Category: Seminars (Page 1 of 3)

February 2019 UWIN seminar: talk by Adam Calhoun

February  2019 UWIN speaker Adam Calhoun

The February 2019 UWIN seminar features a talk by visiting speaker Adam Calhoun, who is a Postdoctoral Fellow in the Princeton Neuroscience Institute at Princeton University. His talk is titled “Quantitative methods to identify behavioral states”.

The seminar is on Wednesday, February 13, 2019 at 3:30 in Husky Union Building (HUB) 337. Refreshments will be served prior to the talk.

Abstract:

Animals must flexibly alter their responses to stimuli according to changing internal needs or behavioral contexts. This source of behavioral variability is often ignored because we lack of methods that are able to identify the changing internal state of an animal. To address this gap, we have developed a novel unsupervised method to identify internal states and have applied it to the study of a dynamic social interaction. During courtship, Drosophila melanogaster males chase and sing to females and, in a manner analogous to human conversation, the structure of their songs is actively patterned by interactions with the female. We identify the internal states of the male use this new model to identify neural correlates of state switching. Our results reveal how animals compose behavior from previously invisible states, a necessary step for quantitative descriptions of animal behavior that link environmental cues, internal needs, neuronal activity, and motor outputs.

January 2019 UWIN seminar: talk by Guillaume Lajoie

January 2019 UWIN Seminar speaker Guillaume Lajoie

The first UWIN seminar of 2019 features a talk by visiting speaker Guillaume Lajoie from Université de Montréal’s Department of Mathematics and Statistics. The talk is titled “Successful learning in artificial networks thanks to individual neuron failure”.

Guillaume is an Assistant Professor in the Department of Mathematics and Statistics at the Université de Montréal, and is also an Associate Member of Mila, the Quebec Institute for Learning Algorithms. We are especially excited to welcome Guillaume back to UW as he was previously a UWIN postdoctoral fellow!

The seminar is on Wednesday, January 9, 2019 at 3:30 in Husky Union Building (HUB) 337. Refreshments will be served prior to the talk.

Abstract:
This talk will outline work in progress. Not unlike the brain, artificial neural networks can learn complex computations by extracting information from several examples of a task. Typically, this is achieved by adjusting the parameters of the network in order to minimize a loss function via gradient descent methods. It is known that introducing artificial failure of single neurons during a deep network’s training, a procedure known as dropout, helps promote robustness. While dropout methods and variants thereof have been successfully employed in a variety of contexts, their effect is not entirely understood, and relies on stochastic processes to select which units to drop. Here, I will discuss two methods designed to purposely select which units would best benefit learning if dropped or temporarily modified, based on their tuning, activation and the current network state: The first method is aimed at improving generalization in deep networks, and the second combats gradient exploding and vanishing in recurrent networks, when learning long-range temporal relations. While gradient descent methods for artificial networks are not biologically plausible, I will discuss how relationships between neural tuning and failure during training can inform exploration of learning mechanisms in the brain.

December 2018 UWIN seminar: Short talks by Tom Daniel and Chris Rudell

December 2018 UWIN Seminar Speakers Tom Daniel and Chris RudellPlease join us for the December 2018 UWIN seminar! This seminar features a pair of short talks by UWIN faculty members Tom Daniel and Chris Rudell:

  • Engineering Odor Guided Flight”
    Tom Daniel, Professor, Department of Biology, University of Washington
  • Highly-Integrated Neural Stimulation Electronics for Bidirectional Brain-Computer Interfaces (BBCI) including Artifact Cancellation”
    Chris Rudell, Associate Professor, Department of Electrical and Computer Engineering, University of Washington

The seminar is on Wednesday, December 12, 2018 at 3:30pm in Husky Union Building (HUB) 337. Refreshments will be served prior to the talks.

Abstracts:

“Engineering Odor Guided Flight” (Tom Daniel):

The capacity for animals to localize odor sources far exceeds what can be manufactured today. In part, this extraordinary capacity is due to the behavioral mechanisms animals use and in part to the neural machine they deploy. This talk will review past work in odor localization and then continue to a neuro-integrated system that draws on the unparalleled sensory capabilities of animals. It is also possible I may change my mind and talk about something else.

 

“Highly-Integrated Neural Stimulation Electronics for Bidirectional Brain-Computer Interfaces (BBCI) including Artifact Cancellation” (Chris Rudell):

Miniaturization of neural stimulation and recording electronics is a key obstacle to the vision of using in vivo Bidirectional Brain Computer Interfaces (BBCI) for neuromodulation. This presentation will highlight techniques enabling integration of BBCI systems in single chip form. Specifically, our group has focused on integrating stimulation electronics using low-voltage digital CMOS to achieve a reliable high-voltage compliant (+/-12V) single-chip stimulator. The chip is capable of delivering a Biphasic Current Pulse of up to 2mA into a broad range of electrode impedances, from purely resistive to capacitive. The presentation will conclude with the description of a recently fabricated BBCE chip. A product of joint collaborative efforts, this 2mm x 2mm single chip integrates a 64-channel neural recording front-end with 4-stimulation channels and both differential- and common-mode artifact cancellation in a 65nm TSMC process.

November 2018 UWIN Seminar: Joint seminar with the eScience Institute, talk by Reza Hosseini Ghomi

Reza Hosseini Ghomi, the November 2018 UWIN seminar speaker The November 2018 UWIN seminar is a special joint seminar with the eScience Institute! The seminar will be given by Reza Hosseini Ghomi, a Senior Fellow in the Department of Neurology at the University of Washington, and the Chief Medical Officer of NeuroLex Laboratories.  He will be speaking on:

Digital Biomarkers: Do they hold promise for better neuropsychiatric disease detection?

The seminar is on Wednesday, November 14th, 2018, at 3:30pm in Health Science Building (HSB) K-069. Refreshments will be served prior to the talk.

Abstract:

For this talk I would like to review the field of digital biomarkers and provide some background and context for our work. Specifically, what are digital biomarkers and how are they useful? I will show some results of our early work using recorded voice samples, accelerometer data, neuroimaging measures, and several other objective and subjective measures from patients with Parkinson’s, Depression, Schizophrenia, and from the Framingham Heart Study’s cognitive aging cohort. We will touch on the shifting paradigm of research to complete work in this area of big data and what we can do differently moving forward to offer novel insights.

Biography:

Reza’s passion lies at the intersection of neuropsychiatry, technology, and education. He is most interested in bringing significant and measurable improvement to the screening, diagnosis, and treatment of neuropsychiatric illness through the advancement of technology, and empowerment through collaboration.

To that end, when he is not practicing neuropsychiatry, he is director of the DigiPsych Lab and chief medical officer for NeuroLex Laboratories where his research and development work focuses on the exciting new field of voice diagnostics – using a brief recording of voice to screen, diagnose, and track a wide range of illnesses in an ultra-rapid, cost-effective, accurate, and accessible way.

Drawing on his previous experience as an engineer – he develops imaging technology at Massachusetts General Hospital and an electronic health record for VecnaCares. He is also a founding partner of Stanford Brainstorm, the first behavioral health innovation and entrepreneurship laboratory.

He holds a BS in electrical and computer engineering from Rensselaer Polytechnic Institute, an MSE in biomedical and electrical engineering from Johns Hopkins University, and an MD from University of Massachusetts Medical School, and is now completing and transitioning from the University of Washington’s psychiatry residency to their neurology movement disorders fellowship to focus on neurodegenerative disease

October 2018 UWIN seminar: Short talks by Howard Chizeck and Bill Moody

Howard Chizeck and Bill Moody will give short talks at the October 2018 UWIN seminarThe UWIN seminar series resumes for the 2018-19 academic year!  The October 2018 UWIN seminar features an exciting pair of short talks by UWIN faculty members Howard Chizeck and Bill Moody:

  • “Challenges in Optimizing Deep Brain Stimulation”
    Howard Chizeck, Professor, Department of Electrical & Computer Engineering, University of Washington
  • “Trans-skull imaging of brain activity in neonatal mice during spontaneous sleep-wake cycles”
    Bill Moody, Professor, Department of Biology, University of Washington

The seminar is on Wednesday, October 10, 2018 at 3:30pm in Health Sciences Building (HSB) G-328.  Refreshments will be served prior to the talks.

Abstracts:

“Challenges in Optimizing Deep Brain Stimulation” (Howard Chizeck):

Deep Brain Stimulation is an approved treatment for Parkinson’s Disease and essential tremor, and is under investigation at various institutions for several other neurological conditions. New devices make it possible to optimally select stimulation parameters for currently approved “open loop” treatments, and to implement closed loop algorithms that adjust stimulation “on the fly,” so as to address tradeoffs between symptom management and side effects. Recent results that we have obtained will be briefly described, and current challenges will be described.

 

“Trans-skull imaging of brain activity in neonatal mice during spontaneous sleep-wake cycles” (Bill Moody):

Widely propagating waves of electrical activity occur throughout the brain during early development, where they provide long- and short-range synchrony in neuronal activity that helps to establish cortical circuitry. Neuronal activity that is synchronized over large distances also occurs during adult slow-wave sleep and serves a central role in memory consolidation. Using trans-skull optical imaging of brain activity in neonatal mice, combined with power spectral analysis of EMG activity to measure sleep-wake cycles and dimensionality reduction methods to analyze the spatio-temporal patterns of brain activity, we have discovered that pan-cortical waves of activity, which had previously been thought to occur during all behavioral states in the developing brain, are in fact already segregated into sleep cycles by the end of the first postnatal week. Our results suggest that pan-cortical waves of activity in development may establish the long-range neuronal circuitry that is used in adult sleep to consolidate events experienced during wakefulness into long-term memory.

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.

February 2018 UWIN seminar: Short talks by Chantel Prat and Eric Shea-Brown

Chantel Prat and Eric Shea-Brown, the 2018 February UWIN seminar speakers.Please join us for the February 2018 UWIN seminar! This installment features a fascinating pair of short talks by UWIN faculty members Chantel Prat and Eric Shea-Brown:

  • “Neurometrics: Resting-state qEEG Predicts Second Language (L2) Learning as well as a Standardized Language Aptitude Test”
    Chantel Prat, Associate Professor, Department of Psychology, Institute for Learning & Brain Sciences, University of Washington
  • “Linking the statistics of network activity and network connectivity”
    Eric Shea-Brown, Assistant Professor, Department of Physiology & Biophysics, University of Washington

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

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Abstracts:

“Neurometrics: Resting-state qEEG Predicts Second Language (L2) Learning as well as a Standardized Language Aptitude Test” (Chantel Prat):

Decades of research using fMRI and EEG have shown that properties of network-level brain functioning at rest can be used to characterize individual differences in a variety of cognitive abilities. My current work explores the predictive utility of various characterizations of brain function using quantitative EEG (qEEG), resting-state fMRI, structural MRI, task-related fMRI, and psychometric tests of cognitive abilities for understanding individual differences in L2 learning. In the current talk, I’ll describe a study showing that 5 minutes of eyes-closed resting-state qEEG data can predict L2 learning as well, or better, than a standardized language aptitude test that takes a bit over an hour to administer. Future directions include the development and testing of neurometric assessment tools for predicting subsequent complex behaviors.


“Linking the statistics of network activity and network connectivity” (Eric Shea-Brown):

There is an avalanche of new data on the brain’s activity, revealing the collective dynamics of vast numbers of neurons. In principle, these collective dynamics can be of almost arbitrarily high dimension, with many independent degrees of freedom — and this may reflect powerful capacities for general computing or information. In practice, datasets reveal a range of outcomes, including collective dynamics of much lower dimension — and this may reflect the structure of tasks or latent variables. For what networks does each case occur? Our contribution to the answer is a new framework that links tractable statistical properties of network connectivity with the dimension of the activity that they produce. I’ll describe where we have succeeded, where we have failed, and the many avenues that remain.

November 2017 UWIN seminar: Short talks by Sawyer Fuller and Wyeth Bair

Sawyer Fuller and Wyeth Bair, the November 2017 UWIN seminar speakers.The November 2017 UWIN seminar features a fascinating pair of short talks by UWIN faculty members Sawyer Fuller and Wyeth Bair:

  • “Fly-inspired visual flight control of insect-sized robots using wind sensing”
    Sawyer Fuller, Assistant Professor, Department of Mechanical Engineering, University of Washington
  • “Comparing shape representation in mid-level visual cortex to that in a deep convolutional neural network”
    Wyeth Bair, Associate Professor, Department of Biological Structure, University of Washington

The seminar is on Wednesday, November 8th, 2017 at 3:30pm in Husky Union Building (HUB) 337.  Refreshments will be served prior to the talks.

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Abstracts:

“Fly-inspired visual flight control of insect-sized robots using wind sensing” (Sawyer Fuller):

In the Autonomous Insect Robotics Laboratory at the University of Washington, one of the projects we are interested in is how to give robots the size of a honeybee the ability to fly themselves autonomously. Larger drones can now do this task, but they use sensors that are not available in insect-sized packages, like the global positioning system and laser rangefinders. We are left with sight, the same modality used by flies. But visual processing is typically computationally intensive. I will describe research I performed on flies that reveals that they overcome this by combining slow feedback from vision with fast wind feedback, and discuss ramifications for our robots.

 

“Comparing shape representation in mid-level visual cortex to that in a deep convolutional neural network” (Wyeth Bair):

Convolutional neural nets (CNNs) are currently the best performing general purpose image recognition computer algorithms.  Their design is hierarchical, not unlike the neural architecture of the ventral visual pathway in the primate brain, which underlies form perception and object recognition.  We examined whether units within an implementation of “AlexNet” (Krizhevsky et al., 2012), following extensive supervised training, end up showing selectivity for the boundary curvature of simple objects in an object-centered coordinate system, similar to that found for neurons in the mid-level cortical area V4 (Pasupathy and Connor, 2001).  I will show how the units in AlexNet compare to those in V4 in terms of shape-tuning and translation invariance and will discuss the benefits and limitations of comparing complex artificial neural networks to the brain.

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