Tag: seminar

May 2018 UWIN seminar: Short talks by David Perkel and Rajiv Saigal

David Perkel and Rajiv Saigal, the 2018 May UWIN seminar speakers.Please join us for the May 2018 UWIN seminar! This installment features a fascinating pair of short talks by UWIN faculty members David Perkel and Rajiv Saigal:

  • A simple microcircuit for generating neural variability to support vocal learning”
    David Perkel, Professor,  Departments of Biology and Otolaryngology, University of Washington
  • “Opportunities and Limitations of Neuroengineering approaches to CNS injury”
    Rajiv Saigal, Assistant Professor, Department of Neurological Surgery, University of Washington

The seminar is on Wednesday, May 9th, 2018, at 3:30pm in Husky Union Building (HUB) 337. Refreshments will be served prior to the talks.

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Abstracts:

A simple microcircuit circuit for generating neural variability for vocal learning” (David Perkel):

Songbirds, like humans, learn their vocalizations from other individuals using a trial-and-error process. We study the neural mechanisms underlying this ability as a model both for speech learning but also more generally as a model for reinforcement learning of complex motor skills. One requirement of trial-and-error learning is variability from trial to trial. Songbirds have a basal ganglia circuit that generates and rapidly modulates neural and behavioral variability, and we have identified experimentally a simple neural microcircuit that can contribute to generating variability. We have also used a highly constrained neural model to explore this possible mechanism for exploring acoustic space during vocal learning.

 

“Opportunities and Limitations of Neuroengineering approaches to CNS injury” (Rajiv Saigal):

Both traumatic brain and spinal cord injury (TBI and SCI) involve a primary mechanical trauma and well-elucidated secondary injury mechanisms. In spite of this knowledge and promising pre-clinical data, multiple clinical trials have failed to demonstrate benefit for human patients. This talk will review some of the clinical challenges and unmet needs for treating these complex injuries. There is a growing body of literature on engineering approaches for treating TBI and SCI. We will review promising approaches and opportunities for collaboration at UW.

 

April 2018 UWIN seminar: Joint seminar with the eScience Institute, talk by John Darrell Van Horn

John Darrell Van Horn, the 2018 April UWIN/eScience Institute seminar speaker.Please join us for the April 2018 UWIN seminar! This month’s installment is special joint seminar with the eScience Institute and features a talk by John Darrell Van Horn, Associate Professor of Neurology, University of Southern California:

“Making data science training resources FAIR”

The seminar is on April 11th, 2018, at 3:30pm in Physics/Astronomy Auditorium (PAA) A102.

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Abstract:

In our rapidly evolving information era, methods for handling large quantities of data obtained in biomedical research have emerged as powerful tools for confronting critical research questions. These methods are having significant impacts in diverse domains ranging from genomics, to health informatics, to environmental research, and beyond. The NIH’s Big Data to Knowledge (BD2K) Training Consortium, in particular, has worked to empower current and future generations of researchers with a comprehensive understanding of the data science ecosystem, giving them the ability to explore, prepare, analyze, visualize, and interpret Big Data. To this end, the BD2K Training Coordinating Center (TCC) was funded to facilitate in-person and online learning, and to open the concepts of data science to the widest possible audience. In this presentation, I will describe the activities of the BD2K TCC, particularly the construction of the Educational Resource Discovery Index (ERuDIte). ERuDIte identifies, collects, describes, and organizes over 10,000 data science training resources, including: online data science materials from BD2K awardees; open online courses; and videos from scientific lectures and tutorials. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required. In effect, data science is being used to inform training in data science where the so-called FAIR principles apply equally to these resources as well as to the datatypes and methods they describe. As a result, the work of the TCC has aimed to democratize novel insights and discoveries brought forth via large-scale data science training. This presentation will be of interest to anyone seeking to personalize their own data science education, craft unique online training curricula, and/or share their own online training content.

 

Bio:

Dr. Van Horn is an associate professor of neurology with additional appointments in neuroscience and in electrical engineering at the University of Southern California (USC) in Los Angeles, California. He received his bachelor’s degree in psychology from Eastern Washington University in Cheney, WA, a masters in electrical engineering and computer science from the University of Maryland, College Park, and his PhD from the University of London in the United Kingdom. He is an accomplished author (over 150 publications, h-index>45), university-level educator, and is known internationally as an expert in neuroinformatics and data sharing. He enjoys traveling, road cycling, mountaineering, is a private pilot, and lives in Los Angeles, CA, with his wife and two daughters.

 

March 2018 UWIN seminar: Short talks by Steve Perlmutter and Steve Brunton

Steve Perlmutter and Steve Brunton, the 2018 March UWIN seminar speakers.Please join us for the March 2018 UWIN seminar! This installment features a captivating duo of short talks by UWIN faculty members Steve Perlmutter and Steve Brunton:

  • Changes in Corticospinal Synaptic Strength Lead to Compensatory Changes in Cortical Neuron Firing. What’s the Feedback Signal?”
    Steve Perlmutter, Research Associate Professor, Department of Physiology & Biophysics, University of Washington
  • Learning physics and the physics of learning”
    Steve Brunton, Assistant Professor, Department of Mechanical Engineering, University of Washington

The seminar is on Wednesday, March 14th, 2018, at 3:30pm in Husky Union Building (HUB) 337.  Refreshments will be served prior to the talks.

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Abstracts:

“Changes in Corticospinal Synaptic Strength Lead to Compensatory Changes in Cortical Neuron Firing. What’s the Feedback Signal?” (Steve Perlmutter):

We are using activity-dependent electrical stimulation to induce synaptic plasticity in behaving non-human primates. Spinal stimulation triggered by corticomotoneuronal cell activity leads to increases in synaptic strength at the synapse to spinal motoneurons.  The activity pattern of the triggering cell changes after the conditioning in a compensatory manner.  The mechanism for this compensatory change is not clear, but suggests an unexpectedly tight feedback loop to precisely regulate cortical output to motoneurons.

 

Learning physics and the physics of learning” (Steve Brunton):

The ability to discover physical laws and governing equations from data is one of humankind’s greatest intellectual achievements.  A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power.  There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy.  With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts.  This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning.