
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University, Stanford, CA

    Project: Learning induces changes in the brain that encode memories. These neural memory traces are transformed over time; hence, a brain area that is essential for the expression of a recently acquired memory may not be required at later times post-training. This well-known phenomenon of memory consolidation suggests that the memory trace is transferred from one brain area to another. This process is thought to be driven by signals sent out from the early-learning brain area during the post-training period, which drive secondary changes in the late-learning brain area. The SURF fellow will work closely with Dr. Stay to analyze the nature of the signals exchanged between the early- and late-learning areas post-training, and the principles (plasticity rules) governing the memory transfer process.

    Biography: Trace Stay did his Ph.D. at Baylor College of Medicine in Houston, TX with Roy Sillitoe and SCGB investigator Dora Angelaki. His dissertation examined how the brain deals with Einstein’s equivalence principle of gravity and acceleration. Utilizing conditional mouse genetics, in vivo electrophysiology, immunohistochemistry, and computational analysis, he found that a brain area called the cerebellar uvula-nodulus distinguishes linear accelerations of the head from head tilts relative to gravity by integrating multiple sensory cues. Dr. Stay’s postdoctoral research at Stanford analyzes the broader neural networks that process the vestibular signals encoding orientation and motion of the head and use those signals to guide body movements. He is using large-scale neural recordings to analyze how these networks adapt their computations in response to changes in the environment, with the goal of developing a comprehensive model of how multiple information sources are used to regulate behavior.

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Boston, MA

  • Leenoy Meshulam (MIT, Fiete Lab)plus--large

    Mentor: Leenoy Meshulam, Postdoctoral Fellow
    Principal Investigator: Ila Fiete, International Brain Laboratory
    Institution: Massachusetts Institute of Technology, Cambridge, MA

    Project: Using complex network theory to understand real brain networks. Every decision, action or thought we make involves millions of neurons rapidly interacting with each other all across the brain. How this incredibly complex network of communication gives rise to our behavior is still largely unknown. In this project, you will be joining the International Brain Laboratory (IBL): an international consortium of neuroscientists investigating how the brain executes decisions. Using tools from complex network theory such as community detection we will be analyzing hundreds of recordings of neural data from 22 laboratories across the world. Together we will investigate how different network elements in the brain facilitate information flow across regions; how network organization changes during a decision-making process; and what is the role of different spatial and temporal scales in creating meaningful sub-networks. Ideal for fellows interested in complex networks/computational neuroscience/big-data based analysis. This is a purely computational project and requires prior experience coding in Python.

    Biography: Leenoy Meshulam is a Swartz postdoctoral fellow in theoretical neuroscience at the University of Washington. Her primary research topic is how function and computation emerge from the coordinated activity of large neuronal populations. Additionally, she uses analytical and computational methods to study how anatomical connections constrain network interactions. She draws on theoretical frameworks from statistical physics and dynamical systems to uncover principles of brain function. Meshulam joined the International Brain Laboratory in 2019 while working in Ila Fiete’s lab at MIT and has been associate-chair of the theory group of the collaboration ever since. Prior to that Leenoy received her Ph.D. from Princeton University, after completing her masters in physics and biology at Tel Aviv University in Israel. She is a committed mentor who will provide close guidance and support, as well as tailor the project direction according to the personal interests of the fellow.

    Note: This is a remote mentorship opportunity. A SURF Fellow matched with this lab would work in the Fiete lab at MIT in Cambridge while Meshulam mentors from UW in Seattle.

  • Hansem Sohn (MIT, Jazayeri Lab)plus--large

    Mentor: Hansem Sohn, Postdoctoral Fellow
    Principal Investigator: Mehrdad Jazayeri
    Institution: Massachusetts Institute of Technology, Cambridge, MA

    Project: Mental simulation is one of the core cognitive functions that subserve decision making and planning across a variety of tasks. However, it has been difficult to tap into its underlying neural mechanisms due to its latent nature and lack of dynamic behavioral readout. In this project, we devised an interval timing task where subjects are asked to precisely respond when a ball reaches a target position in a maze. Crucially, we will monitor eye movements of subjects as a window into their mental simulations while the ball becomes invisible in the maze. This project currently involves human behavioral experiments and normative probabilistic modeling (e.g., Bayesian). It will be potentially extended to non-human primate electrophysiology and computational modeling with artificial neural networks, providing fellows with ample opportunities to experience diverse approaches.

    Biography: I obtained my Ph.D. in neuroscience in South Korea, focusing on human vision and neuroimaging. I am currently a postdoctoral researcher at MIT, studying how the frontal cortex incorporates prior knowledge to optimize behaviors using electrophysiology in primates. My work broadly lies at the intersection of computational, cognitive and systems neuroscience. I have scientific and technical expertise in non-human primate electrophysiology, human EEG/fMRI and computational modeling of behavioral and neural data. I started my scientific career as an undergraduate participating in a project that analyzed EEG of psychiatric patients during cognitive tasks. I was fortunate to have the opportunity and now want to provide the same exciting research experience to fellows. Having experienced multiple disciplines — engineering, psychology and neuroscience — and different cultures as a first-generation immigrant, I endeavor to be a mentor who draws upon diverse experiences and provides multifaceted perspectives to fellows.

  • Mahdi Ramadan (MIT, Jazayeri Lab)plus--large

    Mentor: Mahdi Ramadan, Graduate Student
    Principal Investigator: Mehrdad Jazayeri
    Institution: Massachusetts Institute of Technology, Cambridge, MA

    Project: Hierarchical and counterfactual reasoning. Imagine a doctor thinking of a hierarchy of if-then scenarios to decide which course of action to take for a patient, and needing to make flexible adjustments to their decisions on the fly. How does the brain achieve this level of flexibility in complex decision settings? To investigate this question, this interdisciplinary research project uses a combination of human psychophysics, monkey electrophysiology and computational modeling tools to answer this question. Specifically, we study intuitive physics tasks that involve deliberating between multiple possibilities. Our results show humans and monkeys use an intriguing mental strategy wherein subjects parcel the problem into more manageable decisions, and use counterfactual reasoning to flexibly deliberate between them. Next steps in the project are analyzing brain signals related to counterfactual reasoning, training artificial networks to solve these tasks and modeling subject eye movements to reveal dynamic cognitive processes.

    Biography: I studied a combination of neurobiology, computer science and neural engineering for my undergraduate education at the University of Washington. Here at MIT, I am working on a project at the intersection of neuroscience and cognitive science. I strongly believe that by understanding fundamental principles of the brain we are better able to engineer better intelligent systems, approach medicine and neuro-technology in a more informed manner, and achieve a personal and philosophical satisfaction towards understanding the human mind. I hope to work with someone who is also just as excited as I am in bridging between cognitive behavior and neural implementation. I am also interested in various topics outside of research, including advocating for under-representated populations in education and innovation, philosophy, entrepreneurship, language learning and dance.

  • Rishi Rajalingham (MIT, Jazayeri Lab)plus--large

    Mentor: Rishi Rajalingham, Postdoctoral Fellow
    Principal Investigator: Mehrdad Jazayeri
    Institution: Massachusetts Institute of Technology, Cambridge, MA

    Project: From a few glances of a visual scene, primates can make many rich physical inferences, such as how heavy an object is, if/where it might fall, how to pick it up, etc.. Such inferences are thought to be supported by “mental simulations” of internal models, but it remains unclear if/how the brain might implement such computations. To study this, we trained macaque monkeys on a simple video game where the goal is to intercept a moving ball in the face of occlusion. We then recorded from thousands of neurons in a candidate area: the dorsomedial frontal cortex (DMFC). Our results suggest that monkeys “mentally simulate” the ball, and that DMFC activity reflects this simulation. The proposed project would use sophisticated population analyses, including recurrent neural network models, to investigate how this neural representation is distributed over DMFC, along both spatial (topographic and columnar organization) and temporal (neural dynamics) dimensions.

    Biography: I am a postdoctoral fellow in the Jazayeri lab at MIT, where I study how the brain supports flexible generalization abilities, specifically related to physical inference abilities using neurophysiology in monkeys, behavior, and computational (neural network) models. Prior to this, I received my Ph.D. from MIT; my Ph.D. work focused on the neural basis of visual object recognition. I used pharmacological and optogenetic perturbation methods to test the causal role of the primate ventral stream in object recognition abilities, as well behavioral benchmarks to compare object recognition behavior between deep neural networks and primates. My undergraduate training is in electrical engineering and I am excited by the confluence of quantitative models and benchmarks with rich neurophysiological measurements.

  • Sujaya Neupane (MIT, Jazayeri Lab)plus--large

    Mentor: Sujaya Neupane, Postdoctoral Fellow
    Principal Investigator: Mehrdad Jazayeri
    Institution: Massachusetts Institute of Technology, Cambridge, MA

    Project: Humans and animals can flexibly infer the vectors connecting any two points in their spatial environment. It is thought that neural representation of space code underlies such relational inference, but what computations enable the inference behavior is not known. We have designed a task to investigate the neural basis of mental computation when subjects mentally navigate from one point of a map to another. To test our computational hypotheses, we use normative models to characterize the behavior of humans, monkeys and recurrent neural networks trained to do the task; we also analyze electrophysiological signals recorded from multiple brain areas of monkeys engaged in the task. Potential projects for the candidate include – (i) simulating alternate models of neural representation of a map to make predictions of behavior (ii) implementing unsupervised sequence detection algorithms on primate hippocampal data to test the hypothesis of mental simulation.

    Biography: Dr. Sujaya Neupane is a postdoctoral fellow in the Jazayeri lab at MIT interested in understanding the neural basis of learning cognitive behavior. He obtained his Ph.D. from McGill University under the mentorship of Christopher Pack and Daniel Guitton, studying the effects of eye movement in visual cortical neurons. In his Ph.D. work, Sujaya showed eye-movement-related transient changes in neural connectivity and correctly predicted that subjects over-trained to make a particular eye movement demonstrate perceptual effects of training during fixation. In his postdoctoral work, in order to enquire how learned skills and concepts are stored and used flexibly, he and Dr. Jazayeri have developed a cognitive task which requires subjects to establish long term memory of a sequence of images and make inferences based on this memorized 1D map. Using computational approaches and neurophysiological recordings from primate medial temporal lobe and posterior parietal cortex, they are testing the computational hypotheses of mental simulation for inference and planning.

Chicago, IL

  • Matthew Getz (UChicago, Doiron Lab)plus--large

    Mentor: Matthew Getz, Graduate Student
    Principal Investigator: Brent Doiron
    Institution: University of Chicago, Chicago, IL

    Project: Cortical circuits form the building blocks of higher cognitive function. Decades of work have revealed a remarkable diversity of cell types embedded within these circuits, which display unique response properties across cognitive states. However, the role of this diversity in shaping neural response dynamics and information flow across circuits is still not well understood. Using computer simulations and mathematical analysis, we propose to study problems related to these questions; namely, how is information flow between neural circuits affected by the modulation of these circuits by cognitive processes and how does neural diversity factor into this process? This project would best suit a mathematically mature (working knowledge of linear algebra) fellow with programming experience (e.g. python, julia, Matlab). Additionally, this project would involve collaboration with Gregory Handy, a postdoctoral researcher in the Doiron group at the University of Chicago.

    Biography: Matt is a fourth year graduate student in the Center for Neuroscience at the University of Pittsburgh completing his Ph.D. research at the University of Chicago with Professor Brent Doiron. Prior to joining the Doiron lab at Pitt before its move to Chicago, Matt received a Master’s in Mathematics at the City College of New York where he worked with ProfessorAsohan Amarasingham on problems related to the statistical analysis of neural data. His graduate work is focused on understanding how cognitive states such as attention affect the dynamics within and information flow through neural circuits.

London, United Kingdom

  • Liang Zhou (UCL, Latham Lab)plus--large

    Mentor: Liang Zhou, Graduate Student
    Principal Investigator: Peter Latham
    Institution: University College London, London, United Kingdom

    Project: Humans can efficiently learn to solve a wide range of tasks. A popular approach to model task learning is to train deep recurrent neural networks (RNNs), with the hope that network activity is similar to that of real neurons and real behavior. However, this typically 1) requires lots of data; 2) doesn’t work well with multiple tasks and 3) is biologically implausible. Our goal is to fix these issues by selectively learning the input and output representations of a randomized chaotic RNN, which allows it to flexibly perform an array of neuroscientifically-interesting tasks. Importantly, we’ll also look at our new-and-improved model from a dynamical systems perspective, with the aim of understanding how it reconfigures inherently chaotic dynamics across a variety of computational goals. Within this project, there are multiple avenues for both theoretical and practical extensions, so you’ll have plenty of flexibility and opportunities for a publication.

    Biography: Liang is a Ph.D. student at the Gatsby Computational Neuroscience Unit at University College London. He double majored in neuroscience and computer science at MIT, where he worked on cognitive models of responsibility in intuitive physics. He then hopped across the pond to the UK on a Marshall Scholarship, where he has made no progress on a British accent but has discovered that he likes tea. Liang grew up in southern California and misses the sun dearly in London. In his free time, he enjoys running, dance, dark chocolate, and reading (Reddit, but also real books sometimes).

  • Roman Pogodin (UCL, Latham Lab)plus--large

    Mentor: Roman Pogodin, Graduate Student
    Principal Investigator: Peter Latham
    Institution: University College London, London, United Kingdom

    Project: Inspired by the success of deep learning, neuroscientists have proposed a variety of “biologically plausible” learning rules, which could be implemented with real neurons. However, these studies typically use neuron models that are so oversimplified that they actually lack biological plausibility. We want to study more realistic neuron models (e.g., by making each neuron either inhibitory or excitatory, but never both). This project involves theory: How do neuron models change update equations for each learning rule? Does the rule need additional circuitry to work in real neurons? And also simulations: How does performance on deep learning benchmarks depend on how realistic the neurons are? Are some rules more sensitive to model change than others? A prospective fellow would study the modern literature on biologically plausible deep learning and run experiments in PyTorch (no prior knowledge of PyTorch or Python is needed, but some programming experience is required).

    Biography: Roman Pogodin is a 4th year Ph.D. student in theoretical neuroscience with Peter E. Latham at Gatsby Computational Neuroscience Unit. He studies biologically plausible deep learning. In the past, he worked on models of working memory, learning in motor circuits, and multi-armed bandit algorithms. He earned a bachelor’s degree in applied mathematics and physics from Moscow Institute of Physics and Technology in Russia.

  • Will Dorrell (UCL, Latham Lab)plus--large

    Mentor: Will Dorrell, Graduate Student
    Principal Investigator: Peter Latham
    Institution: University College London, London, United Kingdom

    Project: Sensory systems are marvels, turning a sea of noisy inputs into useful knowledge. Olfaction is no exception, enabling navigation and memory recall from only the soup of ambient volatile chemicals. This project will explore consequences of a recent experimental pre-print. Olfaction has long been thought unique among sensory systems due to its lack of structure — higher-order olfactory neurons randomly sample from sensory input neurons. However, recent electron microscopy work has mapped the connectivity of the fly olfactory system and found something previously overlooked: certain input regions are oversampled vs random. This project will use mathematical and computational approaches to ask, ‘what role does this new-found structure play?’ More broadly, findings could be relevant to processing high-dimensional data (previous olfactory research has inspired nearest-neighbour search algorithms). Further, this project could lead to novel, and much needed, uses of connectivity data.

    Biography: I’m Will, a Ph.D. student at the Gatsby Computational Neuroscience Unit in London, currently working on biological structure learning and neural networks. I was a physics undergraduate at Cambridge University, where I did obnoxious things like running a weekly discussion group. I then moved to Harvard University, initially as a condensed matter physicist working on metamaterials, before seeing the light and becoming a theoretical neuroscientist working with Professor Cengiz Pehlevan on olfaction. Finally, thanks to a surprising burst of foresight, I spent much of the pandemic on the subtropical island of Okinawa working with Professor Erik de Schutter on bio-plausible hierarchical reinforcement learning. I am originally from Worcester, a small city in the West of England, famous for its difficult to pronounce name and eponymous sauce — which is produced less than a mile from my childhood bedroom. In my free time, I enjoy walking, talking and writing small summaries of my achievements.

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  • Grace Lindsay (UCL, Sahani Lab)plus--large

    Mentor: Grace Lindsay, Postdoctoral Fellow
    Principal Investigator: Maneesh Sahani
    Institution: University College London, London, United Kingdom

    Project: Convolutional neural networks are models used to understand visual processing in the brain. Much of their success has been in predicting neural activity and behavior in primates. Yet, in their current form they have struggled to capture processing in the mouse visual system. We aim to build an artificial neural network model that can capture the different processing streams in the mouse visual system. This will involve first defining the different tasks that different secondary visual areas in the mouse visual system perform and identifying image datasets that represent these tasks. We will then train a model with an architecture inspired by the multiple secondary areas of the mouse visual system on these different tasks and compare the learned activity to neural data. Finally, we hope to analyze the trained network to gain some understanding of how it works.

    Biography: Grace Lindsay is a joint Sainsbury Wellcome Centre/Gatsby Research Fellow at University College London working in the labs of Maneesh Sahani and Tom Mrsic-Flogel/Sonja Hofer. She got her Ph.D. from Columbia University in the Center for Theoretical Neuroscience. Working in the lab of Ken Miller, she built models of the neural and behavioral correlates of attention. Prior to that she spent a year as a research fellow at the Bernstein Center for Computational Neuroscience in Freiburg, Germany. Before that she got a B.S. in neuroscience from the University of Pittsburgh. She is currently working on building models of recurrent visual processing.

  • Joaquín Rapela & Mitra Javadzadeh No (UCL, Sahani & Hofer Labs)plus--large

    Mentor: Joaquín Rapela, Postdoctoral Fellow & Mitra Javadzadeh No, Graduate Student
    Principal Investigators: Maneesh Sahani & Sonja Hofer, Gatsby Computational Neuroscience Unit
    Institution: University College London, London, United Kingdom

    Project: Characterization of long-range cortical communication using optogenetic manipulations and advanced statistical models. The joint computational and experimental mentors of this proposal investigate together how populations of neurons in different visual areas of the mouse brain directly influence the activity of each other. For this purpose, we are using and developing state-of-the-art experimental (e.g., optogenetic manipulations and multi-area population electrophysiological recordings) and statistical (e.g., latent linear dynamical models) methods. We can now precisely quantify how visual areas influence the activity of each other, which has opened many new research questions.

    Biography: Joaquín received his undergraduate degree in computer science and Ph.D. in signal processing from the University of Southern California. He did postdoctoral training at University of California San Diego and at Brown University. He develops and applies advanced statistical methods to understand the function of the brain. Currently, he is a research engineer fellow at the Gatsby Computational Neuroscience Unit, where he distributes advanced statistical methods developed at the unit and builds collaborative projects with the Sainsbury Wellcome Center.

    Mitra completed her undergraduate studies in electrical engineering, and currently she is pursuing her Ph.D. in neuroscience at the Sainsbury Wellcome Center. She studies the long range communication between cortical visual areas and how visual processing is shaped by the dynamic flow of information between these areas.

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New York, NY

  • Jack Lindsey (Columbia, Litwin-Kumar & Abbott Labs)plus--large

    Mentor: Jack Lindsey, Graduate Student
    Principal Investigators: Ashok Litwin-Kumar & Larry Abbott
    Institution: Columbia University, New York, NY

    Project: Recent advances in connectomics have enabled the construction of a nearly complete map of neuronal connectivity in the fly brain, containing over twenty million synapses. The unprecedented scale and detail of this data presents a computational challenge: How can we make sense of this neural circuitry and understand its function? In this project, the fellow will work on developing and applying algorithms to identify functionally important pathways and hubs in the connectome data. We are particularly interested in the mushroom body, a key site of learning and memory in the fly brain. Preliminary analysis indicates that dopaminergic neurons (DANs) in the mushroom body, which modulate synaptic plasticity, receive highly diverse inputs from across the brain. Untangling the pathways that drive DAN activity will allow us to make predictions about how flies learn, and may reveal parallels (or inspire extensions) to modern reinforcement learning algorithms.

    Biography: Hi! I’m Jack, a second-year Ph.D. student in computational neuroscience at Columbia. I did my undergrad at Stanford University in math and computer science. I’m interested in understanding how neural representations support complex behaviors and how these capabilities are learned. Some projects I’m working on include: (1) modeling how learned associations and skills are consolidated into long-term memory; (2) understanding the role that heterogeneous dopamine activity plays in reinforcement learning; and (3) exploring how recurrent dynamics in sensory representations affect associative learning. I am applying these modeling ideas to particular learning systems in insects (e.g. the mushroom body) and mammals (e.g. striatum). I also like thinking about how modern machine learning (ML) approaches inform our understanding of these systems, and conversely how neural systems may suggest improvements to ML algorithms. Outside of research, I enjoy tennis, playing guitar, reading, eating cookies and making puns.

  • Kaushik Lakshminarasimhan (Columbia, Litwin-Kumar & Abbott Labs)plus--large

    Mentor: Kaushik Lakshminarasimhan, Postdoctoral Fellow
    Principal Investigators: Ashok Litwin-Kumar & Larry Abbott
    Institution: Columbia University, New York, NY

    Project: We can learn a lot about how the brain works by analyzing its failures. One such failure is characterized by systematic errors in sensory perception, a phenomenon called perceptual bias. An everyday example of perceptual bias is an optical illusion, but there are many more that have been carefully documented over the years using psychophysics experiments. We have a good high-level understanding of why our perceptions are typically distorted, thanks to extensive research into building psychological models of perception. However, we do not completely understand the underlying neural basis. Existing neural models are either too simple to recapitulate known biases, or too complex to give us a proper handle on the mechanisms. In this project, we will attempt to build a mathematical model of the visual cortex that strikes a balance between the two extremes, using tools from neural networks and dynamical systems.

    Biography: Kaushik Lakshminarasimhan is currently a postdoctoral researcher in the Center for Theoretical Neuroscience at Columbia University. Prior to this, he received his Ph.D. in neuroscience from the Baylor College of Medicine in Houston, where he built behavioral models and investigated neural computations underlying sensorimotor transformations during naturalistic behaviors like spatial navigation. His current research focus is on understanding how the neocortex learns, and how the thalamus contributes to this process. In the long term, he hopes to better understand how normative solutions to computational problems faced by the brain are approximately realized in neural circuits. He has mentored undergrads in the past, and most recently served as a teaching assistant to a group of graduate students at the Neuromatch Academy 2020.

  • Joshua Glaser (Columbia, Paninski Lab)plus--large

    Mentor: Joshua Glaser, Postdoctoral Fellow
    Principal Investigator: Liam Paninski
    Institution: Columbia University, New York, NY

    Project: Developing neural “decoders” (mappings from neural activity to motor output) that are accurate across many types of movements is a central challenge in neural engineering and motor control. Overcoming this challenge is of great importance to brain computer interfaces (BCIs), which could help restore movement to those with spinal cord injuries, stroke, and more. For BCIs to be widely applicable, it is essential that they can “generalize” to new movements that the BCI decoder was not explicitly trained on. For example, if we train a decoder using slow movements, we would still want it to work when making fast movements. In this project, we will work to develop machine learning decoders that are better able to generalize to new conditions. To do so, we will fit decoding models to collaborators’ data that includes simultaneous neural and muscle recordings. The ability to code in Python will be beneficial for this project.

    Biography: In my research, I am excited about developing machine learning tools specifically designed to solve problems within neuroscience. I am currently a postdoctoral researcher at Columbia University in the Center for Theoretical Neuroscience, working with Liam Paninski and John Cunningham. I completed my Ph.D. in neuroscience at Northwestern University in the lab of Konrad Kording. Before that, I was an undergrad at the University of Illinois Urbana-Champaign, studying physics and math. I look forward to helping mentor the next generation of computational neuroscientists.

  • Tom Hindmarsh Sten (Rockefeller, Ruta Lab)plus--large

    Mentor: Tom Hindmarsh Sten, Graduate Student
    Principal Investigator: Vanessa Ruta
    Institution: The Rockefeller Institute, New York City, NY

    Project: State-dependent circuits for flexible behavior in Drosophila. Internal arousal states motivate distinct patterns of behavior over short and long timescales, allowing for the temporary emergence of innate behavioral programs like fighting, feeding and mating that subserve the needs of an animal. In this project, we will take advantage of the rich behavioral repertoire and genetic tractability of Drosophila to investigate how internal states regulate information flow in the nervous system to shape an animal’ s ongoing behavior. In natural social settings, male flies alter between performing courtship displays towards females and aggressively fighting competing males. The fellow will contribute to studies of how changes in a male fly’s state of sexual or aggressive arousal trigger distinct behavioral responses to the visual profile of another fly. The fellow will work with the mentor to develop tools for analysis of quantitative behavioral data, examine correlations between ongoing neural activity and behavior, and design neural circuit models.

    Biography: I grew up in the countryside of southern Sweden, and moved to the United States as a high school junior. I completed my undergraduate education at New York University, and had originally intended to become a physician. As an undergraduate, I worked to develop computational models of how animals acquire and utilize knowledge in the laboratory of Robert Froemke — an experience that caused me to change my professional course from medicine to neuroscience. I am currently a fourth-year graduate student in Vanessa Ruta’s laboratory at The Rockefeller University, where I work at the interface between computational and experimental neuroscience to investigate how animals can flexibly change the way they interact with the world depending on their homeostatic and reproductive needs. Outside of the laboratory, I am an avid cook, fiction reader and political junkie.

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Pittsburgh, PA

  • Cheng Xue (UPittsburgh, Cohen Lab)plus--large

    Mentor: Cheng Xue, Postdoctoral Fellow
    Principal Investigator: Marlene Cohen
    Institution: University of Pittsburgh, Pittsburgh, PA

    Project: Classic studies on decision making have specifically designed experiments where the subjects were well-trained or clearly instructed to solve one single task. But what if, as we often find ourselves in real life scenarios, we also need to infer which task is relevant in a situation, and be ready to solve other tasks when they become relevant in a changed environment? Our preliminary data from monkeys implies that the flexibility to switch between tasks comes at a cost of worse perceptual decisions. In this project, we will systematically study this flexibility-accuracy trade-off in belief-based decision making with online human psychophysics experiments, and look for potential neuronal mechanisms using existing monkey electrophysiological data. The fellow will have opportunities to participate in creative designs of close-looped behavioral experiments to test our hypothesis, to build normative behavioral models to understand behavioral mechanisms, and to analyze high dimensional neuronal data in relation with behavior.

    Biography: I am interested in decision-making behavior in a broad sense. During my study and training I have utilized multifaceted approaches including spiking neuronal network simulation, behaving monkey electrophysiology, and human psychophysics to study perceptual decisions in various aspects. Through these research experiences, I have gradually developed a keen interest on the apparent suboptimal behaviors in decision making. Many of them have cognitive origins, and therefore can potentially become a window to reveal important neuronal constraints in perception or decision making. I believe research in this direction will inspire new treatment for various neurological disorders. Additionally, understanding how and why our brains do not produce the most accurate perception or the best judgements in some situations will lead to better awareness of our flaws and become better decision-makers.

  • Ramanujan Srinath (UPittsburgh, Cohen Lab)plus--large

    Mentor: Ramanujan Srinath, Postdoctoral Fellow
    Principal Investigator: Marlene Cohen
    Institution: University of Pittsburgh, Pittsburgh, PA

    Project: Supervised and unsupervised shifts in learned correlations between stimulus parameters in humans and monkeys. Our brain is tasked with making sense of the ever-changing visual information about objects in our environments. In this project, we will study how two visual properties of objects can be learned together and how behavior would change if the learned associations between these properties changes. We are currently training monkeys to do a task where the monkey is reporting the curvature of an object on a continuous scale. We are also doing online human psychophysics experiments using the same task to assess how humans would solve the problem with and without explicit instructions about the shifts in associations. The undergraduate researcher will be involved in both aspects of this exciting project which would include hands-on training with custom software, human experiments and behavioral data analysis.

    Biography: Ram is interested in how we see and interact with objects in the world. In his Ph.D., he studied how the brain quickly transforms image information from the retina to decodable information about the geometry of 3D objects. He is an experienced electrophysiologist, optophysiologist and programmer, and is interested in multifaceted projects that can answer fundamental questions about how we make inferences about and physically interact with our environment. He is currently training monkeys to do a version of the task described above and gearing up to start multi-channel, multi-area recordings in the brain. He is also building deep network models that can do the task as well as (or better than) humans.

Princeton, NJ

  • Yoel Sanchez Araujo (Princeton, Pillow Lab)plus--large

    Mentor: Yoel Sanchez Araujo, Graduate Student
    Principal Investigator: Jonathan Pillow
    Institution: Princeton University, Princeton, NJ

    Project: Characterizing reinforcement learning algorithms using neural data. The overall goal is for the fellow to gain experience and knowledge of reinforcement learning, model comparison, and how to generally preprocess and handle neural data. The fellow will explore two different reinforcement learning (RL) algorithms: one that performs policy iteration only and another that performs both policy and value iteration. Intuitively, algorithms that perform both policy and value iteration (e.g., actor-critic) seem better suited to explaining animal learning behavior in laboratory settings. The goal of this project is to assess this idea quantitatively. The fellow will fit RL algorithms to neural data and examine how well they account for neural dynamics. In doing so, they will learn techniques for model comparison and model evaluation, such as cross validation, Bayes factors and information criteria for model selection (e.g., AIC).

    Biography: Yoel Sanchez Araujo is a first generation Latino Ph.D. student at the Princeton Neuroscience Institute. His main research interests include: using and developing statistical methods for describing neural data, reinforcement learning and broadly the topic of learning and decision making at the systems level. He’s currently formally advised by Professors Jonathan Pillow and Nathaniel Daw, and collaborates with Professor Ilana Witten. He’s additionally interested in programming languages and broad topics in mathematics.

  • Rachel Lee (Princeton, Witten Lab)plus--large

    Mentor: Rachel Lee, Graduate Student
    Principal Investigator: Ilana Witten
    Institution: Princeton University, Princeton, NJ

    Project: Phasic responses from midbrain dopamine (DA) neurons have been argued to report a reward prediction error (RPE) signal. In the classic formulation, RPE is defined as a scalar broadcast, but recent demonstrations of heterogeneous DA responses challenge this view. Here, we propose to use a deep neural network reinforcement learning (RL) model to explain DA variability. To investigate this claim, we build a model that has topography carried through from the input of the network to the state features trained for the task. Specifically, we will separate out the final layer of the network into separate clusters of interconnected units. We hope to see if the network would learn faster, learn more segregated behavioral features, or learn a distinct set of behavioral features for the task.

    Biography: Rachel Lee is a fourth-year Ph.D. candidate co-advised by Ilana Witten and Nathaniel Daw at Princeton University. She received her undergraduate degree in Mathematical and Computational Science in 2015 from Stanford University, where she worked on cognitive modeling by applying deep neural networks to understand the mechanisms of perceptual learning. Her work today focuses on developing models and frameworks to better understand how dopamine neurons help facilitate reinforcement learning in the brain. Working in tandem with experimentalists from the Witten lab, she utilities computational modeling techniques including deep reinforcement learning networks and hierarchical model-free learning. She hopes to pursue computational cognitive neuroscience to further establish the link between our anatomical understanding of the brain and our behavior.

  • Victoria Corbit (Princeton, Witten Lab)plus--large

    Mentor: Victoria Corbit, Postdoctoral Fellow
    Principal Investigator: Ilana Witten
    Institution: Princeton University, Princeton, NJ

    Project: Rodent exploratory behavior is critical for survival in the wild and makes up a large portion of spontaneous behavior in laboratory tests. One circuit that may play a role in naturalistic exploration is the projection from prefrontal cortex (PFC) to ventral tegmental area (VTA). These regions are implicated in trained decision making, but little is known about how direct PFC-VTA projections influence naturalistic exploration. To unbiasedly study this circuit, I use a Hidden Markov Model to identify natural behaviors. However, because this method is unsupervised, it is unclear how identified behaviors compare to human labeling. One computational project is to quantify behavior using supervised machine learning to determine how the unsupervised clusters compare to those identified with supervised methods. Alternatively, an experimental project option is to assist with an optogenetic inhibition study to understand how exploratory behavior changes when we suppress PFC-VTA activity.

    Biography: I’m a second-year postdoctoral scholar in the Witten Lab whose project involves both experimental and computational components. I received my B.S. in neuroscience from Lafayette College in 2013, and completed my Ph.D. in neuroscience in 2019 at University of Pittsburgh. Though I worked with a small-circuit biophysical computational modeling in the first couple years of my Ph.D., the majority of my training prior to joining the Witten lab was in experimental techniques such as mouse behavior, optogenetics, acute slice electrophysiology, and fiber photometry. A goal for my postdoctoral training is to increase my use of computational techniques, specifically with applications to studying unrestrained, naturalistic behavior. As a mentor, I will aim to teach you the creativity, problem-solving and rigor that are essential for becoming a scientist.

Seattle, WA

  • Jon Rueckemann (UWashington, Buffalo Lab)plus--large

    Mentor: Jon Ruckemann, Postdoctoral Fellow
    Principal Investigator: Elizabeth Buffalo
    Institution: University of Washington, Seattle, WA

    Project: The hippocampus is critical for the formation of new memories. Our research investigates the physiology of the hippocampus in non-human primates to uncover how its microcircuitry supports information processing. Theta oscillations in the hippocampal local field potential arise from synchronized activity of principal cells, and thereby reflect the temporal structure of network-level communication. Although theta rhythmic activity is a near constant feature during attentive wakefulness in the rodent hippocampus, it manifests in primates only sporadically and much more variably in frequency. This fundamental difference in network physiology across species underlines the strong need to characterize the mechanisms that temporally structure hippocampal communication in non-human primates. The proposed SURF fellow project will use data collected in our laboratory during navigation of a virtual Y-maze to determine how neuronal spiking is shaped by bouts of theta activity.

    Biography: Jon Rueckemann’s research is centered around uncovering the hippocampal mechanisms that facilitate linking discontiguous events into a unitary sequence. His present research investigates hippocampal activity in non-human primates while they perform tasks in virtual environments to determine how task structure shapes the responses of hippocampal neurons. This top-down approach is complemented by characterization of the temporal structure of network activity and circuit manipulations, because understanding the circuit mechanisms that shape network physiology is key to describing how the hippocampus processes information. In his graduate work, he researched the influence of cortical input on hippocampal activity in rats through a combination of optogenetics, pharmacology, and extracellular electrophysiology in the laboratory of Howard Eichenbaum at Boston University.

    Eligibility: For participation in the research at the Washington National Primate Research
    Center, fellows must be at least 18 years old.

  • Fereshteh Lagzi (UWashington, Fairhall Lab)plus--large

    Mentor: Fereshteh Lagzi, Postdoctoral Fellow
    Principal Investigator: Adrienne Fairhall
    Institution: University of Washington, Seattle, WA

    Project: Role of inhibition in generating variability in birdsong. Some songbirds learn to sing stereotyped songs in a trial-and-error manner. To do so, one region of the bird brain produces a precise sequence of activity, while another region learns to connect these timing signals to a muscle output. The bird produces a variable song during learning. It is thought that its brain can evaluate the quality of these song variations in order to shape the network toward the right strategy. How is this exploratory variability generated in a brain network? In this project, we investigate the properties of variable outputs generated by model networks of interacting excitatory and inhibitory neurons that are inspired by known properties of the nucleus that injects this variability into the song. However, we do not know how the neurons are connected. We will characterize the nature of the activity patterns resulting from different choices of network connectivity and compare our results with experimental data.

    Biography: Fereshteh Lagzi has a background in nonlinear dynamics and control systems. During her Ph.D., she studied how interactions between excitatory and inhibitory neurons and how they are wired together shape the network’s patterns of activity. After completion of her Ph.D., Dr. Lagzi worked on artificial neural networks and analyzed different networks from a dynamical system point of view. In her postdoctoral research, she studied how plasticity rules that are specific to particular cell types can influence how networks organize. She uses mathematical analysis to understand how connections between neurons change, as well as characterizing the resulting changes in network activity. Currently, Dr. Lagzi is a Swartz fellow at the University of Washington and is working with Dr. Fairhall to understand how timing and variability are controlled in birdsong dynamics. Previously, she has successfully co-supervised an undergraduate student on a similar topic, and she has taught the class “Differential Equations” to undergraduate students.

  • Scott Sterrett (UWashington, Fairhall Lab)plus--large

    Mentor: Scott Sterrett, Graduate Student
    Principal Investigator: Adrienne Fairhall
    Institution: University of Washington, Seattle, WA

    Project: Animals use odor cues to navigate complex environments, locate sources of food and mates, or avoid predators. In natural environments, animals experience intermittent sensory cues due to the chaotic structure of odor plumes, yet are able to successfully localize the source of appetitive odors. The goal of this project is to build models which advance our understanding of the behavioral algorithms and neural implementations underlying this behavior. With short-term plasticity rules inferred from neural recordings of sensory, motor, and reward signals in the fly mushroom body, we will test reinforcement learning rules in an agent-based simulation environment. These simulations will be compared with behavioral data from freely navigating flies to understand the mechanisms that support the neural computations underlying this complex behavior.

    Biography: Scott Sterrett is a Ph.D. student in the neuroscience program at the University of Washington (UW) working with Adrienne Fairhall and David Gire. In his research, he develops computational models of natural behaviors and the neural circuits that support them. At UW, he studies odor guided navigation using behavioral and neural data from flies and mice. Previously, he completed a B.S. and M.S. in biomedical engineering at Johns Hopkins University. In his masters, he studied marmoset vocalization behaviors with Xiaoqin Wang. Scott is an active teacher and mentor, co-leading the UW undergraduate computational neuroscience journal club and mentoring an undergraduate in the Gire lab. Outside of the lab, Scott enjoys backpacking around the Pacific Northwest and birdwatching in the Seattle area.

  • Anna Bowen (UWashington, Steinmetz Lab)plus--large

    Mentor: Anna Bowen, Postdoctoral Fellow
    Principal Investigator: Nicholas Steinmetz
    Institution: University of Washington, Seattle, WA

    Project: We value things more if we need them. The brain is thought to control these two aspects of cognition through complementary systems: one that senses the state of the body (am I hungry?) and then creates feedback to guide motivation and attention (find food!); the other that monitors goods received for whether they counter the deficit (am I full yet?). Many cognitive-behavioral disorders involve dysregulation in these systems. My project in the Steinmetz lab seeks to unravel the evolving representation of reward value across the brain as need for the reward changes. Our approach uses brain-wide recordings from 1000s of neurons in mice as they work to receive rewards and reach satiation, and computational techniques to extract neural signatures of value and need that dynamically change across trials. A trainee on this project would contribute to animal training and data analysis and learn about neural systems and computation.

    Biography: I am an Alaskan Native (Aleut tribe) and a first year postdoctoral fellow in the Steinmetz lab. I received my Ph.D. in neuroscience from the University of Washington under Dr. Richard Palmiter studying the brain circuits that relay sensory information to the emotional centers of the brain that are important for forming memories and feelings. I grew up in rural Washington State and didn’t imagine that it was possible for me to be a scientist. The inspiration, encouragement and help given to me by teachers to pursue the subjects that most excited me helped me reach higher education and pursue a career in science, where I have been awarded fellowships during every career stage, from undergraduate to now. These experiences have made me passionate about mentoring others and helping them identify and achieve their goals, and also made obvious the joys and benefits of bravely exploring unfamiliar areas of study.

  • Noam Roth (UWashington, Steinmetz Lab)plus--large

    Mentor: Noam Roth, Postdoctoral Fellow
    Principal Investigator: Nicholas Steinmetz
    Institution: University of Washington, Seattle, WA

    Project: Decision making is a process that involves integrating sensory evidence, weighing that evidence with the current context, and finally executing behavioral choices. Recent findings have shown that choice-related activity is distributed broadly throughout the brain, but exactly how neural activity is coordinated across brain areas to generate a decision is still unclear. We will record simultaneously from large populations of neurons across many brain regions in behaving mice, in combination with targeted inactivation of populations of neurons. This approach will allow us to precisely determine the relationship between activity in one region and that in others. Specifically, we plan to record from the frontal cortex and many of its major outputs throughout the brain, while inactivating specific neurons, as mice perform a decision-making task. This project will thus involve behavioral training of mice, learning how to perform in vivo electrophysiology experiments, and data analysis.

    Biography: I am a postdoctoral scholar in the Steinmetz lab in the Department of Biological Structure at the University of Washington, and also a member of the International Brain Lab. I received my Ph.D. in neuroscience in the Rust lab at the University of Pennsylvania, where I studied the neural correlates of object-based attention and contextual effects on population representations in the macaque visual cortex. My current research is focused on the brain-wide circuits involved in perceptual decision making, with the goal of building an understanding of how the activity in different brain regions is coordinated to compute a decision. Broadly, I’m interested in how context affects neural representations and how different types of neural variability arise in the brain during perception and cognition. Passionate and supportive mentors have been crucial to my journey in becoming a scientist and I am excited to mentor trainees in the next generation of neuroscientists.

Vienna, Austria

  • Oriana Salazar Thula (UVienna, Zimmer Lab)plus--large

    Mentor: Oriana Salazar Thula, Graduate Student
    Principal Investigator: Manuel Zimmer
    Institution: University of Vienna, Vienna, Austria

    Project: The role of oscillatory motor neuron activity in sensory integration. We will investigate whether the coupling of a Central Pattern Generator (CPG) circuit that drives local head movement to global brain activity could play a role in the integration of sensory inputs in C. elegans. The activity of the immobilized C. elegans brain shows signals that are shared across many neurons. These coordinated, cyclical dynamics represent the worm’s major motor commands assembled into an action sequence used for food search. CPG neurons or circuits generate rhythmic neuronal activity to drive rhythmic behaviors. Neurons that are part of CPGs experience periodic membrane potential fluctuations. If these signals are passed onto neurons involved in sensory input processing, CPGs could create a bias in their excitability and thus create windows of integration for sensory information. We will probe this hypothesis using optogenetics, behaviour analysis in freely-moving worms, and calcium imaging.

    Biography: Oriana is a Venezuelan Ph.D. student working in the lab of Manuel Zimmer at the University of Vienna, Austria. Born and raised in Caracas, she left her home country at the age of 19 to pursue her undergraduate studies in Biology at the Freie Universität in Berlin, Germany. In 2014, she moved to Heidelberg, Germany to pursue a molecular biosciences M.Sc. degree. While participating in the Vienna Biocenter Summer School internship program in Vienna, she worked in the lab of Manuel Zimmer, where she later returned to for her M.Sc. thesis work. Since 2017, she is pursuing her Ph.D. in the Zimmer lab, where she focuses on motor neuron dynamics in the C. elegans nervous system. A first-generation university student from Latin America, Oriana is very passionate about improving the opportunities for underrepresented minorities in science and education.

  • Ulises Rey (UVienna, Zimmer Lab)plus--large

    Mentor: Ulises Rey, Postdoctoral Fellow
    Principal Investigator: Manuel Zimmer
    Institution: University of Vienna, Vienna, Austria

    Project: Decision making in conflicting inputs. Fasted C. elegans worms perform exploratory behavior to find food sources like bacteria. At the same time worms have to avoid toxic environments like areas with low oxygen levels. In normal conditions when worms find a food patch they enter it to maximally exploit its resources, even if the oxygen concentration is lower there. However, observations in the Zimmer Lab have shown that when the presence of food (appetitive) is coupled to extra low oxygen levels (aversive) the worms adapt their behavior to find an equilibrium between food exploitation and minimal hypoxia exposure. In the current project, we want to investigate the parameters that control this decision making by modifying the levels of the two contradictory inputs. Moreover, the implicated neural circuits could be uncovered using single neuron calcium imaging. Optogenetic activation/inhibition of the sensory pathways could bring the circuit out of balance to change the behavioral output.

    Biography: Ulises studied human biology at the University Pompeu Fabra in Barcelona before moving to Berlin to join Stephan Sigrist’s group at the Free University of Berlin for his master’s and Ph.D. degrees. He wanted to understand how neurons exchange information with each other and he focused on the process of how proteins are organized at the synaptic terminal. For the last three years he has been a postdoctoral fellow in Manuel Zimmer’s group,trying to understand the organization of behavior using the nematode C. elegans. In his project, he developed a new behavioral assay compatible with whole brain imaging recordings. The behavioral data is quantitatively analyzed to find transitions between different behaviors. At the same time, the activity of every single neuron in the central nervous system of the C. elegans is recorded to understand how the transitions are implemented at the level of global brain dynamics.

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