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 2022 SURF RFA. Applicants may select and rank up to three opportunities of interest.

Atlanta, GA

  • Sam Nason-Tomaszewski (Emory, Pandarinath Lab)plus--large

    Mentor: Sam Nason-Tomaszewski
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Chethan Pandarinath
    Institution: Emory University
    Location: Atlanta, GA
    Website: https://snel.gatech.edu/

    Project:People with paralysis have no avenues through which they can regain able-bodied function of their hands and fingers despite the circuits in the brain controlling movement often remaining intact. This project focuses on developing brain-computer interfaces to investigate how the brain generates dexterous hand and finger behaviors and find ways to restore such function to people with paralysis in a clinical trial. To do so, the researchers implant sensors into the motor cortex (the part of the brain that controls movement) of a person with paralysis. Then, they use artificial neural networks to study how biological neuron populations generate behavior and use them to predict the movements a person is trying to make in real-time to control a virtual hand. The SURF fellow joining this team will assist with developing the virtual hand model and machine learning algorithms. This project is ideal training for someone interested in real-time brain-computer interfaces and cutting-edge machine learning.

    Biography:
    Sam Nason-Tomaszewski is a postdoctoral fellow advised by Chethan Pandarinath in the Systems Neural Engineering lab at Emory University. His current research focuses on developing real-time brain-computer interfaces to recreate hand function in people with upper extremity paralysis. Specifically, he is using deep neural networks that predict high-fidelity arm, hand and finger movements in virtual environments using signals recorded by sensors implanted into the brain. Sam received his doctorate in biomedical engineering from Cindy Chestek’s lab at the University of Michigan where he investigated brain-controlled restoration of dexterous finger function in monkeys using functional electrical stimulation. He received the College of Engineering’s Towner Prize for Outstanding Ph.D. Research for his dissertation work. Prior to his doctoral studies, Sam received a Master of Science in biomedical engineering from the University of Michigan and a Bachelor of Science in electrical engineering from the University of Florida.

    Prerequisites: Due to the computational nature of the project, applicants pursuing a degree in computer science, electrical engineering or related disciplines are preferred. Applicants should be proficient in linear algebra and programming in Python. Training in machine learning is a plus.

    Eligibility: n/a

  • Chris Versteeg (Emory, Pandarinath Lab)plus--large

    Mentor: Chris Versteeg
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Chethan Pandarinath
    Institution: Emory University
    Location: Atlanta, GA
    Website: https://snel.gatech.edu/

    Project:Neuroscientists still don’t know how processing in the brain enables us to catch a baseball or calculate the tip at a restaurant. A key roadblock is the inability to recognize signatures of these computations in the activity of populations of neurons. Working with their mentor, the SURF fellow will train artificial neural networks, a modern machine learning tool, to perform simple cognitive tasks like remembering a string of numbers or performing simple arithmetic. They will then mine the emergent activity patterns for computational principles that may be conserved across tasks. The goal of this project is to build a “Rosetta Stone” for neural computations, helping neuroscientists to understand how neural circuits process information. As part of this project, the fellow will gain experience in building and applying machine learning techniques and learn how these computational tools can help us investigate neural function.

    Biography: Chris Versteeg is a postdoctoral fellow in the laboratory of Chethan Pandarinath, a professor in the Department of Biomedical Engineering at Emory University. Chris received his doctorate from Northwestern University in the lab of Lee E. Miller, where he studied how the sense of proprioception is encoded in the brainstem. During his doctoral work, he was fascinated by the potential of machine learning models to illuminate our understanding of how the brain processes information. To learn these state-of-the-art tools, he joined the Systems Neural Engineering laboratory to study how artificial neural networks can be applied to understand neural computation, in particular the sensory-motor transformations that underlie how the brain generates controlled movements. Chris’ recent work has focused on developing new computational tools to extract interpretable computations directly from neural recordings.

    Prerequisites: Due to the computational nature of the project, applicants pursuing a degree in computer science, electrical engineering or related disciplines are preferred. Applicants should be proficient in linear algebra and programming in python. Training in machine learning and dynamical systems theory is a plus.

    Eligibility: n/a

  • Leila Pascual & Kofi Vordzorgbe (Emory, Sober Lab)plus--large

    Mentor: Leila Pascual & Kofi Vordzorgbe
    Mentor Role: PhD Students
    Principal Investigator: Samuel Sober
    Institution: Emory University
    Location: Atlanta, GA
    Website: https://scholarblogs.emory.edu/soberlab/

    Project:When you’re getting ready in the morning, you’ve probably never wondered how you’re able to button your clothes while chatting with your roommate at the same time. Why? Because your nervous system is so well-practiced in carrying out these tasks that you perform them simultaneously without thought and even simultaneously. But in fact, both of these motor tasks — the fine, ordered coordination of your fingers and the precise vibrations of your vocal muscles — are incredible feats made possible by your brain. However, it remains unknown how the brain controls muscle activity to produce skilled behavior. The Sober lab investigates how neurons and circuits change during the process of learning skilled motor behavior by studying vocal learning in songbirds. The goal of the project is to understand how precise patterns of activity in the regions of the songbird brain that control song behavior are reshaped when a bird learns to sing. To do so, the lab combines advanced electrophysiological methods with novel mathematical tools to examine how the relationship between neural activity and song vocal behavior changes during learning. The fellow will work closely with the co-mentors to gain skills in experimental and computational techniques used in neuroscience research, as well as exposure to a vibrant and collaborative research environment.

    Leila Pascual Biography:Leila Pascual is third-year student at Emory University’s Neuroscience Ph.D. program, where she is a Woodruff fellow. She studies songbirds in the Sober Lab to understand how the brain enables the development of motor skills (a happy coincidence being a singer and songwriter herself). Leila received a Bachleor of Science in neuroscience and biology at Brandeis University, where she caught the ‘science bug’ and received an undergraduate teaching assistant award. Her fascination with the brain has led to her doing wide-ranging research at the University of Michigan, MIT, Brandeis and Emory — from differentiating abnormal muscle innervation patterns in patients with neuropathy and linking prenatal maternal immune function to autism to studying how taste and place memory interacts in the brain and how auditory processing improves maternal behavior.

    Kofi Vordzorgbe Biography:Kofi Vordzorgbe is in the first year of his Ph.D. in the M.D./Ph.D. program at Emory University. His research examines how several aspects of the song — acoustics, sequence and timing — are represented in the songbird brain through changes in activity in response to experimental manipulations. Kofi studied music and pre-medicine at Morehouse College, and has always been curious about the several mysteries surrounding the brain’s role in physical and subjective experiences. His previous research in neuroscience has focused on immediate neurological changes in the brain that may be protective during acute injury or stroke. He has also studied the changes in brain biochemistry in a population of individuals who appear to age without developing amyloid-beta pathology, a known risk factor for Alzheimer’s disease. After medical and graduate school, Kofi hopes to begin a neurology residency, and ultimately help to develop robust ways of clinically representing changes in brain activity, both following acute brain injury and during therapy.

    Prerequisites: Students from all academic backgrounds, with or without prior research experience, are welcome to apply. The mentors are looking for a fellow with an avid curiosity of the brain and its functions, a willingness to learn, and an interest in quantitative methods (though a strong math background is by no means required).

    Eligibility: n/a

  • Kyle Thomas (Emory, Sober Lab)plus--large

    Mentor: Kyle Thomas
    Mentor Role: Graduate Student
    Principal Investigator: Samuel Sober
    Institution: Emory University
    Location: Atlanta, GA
    Website: https://scholarblogs.emory.edu/soberlab/

    Project:Skilled movements require us to integrate both internal and external constraints as we navigate through the world. This integration takes place at multiple levels of the neuromuscular system to ensure success of the intended behavior. However, the activity of spinal motor neuron populations, which directly innervate muscles, remains largely unknown. Recent evidence suggests that the timescale of activity may play a role in how we adjust and adapt our behaviors. This research project aims to investigate this idea by recording motor units (the muscle fibers innervated by a single motor neuron) during locomotion in mice and analyzing their activation patterns during sensorimotor perturbations. The SURF fellow would be directly involved in establishing the behavioral protocol and modifying existing analytical tools for assessing the neural activity.

    Biography: Kyle Thomas is a biomedical engineering graduate student in the laboratory of Sam Sober at Emory University. His current work focuses on understanding how sensorimotor adaptation contributes to basic motor skills through spinal motor networks. Previously, Kyle studied biomedical and systems engineering at Washington University in St. Louis. He is interested in creating accessible neuro-technologies and generating more conversation surrounding neuro-ethics. He has grown a lot from the mentors in his life and hopes to carry that forward with new students.

    Prerequisites: No explicit prerequisites required, but fellows should be comfortable working with mice. Some basic coding skills through either R, MATLAB or Python may also be helpful.

    Eligibility: n/a

Bay Area, CA

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

    Mentor: Sriram Jayabal
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University
    Location: Palo Alto, CA
    Website: https://raymondlab.stanford.edu/

    Project: Neural integrators are networks of neurons that perform the mathematical integral of a signal that varies over time, enabling neurons to compute and store information over different timescales. One of the most well studied neural integrators is the oculomotor integrator, which holds the gaze steady when the eyes are turned away from the null position. However, the precise role of cerebellum in oculomotor integration is still an enigma. This project will use mouse models and transgenics to first investigate how the properties of the oculomotor integrator are adaptively modified by experience. Then, using optogenetics, the role of cerebellar Purkinje cells (the sole output neurons of the cerebellar cortex) in adaptive neural integration will be assessed. The SURF fellow will thus contribute to all aspects of the research from hypothesis formation and testing, experimental design, data collection and analysis, creating visual representations of results and oral and written communication.

    Biography: Sriram Jayabal is a postdoctoral fellow in the laboratory of Dr. Jennifer Raymond, in the Department of Neurobiology at the Stanford University School of Medicine. He received his Bachelor of Technology in biotechnology 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 the cerebellum implements learning. Sriram’s focus is the neurobiological underpinnings of meta-learning (learning to learn). His pioneering work on this topic integrates molecular, cellular and systems-level experimental approaches, and the computational analyses of SCGB collaborators. Sriram has considerable experience mentoring and is fully committed to the mentee’s success.

    Prerequisites: The lab is committed to training students without any previous laboratory experience, but will be delighted if the candidate is proficient in coding, is hardworking and practices meticulous record keeping.

    Eligibility: International students are eligible for funding.

  • Amin Shakhawat (Stanford, Raymond Lab)plus--large

    Mentor: Amin Shakhawat
    Mentor Role: Postdoctoral Scholar
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University
    Location: Palo Alto, CA
    Website: https://raymondlab.stanford.edu/

    Project: This project investigates distributed plasticity in the cerebellum and related oculomotor circuitry during motor learning alters the way the circuit computes. The SURF fellow will have the opportunity to utilize optogenetic perturbation of a synaptic plasticity mechanism combined with targeted optogenetic stimulation in vivo to dissect the algorithm governing the distribution of plasticity across sites in the circuit. The selected candidate will receive extensive training to contribute to this project by collecting and analyzing data, designing follow-up experiments, and presenting research findings to multiple scientific communities.

    Biography: Amin Shakhawat completed his doctoral research at Memorial University of Newfoundland, Canada with Dr. Qi Yuan. His Ph.D. work demonstrated how the locus coeruleus, the major noradrenergic nucleus of the brain, facilitates reward-related odor engram formation in the olfactory bulb and in the piriform cortex. For this project, he employed a molecular imaging technique called Arc catFISH that captures the activity patterns of hundreds of neurons at two different time points. Using this technique, he studied neural dynamics of a rewarded odor engram in the olfactory system in both five-to-six-day old rat pups and adult rats. Amin’s postdoctoral research at Stanford University focuses on elucidating the algorithm that a neural circuit uses to determine which synapses, among a sea of recently active synapses, should be modified to implement learning.

    Prerequisites: Experience with MATLAB is recommended but not required.

    Eligibility: International students are eligible for funding.

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

    Mentor: Trace Stay
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Jennifer Raymond
    Institution: Stanford University
    Location: Palo Alto, CA
    Website: https://raymondlab.stanford.edu/

    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. In this project, the SURF fellow will work closely with Dr. Trace 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 performed his doctoral research at Baylor College of Medicine in Houston, Texas, 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. Trace’s postdoctoral research at Stanford University analyzes the broader neural networks that process the vestibular signals encoding the 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, toward the goal of developing a comprehensive model of how multiple information sources are integrated and transformed to accurately regulate behavior.

    Prerequisites: Willingness to commit to the full SURF program, meaning both fall and winter quarters.

    Eligibility: International students are eligible for funding.

    Additional Mentorship Information

Boston, MA

  • Luis Hernandez-Nunez (Harvard, Engert Lab)plus--large

    Mentor: Luis Hernandez-Nunez
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Florian Engert
    Institution: Harvard University
    Location: Cambridge, MA
    Website: engertlab.org/

    Project: This project is focused on studying the neural mechanisms that mediate cardiac modulation of brain function and brain modulation of cardiac function from a systems-level perspective. Even though the heart-brain communication axis is central for homeostasis, our understanding of this pathway is limited to knowing some of the brain regions and neural populations involved in the process. We do not understand the computations carried out by the neural circuits within and between the cardioregulatory areas of the central, autonomic, and intracardiac nervous systems. Using larval zebrafish as a model organism, the researchers are pioneering systems-level studies of the neural circuits that mediate heart-brain communication. In this project, the SURF fellow will work with their mentor to develop high throughput assays for cardiac function measurements in freely moving larval zebrafish.

    Biography: Luis Hernandez-Nunez is a Life Sciences Research Foundation (LSRF) postdoctoral fellow at the laboratories of Florian Engert and Mark Fishman at Harvard University, and a visiting scientist at the HHMI Janelia Research Campus. Luis obtained his Ph.D. in systems biology from Harvard in 2020. He conducted his doctoral research in Aravinthan Samuel’s lab, where he discovered molecules, cells and circuits that mediate thermal homeostasis in larval Drosophila. Before graduate school, Luis was an undergraduate and then a postbac researcher at Thierry Emonet’s lab at Yale University. Prior to moving to the U.S., Luis studied mechatronics engineering at National University of Engineering in Peru.

    Prerequisites:
    Required: Introductory life sciences classes, calculus and basic Python.

    Eligibility: n/a

Chicago, IL

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

    Mentor: Ramanujan Srinath
    Mentor Role: Postdoctoral Scholar
    Principal Investigator: Marlene Cohen
    Institution: The University of Chicago
    Location: Chicago, IL
    Website: cohenlab.com

    Project: Our brain parses incoming information, decides what is and isn’t relevant, and plans and executes behaviors to interact with the objects in the environment. In comparison, programming computers to do the same tasks with similar accuracy or precision is very hard. This lab studies all the phases of interactive behavior by training monkeys to explore an immersive 3D environment and to engage with a displayed object. They do this by either simply observing its behavior, reporting its properties, or interacting with it in learned ways. Then, using neural recordings and data analysis techniques, the researchers decipher how the brain produces these behaviors. The SURF fellow will be involved in (a) coding and executing experiments in which humans and monkeys will engage with the objects around them in a VR environment, and (b) analyzing data to assess the neural transformations that lead to flexible behavior.

    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 multi-faceted projects that can answer fundamental questions about how we make inferences about and physically interact with our environment. He designs experiments that probe how cognitive processes and learning modulate internal neural representations.

    Prerequisites:
    Required: Proficiency with programming languages, such as C/C++/Objective C, Swift, Python, Java or Matlab, and some experience with data analysis and statistical techniques.
    Desired but not required: some experience with development in VR/AR environments like Blender, Unity, Unreal Engine or ARKit.

    Eligibility: n/a

  • Jose Ernesto Canton-Josh (Northwestern, Pinto Lab)plus--large

    Mentor: Jose Ernesto Canton-Josh
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Lucas Pinto
    Institution: Northwestern University
    Location: Chicago, IL
    Website: www.pintolab.org

    Project: The prefrontal cortex (PFC) is a cortical region crucial for working memory and decision-making. Dysfunction of the PFC has deeply established links to depressive disorders, attention deficit disorders, autism and schizophrenia. Understanding the fundamental computations of the PFC has direct implications for translational neuroscience and public health. This research project involves examining local neuronal micro-circuitry of the PFC regulating decision-making and tackles this complex question by using in vivo two-photon imaging of calcium sensor (GCaMP) activity in mice making navigational decisions in virtual reality. Different subtypes of neurons contribute precisely to computations performed by the PFC. By using transgenic lines, the researchers selectively record activity patterns of these subtypes, and, by combining these techniques with optogenetic tools, they change the activity of single neurons and measure how those disruptions affect circuit activity and decision-making. During the time in the lab, the trainee will spend most of their time learning how to analyze two-photon calcium sensor data. These are large rich datasets and after basic training Jose would be excited to help the trainee design a semi-independent analysis project. Additionally, there will be many opportunities to help with viral tracing experiments and fundamental histological assays.

    Biography: Jose Ernesto Canton-Josh grew up in El Salvador and moved to the US when he was twenty years old to receive an undergraduate education. His own exposure to academia and neuroscience was relatively late, but once he found the joy of scientific discovery it became his passion. He has years of experience using transgenic lines, modern viral tools, acute slice electrophysiology, viral tracing techniques and behavioral paradigms. Jose is proficient in writing analysis code in MATLAB and Python. He believes being a good mentor and teacher is the most important quality for a principal investigator. As a recent Ph.D. graduate with the hopes of one day starting his own lab, Jose is very excited to share his skills and help train new scientists. He has had great experiences mentoring spectacular undergraduate scientists during his graduate work; these trainees were included as authors on his publications, and he helped them apply to graduate and medical schools.

    Prerequisites: No official prerequisite skills, as Jose is happy to teach new skills. However, to get the most from working in the lab, some familiarity with the basics of neuroanatomy and neuronal subtypes would be helpful. Familiarity with some coding skills would also be useful, such as MATLAB or Python.

    Eligibility: n/a

Cold Spring Harbor, NY

  • Christopher Langdon (CSHL, Engel Lab)plus--large

    Mentor: Christopher Langdon
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Tatiana Engel
    Institution: Cold Spring Harbor Laboratory
    Location: Cold Spring Harbor, NY
    Website: www.cshl.edu/research/faculty-staff/tatiana-engel/

    Project: The ability to adapt behavior depending on context is considered a hallmark of cognitive flexibility in humans. While the prefrontal cortex is known to play a key role, the remarkable complexity of neural responses in this region has made mechanistic understanding difficult. In particular, it remains unknown how excitatory and inhibitory interactions between neurons in this region give rise to behavioral flexibility. Recurrent neural network (RNN) models have recently been utilized to capture this complexity and produce mechanistic hypotheses for experiment. In this project, the SURF fellow will learn how to train RNNs to perform context-dependent decision-making tasks. They will then utilize novel computational tools being developed in the lab to reveal how connectivity structure in these networks supports the dynamics and behavior. The fellow will then use these tools to analyze real neural recordings from prefrontal cortex of primates performing the same cognitive tasks.

    Biography: Dr. Christopher Langdon is a postdoctoral fellow in the Engel Lab at Cold Spring Harbor Laboratory (CSHL). He was recently awarded a Swartz Foundation Postdoctoral Fellowship and is currently a researcher for the International Brain Laboratory (IBL). Christopher’s research focuses on neural circuit mechanisms for cognitive tasks and how they are distributed within heterogeneous neural populations. His work utilizes state-of-the-art recurrent neural network models as a testbed for developing and refining computational inference tools for extracting low-dimensional circuit mechanisms from high-dimensional neural recording data. Christopher is particularly interested in flexible decision-making and how animals can flexibly adapt stimulus-response associations depending on context. He is interested in the role of frontal cortex in cognitive control and how these processes facilitate flexible decision-making.

    Prerequisites: 1. The fellow should have some experience with the Python programming language.
    2. The fellow should have some mathematical understanding of basic concepts in linear algebra and dynamical systems.
    3. Lastly, the fellow should have familiarity with experimental paradigms for studying cognition (decision-making, working memory etc.) in neuroscience.

    Eligibility: The lab is able to consider undergraduate students 18 years and older from CSHL and universities other than CSHL. Should an international student express interest in working at CSHL through the SURF program, eligibility would depend on the student’s visa status and other information needed to make a determination of employment eligibility at CSHL.

  • Pavel Tolmachev (CSHL, Engel Lab)plus--large

    Mentor: Pavel Tolmachev
    Mentor Role: Postdoctoral Researcher
    Principal Investigator: Tatiana Engel
    Institution: Cold Spring Harbor Laboratory
    Location: Cold Spring Harbor, NY
    Website: www.cshl.edu/research/faculty-staff/tatiana-engel

    Project: This research group aims to understand how contextual inputs modulate the circuits in the motor cortex to produce flexible motor behaviors. Specifically, in collaboration with Banerjee’s lab (CSHL), the group studies Alson’s brown mice, known for their ability to produce bouts of vocalization. The bouts vary from 6-15 seconds depending on the social context. The neural recordings from the motor cortex showed that, depending on the duration of the “song,” the neurons display the same pattern of the activity, but the pattern is temporally scaled to match the song duration. A plausible hypothesis is that such temporal scaling arises on the network level driven by modulatory contextual inputs. To study the mechanism behind the temporal scaling, the group uses recurrent neural networks (RNNs) trained to produce variable-length tasks. RNNs are amenable to analysis; studying the mechanism behind the temporal scaling in RNNs will generate hypotheses for the mechanisms in the bio-circuits.

    Biography: After finishing his M.S. at Moscow State University at the Department of Physics (2016), Pavel Tolmachev obtained an MPhil from the University of Melbourne (2021), where he collaborated with physiologists on constructing a computational model for the mammalian brainstem neural network responsible for breathing-swallowing interaction. His research interests include a range of topics spanning machine learning (deep learning and reinforcement learning), systems neuroscience (decision-making and motor control), as well as theoretical studies of synaptic plasticity and memory. He also gained extensive teaching experience during his master’s and Ph.D. programs, teaching a variety of subjects (probability theory, signal processing, programming). Pavel is passionate about sharing his expertise, both in technical fields and communication in research. He is looking forward to this program both to teach and learn from the students, hoping to establish a thought-stimulating collaboration.

    Prerequisites: Good knowledge of Python and the standard packages: numpy, scipy, matplotlib. Knowing PyTorch is a plus. A firm understanding of linear algebra and basic numerical methods for solving differential equations. Some understanding of basic neuroscience concepts (e.g., functionality of neurons, anatomical division of the brain) is desirable but not necessary.

    Eligibility: The lab is able to consider undergraduate students 18 years and older from CSHL and universities other than CSHL. Should an international student express interest in working at CSHL through the SURF program, eligibility would depend on the student’s visa status and other information needed to make a determination of employment eligibility at CSHL.

London, United Kingdom

  • Brendan Bicknell (UCL, Häusser Lab)plus--large

    Mentor: Brendan Bicknell
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Michael Häusser
    Institution: University College London
    Location: London
    Website: http://www.dendrites.org/

    Project: One of the most striking features of the brain is the rich diversity of cell morphologies. Elaborately branched axons and dendrites emanate from cell bodies, defining the characteristic shapes of neurons and rules of connection, and fundamentally influencing the way signals are transmitted and received. However, predominant theories of neural computation tend to be based on simplifying ‘spherical cow’ assumptions that ignore these details. In this project the SURF fellow will explore the implications of cell morphology for computation at the single-neuron and circuit level. They will use dynamical systems theory and simulations to study the properties of realistic models of neurons and use these insights to develop spiking network models that more accurately reflect what we know of the underlying biology. This project is ideally suited to a student with a quantitative background who is curious to uncover how the basic cellular hardware of the brain supports its function.

    Biography: Brendan Bicknell is a postdoctoral fellow in the Neural Computation Lab at University College London. He uses theoretical approaches combined with large-scale simulations to understand how the low-level biophysical properties of cells give rise to higher-level function. Previously he studied mathematics as an undergraduate at the University of Queensland, Australia, and completed a Ph.D. in computational neuroscience with Geoff Goodhill at the Queensland Brain Institute. He can often be found in the company of experimentalists, whose perspectives from the coalface have been highly influential.

    Prerequisites: Good working knowledge of differential equations and Python programming.

    Eligibility: To be eligible, the prospective SURF fellow will need to be enrolled as an undergraduate student at the time of application, but not throughout the fellowship.

  • Federico Rossi (UCL, Häusser Lab)plus--large

    Mentor: Federico Rossi
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Michael Häusser
    Institution: University College London
    Location: London
    Website: http://www.dendrites.org/

    Project: The cerebellum is crucial for motor coordination and has recently been implicated in reward learning. Recording in mice learning a motor task (Kostadinov et al, 2019, Nat Neurosci), researchers identified modules in cerebellar lobule V that exhibit motor command signals and modules in lobule simplex that exhibit responses to rewards and errors. However, the brain-wide circuits driving these motor and reward modules, and the downstream targets receiving their output, remain unknown. To map them, the SURF fellow will learn to use retrograde transsynaptic viruses to trace the inputs to motor and reward cerebellar modules, and anterograde transsynaptic viruses to trace their downstream targets. The fellow will then gain experience in serial-section two-photon histology; by quantifying traced neurons across the brain, they will reveal the brain-wide pathways involved in cerebellar learning. These experiments will in turn enable targeted Neuropixels recordings and optogenetic manipulation of origin and target areas.

    Biography: Dr. Federico Rossi trained in neurobiology at Scuola Normale Superiore (Pisa, 2012) and completed his doctorate in neuroscience at University College London, sponsored by a Wellcome Trust Studentship (UCL, 2018). During these studies, Federico pioneered strategies to characterize the connectivity, synaptic architecture, and activity of cortical neurons in vivo. Working jointly in the Kullmann and Carandini labs, he applied these methods to investigate the pathways of propagation of focal cortical seizures (Rossi et al, 2017, Nat Commun). Then, in the Carandini-Harris lab, he focused on mapping the visual circuits underlying image and motion processing in the cortex (Rossi et al, 2020, Nature). Federico currently investigates the neural circuits orchestrating sensory-motor computations in the neocortex and in the cerebellum funded by a Sir Henry Wellcome Fellowship in the Häusser lab (UCL, 2021).

    Prerequisites: U.K. Home Office Personal License for animal experimentation with mice is required (training to obtain the license will be provided if necessary). Previous coding experience in Python or Matlab is desirable. Experience with software for image analysis and neuroanatomy is desirable (e.g., BrainGlobe, Napari).

    Eligibility: To be eligible, the prospective SURF fellow will need to be enrolled as an undergraduate student at the time of application, but not throughout the fellowship.

Los Angeles, CA

  • Joao Couto (UCLA, Churchland Lab)plus--large

    Mentor: Joao Couto
    Mentor Role: Assistant Project Scientist
    Principal Investigator: Anne Churchland
    Institution: University of California, Los Angeles
    Location: Los Angeles, CA
    Website: https://neurobio.ucla.edu/people/anne-churchland/

    Project: The prevalence of many neurological disorders is sex-specific, a disparity that may be due to differences in brain development because of when symptoms first arise. This research project proposes to investigate sex-specific differences in neural processing in the context of a decision-making task. Cortical cells can be grouped into categories based on their gene-expression during development and be targeted using transgenic tools. The research group uncovered differences in the activation of developmentally defined cell-types, suggesting that cell-types may take different roles in decisions. Yet, it is not known if brains of different sexes have the same activation patterns. To address this gap in knowledge, this project will first train mice in visual and auditory decision-making tasks. Second, it will measure activity across the entire dorsal cortex in different developmental mouse lines and animals of different sexes. Finally, it will use machine-learning tools to uncover sex-specific differences in neural processing.

    Biography: Joao Couto is a postdoctoral fellow at the University of California, Los Angeles (UCLA), studying long-range communication between brain areas during decision-making. He does this by combining mouse behavior with optogenetics, calcium imaging and electrophysiology. During his Ph.D. program in Antwerp, Belgium, he developed closed-loop methods to interrogate dynamic properties of neurons and used those to show that cerebellar Purkinje cells transition smoothly between distinct operation modes. He then moved to Leuven, where he studied visual circuits. He found that cell types in the visual thalamus are more susceptible to modulations by behavior and studied visuo-tactile interactions during virtual navigation. Joao develops tools to record and interact with neurons, behavior apparatus and computational resources which he releases in the spirit of open and reproducible science. Moreover, he is mentored in the Optical Imaging and Electrophysiological Recordings course (Paris, France), and will provide close support and guidance.

    Prerequisites: The project has different phases, from animal training, data collection and analysis. Experience with animals and some knowledge of coding is advantageous but not required. The level of involvement in each phase of the project will be adjusted based on the applicant’s future aims and goals.

    Eligibility: For non-UCLA students, the only eligibility requirement is U.S. employment eligibility.

  • Felicia Davatolhagh & Max Melin (UCLA, Churchland Lab)plus--large

    Mentor: Felicia Davatolhagh & Max Melin (Co-Mentorship)
    Mentor Role: Postdoctoral Scholar & Graduate Student
    Principal Investigator: Anne Churchland
    Institution: University of California, Los Angeles
    Location: Los Angeles, CA
    Website: https://churchlandlab.org/

    Project: Learning is a dynamic process that engages multiple neural circuits across the brain. Despite our understanding of learning-related changes at individual circuits and behavior, how learning organizes circuits at a broader spatial scale remains unclear. To assess learning-related changes in neural activity, this project will use a combination of widefield calcium imaging and dense electrophysiological recordings as animals learn a visual-based behavioral task. The SURF fellow will be involved in analyzing behavioral strategies throughout learning using video-tracking computational models of mouse behavior. The fellow will be able to explore mouse decision-making strategies during early and late learning, and how these strategies are represented in the brain. Additionally, the fellow can assist experimentally, gaining hands-on experience in mouse behavioral training. This project is a joint co-mentorship between a postdoctoral scholar Felicia Davatolhagh and M.D./Ph.D. student Max Melin.

    Felicia Davatolhagh Biography: Felicia Davatolhagh is a first-year postdoctoral scholar in the Churchland lab whose project focuses on understanding learning-related changes in neural activity. She received her graduate degree in neuroscience in 2021 from the University of Pennsylvania, mentored by Dr. Marc Fuccillo, where she worked on understanding how Neurexin1alpha, a synaptic cell adhesion molecule implicated in several neurodevelopmental disorders, maintains synaptic function. Her postdoctoral research analyzes how learning is disrupted in the Neurexin1alpha genetic mouse model and aims to identify circuits that may be particularly vulnerable to genetic perturbations. Felicia has benefitted from the mentorship of numerous programs (AMGEN scholars, NIH MARC, HHMI Gilliam) and is passionate about mentoring others in turn.

    Max Melin Biography: Max Melin is in his third year of training at the UCLA-Caltech Medical Scientist Training Program, performing graduate research in neuroscience. He completed his B.S in bioengineering at Stanford University while working with Dr. Thomas Sudhof to study fear memory and essential tremor. In the Churchland lab, he now studies learning and behavioral state — specifically how these are represented and evolve in the brain as mice learn decision-making tasks. When not in the lab, he can be found playing drums, ultimate frisbee or rock climbing in Yosemite or the High Sierra. In addition to scientific mentorship, Max has mentored several undergraduates who have continued on to medical and graduate school and is willing to provide application mentorship to any students who plan to pursue medical, graduate or M.D./Ph.D. training.

    Prerequisites: n/a

    Eligibility: For non-UCLA students, the only eligibility requirement is U.S. employment eligibility.

New York, NY

  • Jeff Johnston (Columbia, Fusi Lab)plus--large

    Mentor: Jeff Johnston
    Mentor Role: Postdoctoral Research Scientist
    Principal Investigator: Stefano Fusi
    Institution: Columbia University in the City of New York
    Location: New York, NY
    Website: https://ctn.zuckermaninstitute.columbia.edu/

    Project: The brain is often viewed as a modular system, which is split into multiple anatomically and functionally distinct brain regions. For example, in the primate visual system, certain brain regions are specialized for the representation of complex objects, while others are specialized to represent visual motion. Here, this project seeks to understand functional modularity in the brain by investigating when it emerges in artificial neural networks, as well as through the application of a previously developed mathematical theory. The researchers hypothesize that a system required to perform multiple distinct task-sets will split into modules at critical points predicted by the theory. The SURF fellow will help develop analytical tools for characterizing modularity and work from an established code base to test this hypothesis. The fellow will learn to train feedforward networks, apply common statistical models to data, and gain a background in related experimental and theoretical work on module formation.

    Biography: Jeff Johnston is a postdoctoral research scientist with Stefano Fusi in the Zuckerman Institute at Columbia University. He completed his graduate work with David Freedman at the University of Chicago, where he received an NRSA predoctoral fellowship from the NIH and the best dissertation award in computational neuroscience. Jeff’s work focuses on how the neural code enables reliable and flexible behavior, using a combination of approaches including data analysis, computer simulations and mathematical theory. Recently, Jeff has shown that “abstract” representations, which support the ability to generalize particular behaviors from one context to another, emerge naturally in artificial neural networks trained to perform multiple tasks, suggesting a method that may be at work in the brain. Jeff has also developed a geometric theory that captures the tradeoff between the representational geometry that enables this generalization and one that enables flexible behavior.

    Prerequisites: One year of coding experience is preferred. Existing code for this project is in Python, so familiarity with Python or a willingness to learn it would be ideal.

    Eligibility: (1) Anyone hired will need to be eligible to work within the United States and follow regulations put in place by New York State and the Federal government. All proper training must be completed before working with non-human primates or other subjects. However, in this case, as the fellow would be in a theory lab, no animal training is anticipated.
    (2) Columbia has a mechanism to administer funds as allocated to an undergraduate fellow at a different undergraduate institution.
    (3) Columbia can hire international students if they have proper work authorization tied to their Visa status. The student must follow the work authorization that is allowed by their Visa Status and school allowable time worked.

  • James Niemeyer (Weill Cornell, Aksay Lab)plus--large

    Mentor: James Niemeyer
    Mentor Role: Postdoctoral Researcher
    Principal Investigator: Emre Aksay
    Institution: Weill Medical College of Cornell University
    Location: New York, NY
    Website: physiology.med.cornell.edu/people/emre-aksay-ph-d/

    Project: Epilepsy is thought to arise from aberrant network dynamics and is poorly controlled in roughly one-third of cases. Researchers have recently determined in an animal model of epilepsy that seizure initiation is associated with excessive activity in small groups of excitatory neurons in the midbrain. The SURF fellow will initially be involved in analyzing data from these groups to see what patterns of activity are associated with initiation. Ideally, the fellow will also be involved in closed-loop optophysiology experiments where identified patterns are suppressed in a targeted manner to dampen or eliminate seizure generation.

    Biography: James Niemeyer is a postdoctoral researcher at Weill Cornell Medicine (WMC). He received his Ph.D. from Brown University in 2015, where he studied visual system physiology, perception and decision-making in non-human primates. Niemeyer then took a short postdoctoral appointment at Rockefeller University where he initiated a study of cognition and memory during single-cell imaging in a rodent model. He then transitioned to the Epilepsy Research Laboratory, where he now studies seizure dynamics using fluorescence imaging and electrophysiology. James’ research is focused on understanding which populations of brain cells initiate seizures and which populations of cells could be manipulated to prevent seizures. Recently, he published collaborative work that examined some of these questions in larval zebrafish, an important new model organism in the neurological disease field. His ongoing work continues to address questions related to the roles of different cell types and brain networks in epilepsy

    Prerequisites: Applicants should have some proficiency in coding (Matlab, Python or C preferred), and have completed multi-variate calculus and introductory physics (mechanics, E&M, optics and waves). Familiarity with linear algebra, statistics and instrumentation is preferred.

    Eligibility:
    1. Students should be 18 years or older.
    2. WMC can administer funds to a fellow that is enrolled at a different undergraduate institution.
    3. WMC expects to administer funds to a student located domestically.

  • Jingpeng Wu (Flatiron Institute, Chklovskii Group)plus--large

    Mentor: Jingpeng Wu
    Mentor Role: Associate Research Scientist, Neural Circuits and Algorithms Group
    Principal Investigator: Dmitri Chklovskii
    Institution: Center for Computational Neuroscience, Flatiron Institute
    Location: New York, NY
    Website: https://www.simonsfoundation.org/flatiron/center-for-computational-neuroscience/neural-circuits-and-algorithms/

    Project: U-Net is a U-shaped convolutional network architecture that is widely used in biological image segmentation. Convolutional networks are typical techniques in deep learning. The research group trains U-Net models to detect neuron membranes, synapses, mitochondria and glia, among other structures. Improving the model architecture with state-of-the-art techniques is expected to increase detection accuracy. The current U-Net is designed about four years ago and is outdated considering the rapid development of deep learning technologies. Recently, a combination of modern techniques has made the Convolutional Neural Networks (ConvNet) better than Transformers in terms of both speed and accuracy [1]. Another direction of improvement is the loss function, we would like to improve the model with some state-of-the-art loss functions [2]–[4]. This project aims to test these features and incorporate the useful ones into the existing Deep Learning framework.

    [1] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie, “A ConvNet for the 2020s,” ArXiv220103545 Cs, Jan. 2022, Accessed: Feb. 09, 2022. [Online]. Available: http://arxiv.org/abs/2201.03545
    [2] A. Sheridan et al., “Local Shape Descriptors for Neuron Segmentation,” bioRxiv, p. 2021.01.18.427039, Jan. 2021, doi: 10.1101/2021.01.18.427039.
    [3] R. El Jurdi, C. Petitjean, P. Honeine, V. Cheplygina, and F. Abdallah, “High-level prior-based loss functions for medical image segmentation: A survey,” Comput. Vis. Image Underst., vol. 210, p. 103248, Sep. 2021, doi: 10.1016/j.cviu.2021.103248.
    [4] X. Hu, Y. Wang, L. Fuxin, D. Samaras, and C. Chen, “Topology-Aware Segmentation Using Discrete Morse Theory,” ArXiv210309992 Cs, Mar. 2021, Accessed: Mar. 26, 2021. [Online]. Available: http://arxiv.org/abs/2103.09992

    Biography: Jingpeng Wu joined the Flatiron Institute in 2020 as an associate research scientist to work on mapping neurons based on high-resolution Electron Microscopy images. Prior to coming here, Jingpeng was an associate research scholar at Princeton University, where he worked on petabyte-scale neuron reconstruction based on electron microscopy images using deep learning and cloud computing technologies. Jingpeng has a Ph.D. in biomedical engineering from Huazhong University of Science and Technology in China. In pursuing his Ph.D., he worked on large scale neuron and blood vessel tracing based on light microscopy images of whole mouse brains.

    Prerequisites:
    Machine Learning, preferably Deep Learning
    Coding skills using Python
    Experience or interest in image processing
    Experience with C/C++ preferred

  • Alex Williams (Flatiron Institute, Williams Group)plus--large

    Mentor and Principal Investigator: Alex Williams
    Mentor Role: Associate Research Scientist and Project Leader, Statistical Analysis of Neural Data Group
    Institution: Center for Computational Neuroscience, Flatiron Institute
    Location: New York, NY
    Website: https://www.simonsfoundation.org/flatiron/center-for-computational-neuroscience/statistical-analysis-of-neural-data/

    Project: This project analyzes camera and microphone array recordings of Mongolian gerbil families in collaboration with labs run by Professors Dan Sanes and David Schneider at New York University (NYU). Gerbils are highly social rodents — more so than typical laboratory species — and they produce elaborate vocal call sequences in their social interactions. From these video and audio data, this project aims to extract all vocalizations and identify which gerbil produced them (a problem known as speaker diarization). To achieve this, the team plans to utilize deep convolutional networks accompanied by methods to quantify uncertainty in deep learning models. A SURF fellow will implement these deep network models in Python and help curate the video and audio datasets (e.g., flagging and removing mislabeled video frames). The fellow will also aid in the collection of behavioral data.

    Biography: Alex Williams is jointly appointed as an assistant professor in the Center for Neural Science at NYU and an associate research scientist/project leader at the Flatiron Institute. Alex develops statistical models to characterize functional flexibility in large-scale neural circuits — e.g., how the dynamics of large neural ensembles change when learning a new skill, during periods of high attention or task engagement, or over the course of development and aging.

    Alex performed his postdoctoral work in statistics at Stanford University working with Scott Linderman’s research group. Before that, he obtained a Ph.D. in neuroscience from Stanford with supervision from Surya Ganguli. He has also worked at Google Brain (with David Sussillo), Sandia National Labs (with Tamara Kolda), the Salk Institute (with Terry Sejnowski) and Brandeis University (with Eve Marder) as a visiting researcher/technician. Alex first began studying neuroscience as an undergraduate at Bowdoin College, where he was advised by Patsy Dickinson.

    Prerequisites: Working knowledge of Python

  • Gerick Lee (NYU, Movshon Lab)plus--large

    Mentor: Gerick Lee
    Mentor Role: Graduate Student
    Principal Investigator: Tony Movshon
    Institution: New York University
    Location: New York, NY
    Website: https://www.cns.nyu.edu/~vnl/

    Project: Development of contour selectivity in area V4
    The ventral stream of visual cortex is associated with the processing of visual forms. Neurons in ventral area V4 are known to be tuned for curvature; their firing rates are modulated by the presence or absence of particular contour features within visual stimuli. It is unknown how or whether this tuning changes during early life. To better understand the origin of shape tuning in V4, this project will analyze neural recordings made from macaques during the first year of life in response to shape stimuli shapes varying parametrically in orientation and curvature. The researchers will measure the effects of age on the encoding of stimulus features, the nature of stimulus encoding over time, and the temporal dynamics of the evoked response. Given the importance of area V4 to form processing as a whole, a characterization of how shape tuning changes during early life will provide insight into the origin of form vision.

    Biography: Gerick Lee is a graduate student in the Visual Neuroscience Laboratory at NYU. He is interested in how visual cortex supports the perception of visual forms and how development affects this relationship. Gerick entered science at the University of Washington, where he studied the effect of sleep deprivation on memory in Reykjavik, Iceland, and with brain computer interfaces in macaques in Seattle. Following this, he obtained a master’s in Neural systems and computation from the University and ETH Zurich, where he first became interested in development, studying how sleep-related neural signals change across childhood. After a six-month fellowship in Melbourne, Australia, studying the effects of anesthesia on the encoding of auditory information, he moved to New York in 2015. At NYU, Gerick studies development through the use of behavioral and physiological methods in macaques. He also studies how amblyopia, a disorder caused by atypical visual development, affects form processing in humans.

    Prerequisites: Familiarity with statistics, competence in Matlab and/or Python, willingness to work with animals.

    Eligibility: n/a

  • Manu Raghavan (NYU, Movshon Lab)plus--large

    Mentor: Manu Raghavan
    Mentor Role: Postdoctoral Associate
    Principal Investigator: Tony Movshon
    Institution: New York University
    Location: New York, NY
    Website: https://www.cns.nyu.edu/~vnl/
    Mentor Website: http://rtraghavan.com

    Project: Visual search and object recognition in natural scenes
    The goal of this project is to generate behavioral benchmarks that can be used to compare the performance of artificial neural networks that model core object recognition to human performance. The comparison will take place within the Simons-funded Brain-Score framework. Concretely the project will involve the design and collection of data from human subjects as they detect/search for targets ranging from simple sinusoidal gratings to objects (like faces or animals) placed at various locations in natural images. Analysis of this performance will be uploaded to Brain-Score, where performance on these tasks can be automatically compared to the performance of dozens of artificial neural networks on the same task. Analysis will be performed using the Python programming language (no prerequisite knowledge of programming required). Depending on your interest and experience we can make the project more or less computational.

    Biography: Manu Raghavan is a fifth year postdoctoral researcher working with Tony Movshon at NYU. His undergraduate background is in biology and psychology with some philosophy and engineering mixed in. He completed his Ph.D. at Duke University studying how monkeys execute eye movements to track and fixate on objects in their environment. He currently studies how brain circuits from the retina through the rest of the brain analyze motion and form. At Duke, Manu builds models of visual processing to study how human subjects perceive images. He records the activity of large numbers of neurons throughout the monkey visual system in response to those same images to try to link activity in the brain to perception. Manu has mentored several undergraduates who have gone on to do everything from pursuing a Ph.D. in neuroscience, to going on to medical school, to working at companies in the area. He hopes he can help the fellow achieve their career goals.

    Prerequisites: This is a project that can be completed with high-school knowledge of algebra and a willingness to learn. Regardless of background the fellow will receive training/experience in both Python programming and experimental design by the completion of the summer.

    Eligibility: n/a

San Diego, CA

  • Assaf Ramot (UCSD, Komiyama Lab)plus--large

    Mentor: Assaf Ramot
    Mentor Role: Postdoctoral Scholar
    Principal Investigator: Takaki Komiyama
    Institution: University of California San Diego
    Location: La Jolla, CA
    Website: https://komiyamalab.biosci.ucsd.edu/

    Project: The project’s primary research interest is unraveling the neural circuit basis of motor learning and movement generation. This understanding is essential for a fundamental understanding of brain mechanisms and diagnosing and treating clinical conditions like Parkinson’s disease, multiple sclerosis, amyotrophic lateral sclerosis (ALS) and stroke. The motor cortex is a central locus for motor learning. Advanced imaging methods developed in recent years, such as two-photon calcium imaging, made it possible to record the activity of many neurons and, therefore, explore how long-lasting motor memories are stored in the brain. However, despite the high-quality research done so far, none of the past work has addressed the critical aspect of integrating connectivity and activity of diverse cortical neuronal populations associated with motor learning. This project aims to fill this gap in knowledge using cutting-edge methods to integrate patterns of connectivity and activity of the rodent primary motor cortex

    Biography: Assaf Ramot earned his bachelor’s and master’s degree in psychology. He then completed his Ph.D. under the supervision of Professor Alon Chen in the Department of Neurobiology at the Weizmann Institute of Science, Israel, where he studied the underlying mechanisms of long-term exposure to stress. Since 2017, Assaf has served as a postdoctoral scholar at the lab of Professor Takaki Komiyama, at the University of California, San Diego. Motor learning refers to the ability to alter movements in an ever-changing environment and is fundamental for the well-being of many animal species, including humans. Assaf has been using advanced imaging methods developed in recent years to unravel the fundamental principles of the mammalian brain operating during motor learning, planning and movement execution. Assaf’s specific focus is on exploring the interaction between long-range inputs and the microcircuit connectivity within the motor cortex in a way previously impossible to test in-vivo.

    Prerequisites: The project requires working with laboratory animals (mice).
    Required: Great team player, high motivation to learn advanced optical methods.
    Preferred but not required: Knowledge of neuroscience and basic coding (MATLAB) and lab experience.

    Eligibility: n/a

  • Enida Gjoni (UCSD, Komiyama Lab)plus--large

    Mentor: Enida Gjoni
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Takaki Komiyama
    Institution: University of California San Diego
    Location: La Jolla, CA
    Website: https://komiyamalab.biosci.ucsd.edu/

    Project: Motor behaviors arise from dynamic interactions of interconnected neural populations across different brain areas. The underlying principles of information flow remain largely unknown. This project aims to determine the functional role of neuronal pathways involving motor cortex and intralaminar thalamus in driving specific subpopulations of the striatum — the input nucleus of the basal ganglia — during movement. For this, the activity of direct and indirect pathway medium spiny neurons (dMSNs and iMSNs) in the striatum will be imaged as mice perform motor tasks, by using in vivo two-photon calcium imaging through GRIN lens. Concomitantly, optogenetic inactivation of cortical or thalamic inputs will be performed and the effect of these manipulations on the striatal activity will be quantified. These experiments will help elucidate whether and how cortical and thalamic inputs contribute differentially to the activity of dMSNs and iMSNs.

    Biography: Enida Gjoni was born in Albania. During her childhood she moved to Italy, where she pursued her studies in medical biotechnology at the University of Milan. She chose the neuroscience curriculum and her master’s thesis project consisted in studying the role of bioactive sphingolipids in glioblastoma-derived cell lines. She did her doctoral studies at the École Polytechnique Fédérale de Lausanne, in Switzerland. This work focused on the functional and structural characterization of excitatory and inhibitory synapses within a binaural circuit of the brainstem, involved in sound localization. She combined patch-clamp recordings in mouse brain slices with volume electron microscopy. For her postdoctoral studies, Enida joined Takaki Komiyama’s lab at the University of California, San Diego, where she currently investigates how different brain regions communicate and coordinate together in order to generate precise movements. She uses two-photon microscopy to image active neurons as mice perform motor behaviors.

    Prerequisites: Previous experience working in a lab or with mice or with data analysis is highly appreciated but not necessary.

    Eligibility: n/a

  • Ram Dyuthi Sristi (UCSD, Mishne Lab)plus--large

    Mentor: Ram Dyuthi Sristi
    Mentor Role: PhD Student
    Principal Investigator: Gal Mishne
    Institution: University of California, San Diego
    Location: La Jolla, CA
    Website: http://mishne.ucsd.edu/

    Project: Understanding the connectivity between brain regions is important, as it governs the way we think, eat, sleep, communicate and perform our everyday actions. However, the information flow between various interconnected brain regions remains largely unknown. This project aims to determine the functional role of neuronal pathways involving motor cortex and thalamus in driving specific subpopulations of the striatum. Working with their mentor, the SURF fellow will analyze calcium imaging recordings from these regions alongside behavioral video recordings of mice performing a specific motor task. The project plans to establish a map between the behavior and the neuronal activity which can then be used to map between the neuronal activity of different brain regions. In this computational role, the fellow will learn various machine learning techniques including autoencoders, recurrent neural networks, attention mechanisms and expand on an existing codebase in Python.

    Biography: Ram Dyuthi Sristi is a third year Ph.D. student in electrical engineering in Gal Mishne’s lab at the University of California, San Diego. Dyuthi’s interests lie in developing machine learning techniques that enhance the field of the medical domain. Currently, she is developing computational techniques to understand the interconnections between different brain regions. She enjoys teaching and believes that one has a strong conceptual understanding of a subject only when one can explain it to someone completely unaware of that subject. She has received exceptional feedback while working as a teaching assistant. Dyuthi completed her undergraduate studies at the Indian Institute of Technology Hyderabad. There she received an Institute Silver Medal for securing the highest overall CGPA in the Department of Electrical Engineering along with a Research Excellence Award and various recognitions and accolades in local and international competitions. Dyuthi is a classical dancer and always finds time for nature.

    Prerequisites: Required: experience coding in Python/MATLAB
    Preferred: experience with Python packages, such as numpy/scipy, Pytorch/Keras/Tensorflow

    Eligibility: n/a

  • Zhanqi Zhang (UCSD, Mishne Lab)plus--large

    Mentor: Zhanqi Zhang
    Mentor Role: PhD Student
    Principal Investigator: Gal Mishne
    Institution: University of California, San Diego
    Location: La Jolla, CA
    Website: http://mishne.ucsd.edu/

    Project: This interdisciplinary research project aims to build an unsupervised framework to address a need for quantification of undirected human behavior in open-field clinical assessment and analysis. It will combine computer vision, deep learning and probabilistic reasoning to extract spatiotemporal dynamics of human subjects from video. Furthermore, it will differentiate behavioral motifs and quantify hallmark features related to bipolar disorder. The principles of this approach are not specific to certain tasks or animal models. In the future, researchers plan to extend this framework to study and compare behavioral structure in other neuropsychiatric disorders, within or across species. Furthermore, by combining our methods with neural activity dynamics, they could further associate behaviors to neural recordings to gain more insights on how information processing maps onto behaviors, putting us one step closer to understanding the link between the brain and the mind.

    Biography: Zhanqi Zhang is a first-year Ph.D. student in computer science at the University of California, San Diego (UCSD), supported by the HDSI Ph.D. Fellowship. She is co-advised by Drs. Mikiio Aoi and Gal Mishne. Previously, she studied computer science and electrical engineering at Washington University in St. Louis. Zhanqi is broadly interested in how machine and biological intelligence complement each other to push forward the discoveries of the brain. Specifically, she is currently interested in how human behavior reflects complex neural and physiological processes such as learning, perception and decision-making in the brain. Using tools in machine learning, computational neuroscience, optimization and signal processing, she attempts to construct unsupervised animal and human action recognition and behavior classification models to understand disorders in clinical settings. Zhanqi is originally from a small town in China. Outside the lab, she enjoys hiking, watercolor painting, bird watching and cooking.

    Prerequisites: Required: Enrolled in an undergraduate program in electrical engineering, physics, computer science, mathematics, or related fields; coding experience using Python. Preferred: interest in video and image processing as well as machine learning.

    Eligibility: n/a

Seattle, WA

  • Leo Scholl & Pavitha Rajeswaran (UWashington, Orsborn Lab)plus--large

    Mentor: Leo Scholl and Pavithra Rajeswaran (Co-Mentorship)
    Mentor Role: Postdoctoral Fellow and Graduate Student
    Principal Investigator: Amy Orsborn
    Institution: University of Washington
    Location: Seattle, WA
    Website: http://faculty.washington.edu/aorsborn/

    Project: Vision is critical to guide our movements. Just imagine reaching for your coffee mug with your eyes closed. Interestingly, we actively choose where to look. Eye movements allow us to actively sample visual information and contribute to reaching computations in well-learned behaviors. This project asks whether eye movements also contribute to motor learning. The research team explores this by asking non-human primates to learn novel motor brain-machine interfaces (BMI), and then analyze interactions between eye movements and BMI, both at the level of behavior and in the brain. As part of the team, the SURF fellow will assist with real-time BMI experiments and refine methods for eye-tracking. The fellow will also use machine learning methods to identify latent interactions between eye movements, measures of arousal (e.g., pupil size), and BMI cursor control. The project is ideal training for someone interested in experimental and computational approaches in systems neuroscience, and novel ways to study learning.

    Leo Scholl Biography: Dr. Leo Scholl is a postdoctoral fellow at the Orsborn lab. His current work focuses on improving the usability of brain-machine interface (BMI) devices. He conducts optogenetics and BMI experiments in monkeys to investigate how brain structure influences learning. Leo received his Ph.D. in psychology from the University of California, Irvine. During graduate school, he built state-of-the-art viral tools for optogenetics in rodents to study visual processing.

    Pavithra Rajeswaran Biography: Pavi Rajeswaran is a Ph.D. student also working in the Orsborn lab to study how the brain learns new motor skills using BMIs. She is studying how rest and sleep helps the brain to correlate actions with outcomes. Pavi has a master’s degree in bioinstrumentation from the University of Illinois at Urbana-Champaign, where she developed virtual reality tools for medical education for surgeons.

    Prerequisites: To best participate in this project, fellows should have at least one year of coding experience (any language; Python preferred), and be interested in gaining additional coding expertise. For participation in the research at the Washington National Primate Research Center, fellows must be at least 18 years old.

    Eligibility: n/a

    Additional Mentorship Plan

Vienna, Austria

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

    Mentor: Ulises Rey
    Mentor Role: Postdoctoral Fellow
    Principal Investigator: Manuel Zimmer
    Institution: University of Vienna
    Location: Vienna, Austria
    Website: https://www.imp.ac.at/groups/manuel-zimmer/

    Project:Fasted C. elegans worms perform exploratory behavior to find food sources like bacteria. At the same time these 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. 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: As an undergraduate, Ulises Rey 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 doctoral degrees. To understand how neurons exchange information with each other, 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 at the University of Vienna, 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.

    Prerequisites: n/a

    Eligibility: n/a

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