Institute of Neuroscience Menu

Catch up on the latest ION research. Enjoy delicious food provided by Tasty Thai Kitchen, and great conversation provided by your fellow neuroscientists. Bring the family!

For more details & a link to RSVP please see the ion mailing list announcement.

Hello all!

Gender Inclusion in Neuroscience (GiiN) is hosting a Rotation 101 workshop on November 27th 10-11am in LISB 317! We will be discussing what is expected of a rotation talk and general tips on how to prepare and present. We welcome any and all first years to attend! If you would like to give a practice talk during the workshop, please send an email to ksuzuki@uoregon.edu. Snacks will be provided!

Glaser Lab website

Abstract: For an animal to perform any function, millions of cells in its body furiously interact with each other. Be it a simple computation or a complex behavior, all biological functions involve the concerted activity of many individual units. A theory of function must specify how to bridge different levels of description at different scales. For example, to predict the weather, it is theoretically irrelevant to follow the velocities of every molecule of air. Instead, we use coarser quantities of aggregated motion of many molecules, e.g., pressure fields. Statistical physics provides us with a theoretical framework to specify principled methods to systematically ‘move’ between descriptions of microscale quantities (air molecules) to macroscale ones (pressure fields). Can we hypothesize equivalent frameworks in living systems? How can we use descriptions at the level of cells and their connections to make precise predictions of complex phenomena? My research focuses on the theory, modeling, and analysis required to discover generalizable forms of scale bridging across species and behavioral functions. In this talk, I will present lines of previous and ongoing research that highlight the potential of this vision. I shall focus on two seemingly very different systems: mouse brain neural activity patterns, and octopus skin cells activity patterns. In the mouse, we reveal striking scaling behavior and hallmarks of a renormalization group- like fixed point governing the system. In the octopus, camouflage skin pattern activity is reliably confined to a (quasi-) defined dynamical space.  Finally, I will touch upon the benefits of comparing across animals to extract principles of multiscale function in biological systems, and discuss potential avenues of investigation that could allow us to decipher how macroscale properties, such as memory or camouflage, emerge from microscale level activity of individual cells. 

Leenoy Meshulam is a theory fellow at the University of Washington in Seattle, and a Burroughs Wellcome CASI awardee. Her research interests are at the intersection of physics, biology, and neuroscience. Dr. Meshulam received her Ph.D. from Princeton University in 2018. Prior to that, she completed her M.Sc. summa cum laude at Tel Aviv University in 2012 and graduated the Adi Lautman Interdisciplinary Honors Program for Outstanding Students.

Dr. Bower is a computational neurobiologist who received his PhD in neurophysiology at the University of Wisconsin Madison, and completed postdoctoral studies at New York University and the Marine Biological Laboratory in Woods Hole, Massachusetts. His first faculty appointment was at the California Institute of Technology (Caltech), where over 17 years he helped found the first graduate program in computational neuroscience. Subsequent to Caltech he was a professor of Computational Neuroscience within the University of Texas System.  

In addition to model and experimental based research on the olfactory system and cerebellum, Dr. Bower’s laboratory has also been involved in numerous infrastructure projects including constructing GENESIS, one of the first simulation platforms supporting detailed models of neuronal structures. Dr. Bower also founded the annual international conference on Computational Neuroscience, the first summer courses in computational neuroscience in the United States, Europe and Latin America, and the Journal of Computational Neuroscience. Dr. Bower was also involved in the re-emergence of neural networks, co- organizing the first NIPS meeting, (recently re-named NEurIPS), which has become one of the largest and most important annual meetings in Machine Learning.  Dr. Bower is the author of many scientific research articles and has edited and written numerous books.  While he is now officially retired, he remains an affiliate faculty member at Southern Oregon University and the University of Hertfordshire in the UK.

In addition to his work in science, Dr. Bower has also had a long-standing interest and involvement in science education and digital-based educational innovation. While a professor at Caltech, Dr. Bower founded and for 17 years co-directed the Caltech Precollege Science Initiative (CAPSI) a multi-million-dollar hands-on science education outreach program to majority minority school districts in the State of California. While supporting the introduction of hands-on science learning in public schools, Dr. Bower’s efforts in CAPSI also explored the use of digital technology in support of science learning. In 1998 Dr. Bower founded Numedeon Incorporated, which in 1999 launched Whyville.net the first game-based learning virtual world. Whyville now has over 8.5 million cumulative registered users worldwide. In 2014, Dr. Bower founded the company Virtual Worlds IP, intended to support the wider application of the simulation-game-based virtual world learning engine developed using Whyville. That technology is protected by a core and foundation patent in on-line virtual worlds (US patent # 7,925,703). 

This seminar has been postponed due to unforseen circumstances.  Please stay tuned for a rescheduled date for Dr. Saxena's visit! 

Abstract: Our ability to record large-scale neural and behavioral data has substantially improved in the last decade. However, the inference of quantitative dynamical models for cognition and motor control remains challenging due to their unconstrained nature. Here, we incorporate constraints from anatomy and physiology to tame machine learning models of neural activity and behavior.

How does the motor cortex achieve generalizable and purposeful movements from the complex, nonlinear musculoskeletal system? I will introduce a deep reinforcement learning framework that trains recurrent neural network controllers to generate purposeful movements in anatomically accurate macaque and mouse musculoskeletal models. This framework mirrors biological neural strategies and aids in predicting and analyzing novel movements. Next, I will discuss ongoing work on integrating region-specific constraints in models of the cortico-basal ganglia-thalamic loop during timing tasks to gain insights into pathway-specific computations. Through these projects, we show that a constraints-based modeling approach allows us to predictively understand the relationship between neural activity and behavior.

Bio: Shreya Saxena is broadly interested in the neural control of complex, coordinated behavior. She is currently an Assistant Professor of Biomedical Engineering at the Center for Neurocomputation and Machine Intelligence at the Wu Tsai Institute at Yale University. During Shreya’s postdoctoral research at the Center for Theoretical Neuroscience at Columbia University’s Zuckerman Mind Brain Behavior Institute, she developed machine learning methods for interpretable modeling of neural and behavioral data. Her PhD in the Department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology (MIT) dealt with performance limitations in sensorimotor control. Shreya received an M.S. in Biomedical Engineering from Johns Hopkins University, and a B.S. in Mechanical Engineering from the École Polytechnique Fédérale de Lausanne (EPFL). She is honored to have been selected as a Rising Star in both Electrical Engineering (2019) and Biomedical Engineering (2018).

Saxena Lab website

Yale Engineering profile for Shreya Saxena