Author: uwin

World’s lightest wireless flying robot created by UWIN faculty member Sawyer Fuller’s team

Size comparison between RoboFly, the lightest wireless flying robot, and a real fly

Size comparison between RoboFly and a real fly

UWIN faculty member Sawyer Fuller and his team have created what is to date the world’s lightest wireless flying robot. Weighing in at 190 mg, “RoboFly” is only slightly larger than an actual fly. The team also includes Vikram Iyer, Johannes James, Shyam Gollakota, and  Yogesh Chukewad. See the research paper here.

Currently, insect-sized flying machines need to be tethered in order to deliver the power required for flight (check out Fuller’s “RoboBee“). In order to circumvent this issue, RoboFly is powered by a laser beam using a photovoltaic cell. An on-board circuit boosts the seven volts generated by the cell to the 240 necessary to power the wings. The circuit also contains a microcontroller which controls the movement of the wings. “The microcontroller acts like a real fly’s brain telling wing muscles when to fire,” according to Vikram Iyer.

RoboFly, the lightest wireless flying robot, circuit

RoboFly’s flexible circuit. The copper coil and black boxes to the right comprise the boost converter, and the microcontroller is the small square box in the top right.

In the future, autonomous roboinsects could be used to complete tasks such as surveying crop growth or detecting gas leaks. “I’d really like to make one that finds methane leaks,” says Fuller. “You could buy a suitcase full of them, open it up, and they would fly around your building looking for plumes of gas coming out of leaky pipes. If these robots can make it easy to find leaks, they will be much more likely to be patched up, which will reduce greenhouse emissions. This is inspired by real flies, which are really good at flying around looking for smelly things. So we think this is a good application for our RoboFly.”

The team that created Robofly, the world's lightest wireless flying robot.

The Robofly team. Front row: Vikram Iyer (left) and Johannes James; back row (from left): Yogesh Chukewad, Sawyer Fuller, and Shyam Gollakota.

At the moment, RoboFly is only capable of taking off and landing, as there is no way for the laser beam to track the robot’s movement; but the team hopes to soon be able to steer the laser and allow the machine to hover and fly. Shyam Gollakota says that future versions could use tiny batteries or harvest energy from radio frequency signals. That way, their power source can be modified for specific tasks.

See a video below of the RoboFly in action!

RoboFly has received extensive publicity, see coverage by WIRED, The Economist, IEEE Spectrum, MIT Tech Review, TechCrunch, Discover Magazine, GeekWire, Popular Mechanics, Engadget, CNET, Digital Trends, Siliconrepublic, and SlashGear.

UWIN faculty members Steve Brunton and Kat Steele win 2018 UW College of Engineering Awards

UW College of Engineering Award winners Steve Brunton and Kat SteeleWe are excited to announce that two UWIN faculty members have won 2018 UW College of Engineering Awards! Steve Brunton won the Junior Faculty Award and Kat Steele received the Team Award as part of the Engineering Innovation in Health teaching team.  The College of Engineering Awards “acknowledge the extraordinary efforts of the college’s teaching and research assistants, staff and faculty members.” Congratulations to Steve and Kat!

Both have previously won UW College of Engineering Awards.  In 2017, Steve won the Faculty Award for Teaching; and in 2016, Kat  won the Junior Faculty Award.

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.


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.


UWIN fellows Kaitlyn Casimo and Karley Benoff named two of the 2018 Husky 100!

UWIN fellows Kaitlyn Casimo and Karley Benoff, both named to the 2018 Husky 100Congratulations to UWIN graduate fellow Kaitlyn Casimo and UWIN undergraduate fellow Karley Benoff, who were named two of the 2018 Husky 100!  Each year the Husky 100 award “recognizes 100 UW undergraduate and graduate students from Bothell, Seattle and Tacoma in all areas of study who are making the most of their time at the UW”.

Students named to the Husky 100 “actively connect what happens inside and outside of the classroom and apply what they learn to make a difference on campus, in their communities and for the future. Through their passion, leadership and commitment, these students inspire all of us to shape our own Husky Experience.”

UWIN fellow Kaitlyn Casimo, one of the 2018 Husky 100Kaitlyn Casimo, UWIN graduate fellow, says: “Besides developing programs in interactive, informal science learning to make science fun for kids and adults, I’m improving the way we tell stories about science on the stage and page. When I’m not doing research on patterns of connectivity in the human brain, I’m working to bring science to new audiences in innovative and accessible ways and to teach other scientists to do the same.”

Kaitlyn was awarded a UWIN graduate fellowship in 2014 and is a Ph.D. student in the Neuroscience program, where she is a member of Jeff Ojemann’s lab in Neurological Surgery.  She studies the electrophysiology of human resting state and task based brain connectivity, working with patients undergoing epilepsy surgery. She is especially interested in changes in connectivity related to brain-computer interface use and learning. Kaitlyn received a bachelor’s degree in Neuroscience from Pomona College, where she studied physiological responses to stress. She is a joint fellow of UWIN and the UW Computational Neuroscience Training Grant.  Kaitlyn  was named to the 2016 class of AAAS Emerging Leaders in Science & Society.

UWIN fellow Karley Benoff, one of the 2018 Husky 100Karley Benoff, UWIN undergraduate fellow, shares: “Throughout my Husky Experience, I have sought opportunities to empower people with all levels of mobility. This includes researching to develop and evaluate assistive devices, working in teams to tackle unmet clinical needs and helping establish HuskyADAPT, a student organization dedicated to improving and advocating for accessibility. As a 2018 graduate, I aspire to use my technical background and leadership experience to help advance healthcare technology.”

Karley was awarded a UWIN undergraduate fellowship in 2017 and is a Mechanical Engineering major working with Kat Steele in the ME Ability & Innovation Lab. Karley’s research focuses on designing and optimizing body-powered orthoses for individuals with neuromuscular deficits of the arm. She will test her device with participants using electromyography (EMG) signals to evaluate motor learning and user adaptation. Karley’s goal is have the final orthosis design be open source.

Kaitlyn and Karley join former UWIN undergraduate fellow Camille Birch, who was named one of 2017’s Husky 100.

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.


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.



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.


“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.

2018 Neural Computation and Engineering Connection Highlights

Poster for the 2018 Neural Computation and Engineering ConnectionThe 2018 Neural Computation and Engineering Connection (NCEC) was held on January 18-19, 2018.  This annual event brings together the UW neuroengineering and computational neuroscience communities to share and discuss new research and facilitate collaborations.  The event is sponsored annually by the UW Institute for Neuroengineering (UWIN), the Center for Sensorimotor Neural Engineering (CSNE), and the UW Computational Neuroscience Center.  This year’s connection drew 140 attendees and was highlighted by five invited keynote lectures, four talks by local UW faculty, two talks by senior UWIN postdoctoral fellows, seven talks by senior graduate and undergraduate students, a series of lightning talks by new graduate students and postdoctoral fellows, and a poster session.  A huge thank you to all those who attended and participated!

Day 1: Thursday, January 18, 2018

Poster Session

Day 1 of NCEC kicked off with a poster session over lunch.  20 UW faculty members and students presented their work on a variety of neural engineering and computational neuroscience topics.

(Click on thumbnails to see full-size images)

Poster session at the 2018 Neural Computation and Engineering ConnectionPoster session at the 2018 Neural Computation and Engineering ConnectionPoster session at the 2018 Neural Computation and Engineering ConnectionPoster session at the 2018 Neural Computation and Engineering Connection


Keynote lecture: “Engineering Haptic Illusions”

Allison Okamura, Professor, Department of Mechanical Engineering, Stanford University

In the first keynote lecture, Allison Okamura spoke about her work with haptics, the study of human touch sensing.  She provided examples of situations in which haptic feedback can be beneficial when the sense of touch is absent or has been lost: in robot-assisted surgery, with prostheses, for manipulation of robots/tools in dangerous situations (e.g., in space, or mine diffusion).  Okamura discussed a few of the many ways her lab focuses on haptics:

  • Design and control of haptic systems: When does it make sense to provide haptic feedback rather than visual feedback?
  • How haptic feedback can optimize robot-assisted minimally invasive surgery.
  • Wearable haptics, to be used by stroke patients during exercise(s).

Allison Okamura speaks at the 2018 Neural Computation and Engineering Connection

Allison Okamura speaks at the 2018 Neural Computation and Engineering Connection







“Sparse sensing by arrays of wing mechanosensors for insect flight control”

Thomas Mohren, UWIN graduate fellow, S. Brunton and Daniel labs

Mohren seeks to answer the question of how insect mechanosensors work to sense things; specifically, how distributed sensors combine information to assess rotational movement. He uses a computational model of a moth wing, building in computations for flapping and rotation in an attempt to distinguish flapping vs. flapping + rotation.


“Synaptic specialization and convergence of visual channels in the retina”

Phil Mardoum, UWIN graduate fellow, Rieke and Wong labs

Mardoum asks if signals from rods and cones in the retina are filtered differently; specifically, if postsynaptic receptor mechanisms differ between rods and cones.  He records from bipolar cells in zebrafish while exposing the fish to lights of different wavelengths and/or disrupting cell signaling in the retina.


“Visual learning and processing in the honeybee, Apis mellifera

Claire Rusch, UWIN graduate fellow, Riffell lab

Rusch asks how honeybees are capable of complex learning and behavior with so few neurons in their brains (1 million neurons total in the honeybee brain, 170,000 in the mushroom body, the center of learning/memory).  She records activity from the honeybee brain and assesses how learning changes neural activity.


“Engineering direct cortical stimulation in humans”

David Caldwell, UWIN/BDGN graduate fellow, Rao and Ojemann labs

Caldwell’s goal is to enhance neural connectivity through electrical cortical stimulation.  He looks at the behavioral and neural effects of cortical stimulation in humans, working with patients implanted with electrocorticographic (ECoG) grids in preparation for epilepsy surgery.


“Simulating Axon Health’s Impacts in the Setting of Cochlear Implants”

Jesse Resnick, Computational Neuroscience Training Grant graduate fellow, Rubinstein lab

Even with cochlear implants (CIs), some hearing tasks can still be challenging for people who have lost their hearing: localizing the source of a sound and speech recognition in noisy environments.  These tasks require assessing the time difference in sound arrival between the ears (Interaural Time Difference, ITD).  By building a computational model of demyelination in auditory neurons, Resnick is working to asses if variability in demyelination severity contributes to ITD detection ability.


“Rod-cone flicker cancellation: retinal processing and perception in intermediate light”

Adree Songco-Aguas, Computational Neuroscience Training Grant undergraduate fellow, Rieke lab

The rods and cones in our retina make vision seamless across day and night.  Songco-Aguas is interested in how the retina behaves in intermediate light, assessing how retinal ganglion cells respond when photoreceptors are stimulated with intermediate light levels.


“Spatiochromatic integration by V1 double opponent neurons”

Abhishek De, Computaional Neuroscience Training Grant graduate fellow, Horwitz lab

De addresses the question of how color is processed spatially.  Specifically, he asks how neurons in area V1 of the visual cortex combine color signals across their spatial fields.  He records extracellularly from V1 neurons in primates to see how neurons process color across receptive fields.


Day 2: Friday, January 19, 2018

Panel Discussion on Ethics in Neuroscience.

Day 2 started with breakfast and a lively discussion of ethics in neuroscience by a panel composed of UW and visiting faculty.  The panel began by discussing effective collaboration between theorists and experimentalists, particularly as it pertains to authorship on publications.  The session then shifted to a discussion about our responsibilities as scientists in presenting results to the public.  There were discussions about balancing highlighting the novelty and general interest of research vs. realism about data, especially given the pressure for needing exciting results to generate funding.

(Seated from left to right are: Adrienne Fairhall (UW), Rafael Yuste (Columbia), Beth Buffalo (UW), David Perkel (UW), Tom Daniel (UW), Eric Shea-Brown (UW), Michael Berry (Princeton))

Neuroscience ethics panel at the 2018 Neural Computation and Engineering Connection





“Behavioral Implementation of Mnemonic Processing”

Sheri Mizumori, Professor, UW Department of Psychology

Mizumori addresses the question of how the hippocampus influences behavior; in other words, how memory drives behavior choice.  During her lecture, she spoke about possible connectivity routes in the brain related to the question, and discussed her research showing the lateral habenula’s (LHb) role in reinforcement learning.  Her research provided evidence that the hippocampus and LHb are part of the same memory-driven system, linked to a theta-generating network; and that the LHb is involved in the same flexible behaviors the hippocampus is involved in.


“Optogenetic stimulation leads to connectivity changes across sensorimotor cortex in non-human primates”

Azadeh Yazdan, Washington Research Foundation Innovation Assistant Professor of Neuroengineering, UW Departments of Bioengineering and Electrical Engineering

One focus of Yazdan’s lab is the development of efficient stimulation-based therapies for stroke.  She developed a large-scale interface for optogenetics in non-human primates, and is looking at how this technique can be used to change functional connectivity between somatosensory and motor cortices.  Her future work in stimulation-based stroke therapies will focus on three components: mechanisms of stimulation-induced plasticity, stroke studies in non-human primates, and large-scale interfaces.

Azadeh Yazdan speaks at the 2018 Neural Computation and Engineering Connection





Keynote lecture: “Emergence of dynamically reconfigurable hippocampal responses by learning to perform probabilistic spatial reasoning”

Ila Fiete, Associate Professor, Department of Neuroscience, UT Austin

Fiete’s goal is to learn how the brain computes and to better understand neural coding and dynamics.  She asks how neural computation unfolds over time, and how neural responses underlie the computations that are performed.  One difficult problem her lab considers is how to simultaneously know where you are in the environment, and also build a map of where you are.  Her lab trained a neural network to solve these problems, to report an accurate estimation of position in complex tasks.


Keynote lecture & Robotics Colloquium: “Robotic-assisted movement training after stroke: Why does it work and how can it be made to work better?”

David Reinkensmeyer, Professor, Departments of Mechanical and Aerospace Engineering, Anatomy and Neurobiology, and Biomedical Engineering, UC Irvine

Robot-assisted stroke therapy can provide improvements to stroke patients, but there is high variability.  By what mechanisms of plasticity or motor learning does robot-assisted therapy work? To answer this question, Reikensmeyer’s lab is building computational models of recovery after stroke, testing and refining them with data from patients receiving robotic-assisted therapy.  During his lecture, he showed examples of various types of robotic-assisted therapy, including rehabilitation devices available to the public.

David Reinkensmeyer speaks at the 2018 Neural Computation and Engineering Connection




“Info in a bottleneck”

Gabrielle Gutierrez, UWIN postdoctoral fellow, UW Applied Mathematics Department

Gutierrez asks how signal processing in the retina preserves image information.  In the retina, many bipolar cells converge onto one retinal ganglion cell (RGC).  She looked at response functions between bipolar cells and RGCs, finding that many bipolar cells converging on one RGC helps with noise processing.


“Modeling the perceptual experience of retinal prosthesis patients”

Michael Beyeler, UWIN postdoctoral fellow, UW Psychology Department & the UW eScience Institute

Retinal prostheses can restore some sight to individuals who have lost vision due to conditions such as macular degeneration and retinitis pigmentosa.  Beyeler asks what people with retinal prostheses actually see; visual perception with a retinal prosthesis is highly distorted relative to normal vision.  He is working on a computational model that predicts what people with retinal prostheses will actually see, using data from patients with retinal prostheses.  His ultimate goal is to use the model to improve and optimize the stimulation protocol for these prostheses to improve what patients actually see.


Keynote lecture: “Neural substrates of prospection”

Loren Frank, Professor, Kavli Institute for Fundamental Neuroscience, Department of Physiology, UC San Francisco

One role played by memories is their use in decision-making.  Frank discussed his lab’s work using large-scale recording techniques to record activity in the hippocampus, as well as other areas of the brain, to relate patterns of activity to the basic cognitive functions of the structure.  He shared research showing neural activity in the hippocampus was not just a representation of past locations, but also representation of possible future paths in space; recorded neural activity guides future behavior.


“Interfaces to monitor and manipulate large-scale neural circuits in primates”

Amy Orsborn, Clare Boothe Luce Assistant Professor, UW Electrical Engineering & Bioengineering Departments

Brain-machine interfaces (BMIs) can restore motor abilities; individuals who are paralyzed or missing a limb can control robotic limbs through motor cortex activity in their brain.  Orsborn studies motor learning in order to improve BMIs.  She discussed research working to improve the algorithms used to map neural activity onto control of robotic devices in BMIs.  Orsborn also talked about tools she is developing to study neural networks across multiple spatial scales.


“Dynamic Mechanisms underlying rhythm generation in cortical and brainstem microcircuits”

Nino Ramirez, Professor, UW Neurological Surgery Department, Director, Center for Integrative Brain Research, Seattle Children’s Research Institute

Ramirez discussed microcircuits in the brain that establish rhythms for breathing, and specifically presented work on the role of inhibition and excitation in breathing mechanisms.  Understanding these mechanisms has implications for understanding conditions associated with disordered respiratory rhythms.


Keynote lecture: “Circuit basis for behavioral flexibility”

Takaki Komiyama, Associate Professor of Neurobiology and Neurosciences, UC San Diego

Komiyama studies how circuits of neurons allow for behavioral flexibility; noting how amazing the flexibilities of the brain and our behavior are.  He addresses the questions of what circuits are involved in motor learning over many repetitions and practice, and if the relationship between brain activity and movement is stable.  Komiyama uses wide-field calcium imaging to assess how brain activity changes with motor 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.


“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.