Poster for 2019 Neural Computation and Engineering Connection

The 2019 Neural Computation and Engineering Connection (NCEC) was held on January 24-25, 2019. This annual event brings together the UW neuroengineeirng and computational neuroscience communities to share and discuss new research and facilitate collaborations. The event is sponsored annually by the UW Institute of Neuroengineering (UWIN), the Center for Neurotechonolgy (CNT), and the UW Computational Neuroscience Center.

This year’s connection drew 160 attendees, with a multitude of talks: four by invited keynote lecturers, four from local UWIN faculty, three talks by senior UWIN/Swartz postdoctoral fellows, and seven talks by senior UWIN and Computational Neuroscience graduate students. Alongside these talks, there were a series of lightning talks by new graduate students and postdoctoral fellows, a poster session, and an ethics panel. Thank you to all who attended and participated!

Day 1: Thursday, January 24, 2019

Poster Session

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

“Subject-specific quantification and acceleration of motor learning “

Momona Yamagami, UWIN Graduate Fellow, Steele and Burden labs

Yamagami looks to improve treatment of individuals with neurologic injury by analyzing how predictive (feedforward) and reactive (feedback) controllers change as individuals perform a manual trajectory tracking task.

“Electrophysiological Correlates of Feedback Learning in Humans”

Patrick Rice, Computational Neuroscience Graduate Fellow, Stocco lab

Rice investigates how learning and behavior in target discrimination tasks can be improved through the use of noninvasive neuromodulation of various event-related potentials, such as error- and feedback- related negativity. With this investigation in progress, plans were discussed to apply this research to deeper cortical regions of the brain through trans-cranial magnetic stimulation.

“Continuous long-term recording of sleep in a mouse model of epilepsy”

Raymond Sanchez, UWIN Graduate Fellow, de la Iglesia lab

Sanchez addresses issues with manual long-term sleeps studies, which are often time-consuming and error-prone, by working to develop sleep-stage classification algorithms that can parse the stages of rest. The algorithm currently can detect REM sleep well, and is continuing to be worked on with an increased training set size.

“Does the mouse see the world like an artificial neural network?”

Iris Shi, Computational Neuroscience Graduate Fellow, Shea-Brown and Buice labs

Shi’s goal is to predict single neuron responses through an artificial neural network and to compare the responses that occur in a mouse’s visual cortex, in hopes of gaining insights into how the mouse visual cortex processes natural visual stimuli.

“What do patients with retinal prostheses actually see?”

Ezgi Irmak Yücel, UWIN Graduate Fellow, Fine and Rokem Lab

Yucel looks to discover the experiences of retinal prostheses patients through a series of manipulations of stimulation protocols, in the process solidifying a few problems in creating reproduceable percepts. One of these problems revolves around differences in retinal structure which causes variation in electrode stimulation, producing a variety of different shapes which are inconsistent from patient to patient.  

“Concurrent measurement of olfactory information and behavior tracking during odor-guided navigation”

Mohammad Tariq, UWIN Graduate Fellow, Gire and Perkel labs

Tariq works to study how dynamically varying odor flow guides decision making in animals who rely on odor as a method of navigation. He uses a network of low-cost, lightweight sensors to monitor olfactory information in real time, which is then combined with behavioral tracking of animals.

Investigating Neural Synchrony Within the Monkey Hippocampus”

Aaron Garcia, UWIN Graduate Fellow, B. Brunton and Buffalo labs

Garcia expands the investigation of simultaneous neural firing from rodents to primates in an attempt to determine the mechanisms of memory formation and navigation in primates. This effort revolves around applying non-traditional analysis methods on recorded local field potential from the primate hippocampus.

Keynote Lecture: “He can be a normal child; he couldn’t do that before.”: The potential for therapy-directed neurorecovery after pediatric-onset SCI

Andrea Behrman, Professor, Neurological Surgery, University of Louisville

In the first keynote lecture, Berhman investigates the status of neurotherapeutic interventions and how the current state of therapies based on activity-dependent plasticity has altered the trajectory of outcomes with acquired spinal cord injuries. By using locomotion training with patients, even if the patient cannot make certain movements voluntarily, the neuromuscular system can learn the correct motor patterns and the spinal cord nerves can learn to fire to encourage voluntary movement. These interventions can decrease scoliosis severity, improve respiratory function, improve ability to sit, and improve trunk control, and with continued research can perhaps reach even further.

Panel Discussion of Ethics in Neuroscience

Day 1 ended with a discussion on the topic of “what are our responsibilities as scientists at the interface of neuroscience, computation, and engineering?” The panel covered many different points including managing data and protecting patient privacy, considering the place neurointerventions have in society, as well as the separation between restoring function and enhancing function.

Panel Speakers (left to right): Mitra Hartmann, Andrea Behrman, Eric Shea-Brown, Abby Person, Maryam Shanechi, Michael Berry, David Perkel, Loren Frank. Panel chair (not pictured): Adrienne Fairhall.

2019 Neural Computation and Engineering Connection Panel

Day 2: Friday, January 25, 2019

Keynote Lecture: “Neural Decoding and Control of Mood to Treat Neuropsychiatric Disorders”

Maryam Shanechi, Assistant Professor, Electrical Engineering, University of Southern California

2019 Neural Computation and Engineering Connection Speaker

Shanechi works to model and decode mood variations in an effort to create future closed loop therapies which would have the ability to read the brains signal, encode this bio-signal to an electrical one, provide some electrical stimulation as input, and continue to self-modulate to monitor and manage mood. Her current investigations work with creating a model to characterize the bio-signal output by the brain as well as to identify the locations in the brain that are most predictive of mood.

“Advancing Neuroscience through Next-Generation Optical Tools”

Andre Berndt, Assistant Professor, Bioengineering, University of Washington

Berndt designs a new method of optimizing new sensor proteins in order to streamline a resource and time-intensive task of creating tools that have the potential to provide data at high spatial and temporal resolution. Through a recursive high throughput method consisting of random mutagenesis, ensuring individual sensor expression, screening in microarrays, and DNA sequencing of the best performing sensor, Berndt believes the creation of these sensor proteins will be more efficient.

“Context-Adaptive Neural Models of Language”

Mari Ostendorf, Professor, Electrical & Computer Engineering, University of Washington

Ostendorf looks to transfer the fluid language adaptation of humans to computational modeling of language through the incorporation of modified variables into the neural network model of language. These variables include changing discrete variables to continuous, allowing for words to be related to each other and characterize similarity easier, as well as various methods of a weighted variable of bias, which allows for multiple contexts to be considered at once.

“Bioinspired multifunctional mobility”

Tom Libby, Washington Research Foundation Postdoctoral Fellow in Neuroengineering, Electrical & Computer Engineering and Biology, University of Washington

Libby works to improve robotics through incorporating the understanding of animal’s complex interactions between behavior and morphology. He emphasizes multi-functionality exhibited in animals and emphasized the need for multi-functionality in robotics in order to reduce the need for isolated movements and highly calculated encounters.

Decoding motor control to improve movement in cerebral palsy”

Kat Steele, Assistant Professor, Mechanical Engineering, University of Washington

Steel examines whether altered motor control limits potential improvements in movement patterns of individuals with cerebral palsy and the implications for their clinical care. Using a simulation model which displays optimal gait for varied synergy levels – varying the ability to co-activate certain muscle groups and display fine motor control – which at the lowest levels optimized to movement patterns similar to those in individuals with cerebral palsy. The lowered synergy levels lowers the ceiling on recovery as even after surgery and rehabilitation, synergy spaces is hard to modify, although in the future, therapy may focus specifically on expanding synergy space.

Keynote Lecture: “Insights into cerebellar control of movement”

Abigail Person, Assistant Professor, Physiology and Biophysics, University of Colorado Denver

Person investigates how cerebellar damage leads to dysmetric movement through experiments defining the relationship between cerebellar activity and limb reaching movement in freely behaving mice. The experimentation supports the idea that the signaling by Purkinje neurons are used for the feedforward control of the reaching limb.

Signatures and mechanisms of low-dimensional neural predictive manifolds.”

Stefano Recanatesi, Swartz Postdoctoral Fellow, Physiology & Biophysics, University of Washington

Recanatesi looks at ability of recent neural networks to solve sequential processing tasks using predictive modeling and uses a recurrent artificial neural network model to investigate if the ability of the hippocampus to guide sequential planning. The recurrent neural network was trained with predictive learning on a simulated spatial navigation task and resulted in a series of nonlinearly modified inputs which captured the structure of the environment, highlighting the predictive aspect of neural representations.

“From the wet lab to the web lab”

Anisha Keshavan, Washington Research Foundation Postdoctoral Fellow in Neuroengineering and Data Science, Speech and Hearing Science & eScience Institute, University of Washington

2019 Neural Computation and Engineering Connection Speaker

Keshavan addresses the difficulties that have arisen from advances in technology allowing massive amounts of data to be collected, and proposes how using web technology can help neuroscientists manage the big data challenge. Through the use of web-based visualization, collaborative meta-analysis, and citizen science platforms, scientists can address challenges of high data dimensionality, integrating a seemingly endless stream of new results into literature, and scaling decisions made by neuroimaging experts to large data sets.

Distributed correlates of visually-guided behavior across the mouse brain”

Nick Steinmetz, Assistant Professor, Biological Structure, University of Washington

Steinmetz presents recent work studying the neural mechanism of visually-guided behavior in mice by using Neuropixels probes to record the activity of 30,000 neurons over 42 brain regions while mice performed a vision-based behavior task. The work provides a new view on the neural population, showing how neurons in different regions respond differently when making decisions as opposed to when responding to purely visual signals, as well as highlighting how all brain regions are active and working together to respond to a stimulus.

Keynote Lecture: “Whiskers as tactile and flow sensors: linking neuroscience, mechanics, and robotics”

Mitra Hartmann, Professor, Biomedical Engineering and Mechanical Engineering, Northwestern University

Hartmann studies the use of animal vibrissae (whiskers) in order to sense fluid flow in their environment, and works on applying biological insight to the design of artificial whiskers for tactile and flow sensing. These insights have led to mechanical analysis that also is applied to guide neurophysical experiments in hopes of better understanding both the neuroscience and mechanics of whisker-based sensing.