Training for diverse career paths

In a recent SfN webinar Developing a 21st Century Workforce, moderated by Katja Brose, speakers Huda Akil, Elisabeth van Bockstaele and Adrienne Fairhall discussed training for career paths beyond academic science, the importance of interdisciplinary training, and strategies for programs and individual students to gain opportunities to enhance students’ quantitative backgrounds. At UW, UWIN, the Computational Neuroscience program and the CSNE aim to provide students with these training opportunities and to give exposure to an increasing range of job openings in neurotechnology companies. To help plan for such a future direction, it is interesting to look at the hiring criteria for jobs at a new start-up and CSNE partner, ArianRF:

  • Solid knowledge in neuroscience
  • Experienced with neural signal acquisition
  • Experience with EEG/ECoG
  • Solid knowledge in signal processing/ statistical signal processing
  • Knowledge in machine learning and computational neuroscience
  • Motivated and self-driven: willing to be a core person and “go to guy” in a start-up company.

Graduate training applications now open!

We are very happy to announce that there are now multiple opportunities open for graduate student support in computational neuroscience and related areas. Applications are due on July 17.

Please see the websites of each program for relevant requirements. All of these slots are open to graduate students in a wide range of degree programs as long as your research lies in an appropriate area. Applications can be shared between programs; please indicate on your application which of the other programs you would like to be considered for, taking into account program requirements.

Underrepresented groups are strongly urged to apply.

Studies in Neural Computation and Engineering at UW

The University of Washington has a rich, active and highly collaborative community of researchers in the field of computational neuroscience and neural engineering. The University of Washington is a vibrant research university with a beautiful campus in a spectacular urban setting, with an ERC Center for Sensorimotor Neural Engineering, the UW Institute for Neuroengineering, close connections to the local tech industry and the Allen Institute for Brain Science. UW is also a major data sciences center with interdisciplinary interactions coordinated through the eSciences Institute. The city is a short distance from wilderness and outstanding summer and winter outdoor adventure.

While faculty advisors belong to a wide range of different departments, researchers come together regularly for seminars, journal clubs and a yearly retreat. Many student funding opportunities exist through multiple training grants, UWIN and the CSNE. Doctoral programs encourage collaborative research projects across departmental boundaries, but admissions and first-year course work and formal requirements are handled by graduate programs individually. Students interested in this area should apply to the program that best fits their background, interests and career goals.

Students enrolled in any relevant program are eligible to join the Graduate Certificate Program in Neural Computation and Engineering, and to apply for funding through UWIN and the Neural Computation and Engineering Training Program.

 Relevant programs, websites and application deadlines include:

Faculty include:

  • Wyeth Bair (Neuro, CSE): Computer modeling of visual cortical circuits
  • Geoff Boynton (Neuro, Psychology): Functional imaging of vision
  • Beth Buffalo (Neuro): Navigation and memory in primates
  • Michael Buice (Allen Institute and AMath): Models of visual computation
  • Bing Brunton (Biology, Data Sciences): High dimensional neural data
  • Howard Chizeck (CSE, Neuro): Performance metrics for neural interfaces
  • Tom Daniel (Neuro, Biology): Sensorimotor integration and flight control
  • Horacio de la Iglesia (Neuro, Biology): Circadian rhythms
  • Marcel den Nijs (Physics): Statistical mechanics of brain function
  • Adrienne Fairhall (Neuro, BPSD, Physics): Adaptive neural coding, sensorimotor integration
  • Eb Fetz (Neuro): Motor control and brain-computer interfaces
  • Ione Fine (Neuro, Psychology): Human visual psychophysics and imaging
  • Emily Fox (CSE, Stats): Bayesian network analysis
  • David Gire (Neuro, Psychology): Mammalian olfaction
  • Bertil Hille (Neuro, BPSD): Biophysics of neuronal signal transduction
  • Greg Horwitz (Neuro): Cortical color processing
  • Nathan Kutz (A Math): Nonlinear dynamics and dimensionality reduction
  • Adrian KC Lee (Neuro, Speech and Hearing): Auditory scene analysis with imaging
  • Stefan Mihalis (Allen Institute, AMath): Algorithms of computation and learning
  • Chet Moritz (Neuro, Rehab Medicine): Neural prosthetics
  • Sheri Mizumori (Neuro, Psychology): Neurobiology of decisions, learning, and memory
  • Bill Moody (Neuro, Biology): Cortical development
  • Scott Murray (Neuro, Psychology): Visual neuroimaging
  • Jeff Ojemann (Neuro, Neurology): Human neural function and neuroprosthetics
  • Anitha Pasupathy (Neuro): Neurobiology of visual shape processing
  • David Perkel (Neuro, Biology, S&H): Neural mechanisms of vocal learning
  • Steve Perlmutter (Neuro): Motor control
  • Chantal Prat (Neuro, Speech and hearing): Auditory processing
  • Nino Ramirez (Neuro): Neural control of rhythmic activity
  • Rajesh Rao (Neuro, CSE): Computational modeling and brain-computer interfaces
  • Fred Rieke (Neuro, Physics, BPSD): Sensory signal processing in the retina
  • Jeff Riffell (Neuro, Biology): Neuroecology and chemosensation
  • Ed Rubel (Neuro, BPSD, S&H): Development of the auditory system
  • Jay Rubinstein (Neuro, BioE, S&H): Biophysics and engineering of cochlear implants
  • Eric Shea-Brown (Neuro, A Math): Nonlinear dynamics in neural computation and coding
  • Eli Shlizerman (EE): Neural networks and computation
  • Bill Spain (Neuro, BPSD): Biophysics of neuronal computation
  • Kat Steele (Mec Eng): Human movement
  • John Tuthill (PBIO): Proprioception and decision-making in flies
  • Emo Todorov (Neuro, A Math, CSE): Optimal motor control
  • Daniela Witten (Biostat): Big data approaches to neural data

The dynamic brain!

A new method to segment brain regions by their connectivity.. examining how particular cell types participate in the cortical “chorale”.. new open-source tools to help visualize how brain regions are connected.. a data-driven model to capture how information is transformed between layers in visual cortex: these are a sampling of some of the student team projects presented on the final day of the 2016 Workshop on the Dynamic Brain.

Held at UW’s beautiful Friday Harbor campus in the San Juan Islands, this course is a joint endeavour of the Allen Institute for Brain Science and the University of Washington’s Computational Neuroscience program. With a teaching program focused on understanding cortical function, and centered on the open-access data sets and tools of the Allen Institute, students engage in an intense, hands-on learning experience that ranges from basics of neuroanatomy to theoretical models of brain function. Tutorials and problem-solving exercises working from Python notebooks get everyone up to speed and quickly engaged in developing original projects combining modeling and analyses of the Allen’s rich, unique and carefully curated data sets—including detailed connectivity maps, a wide sampling of functional properties of different cell types, and the newly released Allen Brain Observatory, recordings from many neurons while an animal is engaged in a variety of different perceptual tasks. The students, stimulated by a diverse set of lectures, are able to pose an array of novel questions that can be explored for the first time in this data.

group working

Team KART: a prize-winning collaboration.

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Team COBRA’s presentation included their team project management plan.

The progress made by course participants in these projects in two short weeks is stunning. Several factors make it possible. The course began with a Python bootcamp held at UW’s Data Sciences Studio, so that everyone hit the ground running with AIBS’ Python-based interfaces. Upon arriving at Friday Harbor, students, TAs and faculty spend all working hours together in an airy and hospitable dining hall overlooking the sea, where the buzz of activity continues into the small hours. AIBS staff and our TAs and faculty invest an enormous amount of effort preparing teaching resources for the class and are constantly on hand to assist students with accessing and manipulating data and in brainstorming project ideas. Lectures are followed up with hands-on tutorials so that students are supported while working through structured problems before proposing and launching into projects. Students bring their talent, energy and enthusiasm, and share their diverse expertise through teamwork.

panel

Pat Churchland, Christof Koch and Blaise Aguera y Arcas field questions and discussion on consciousness in man, animals and machines.

Along with presentations from locals from UW and the Allen, lectures also highlighted forefront work being done in labs around the country; Anne Churchland (Cold Spring Harbor Labs) showed how multiple sources of information are represented and processed in the brain during complex decisions, and Rafael Yuste (Columbia) described cutting-edge optical technologies that allow one to “write in” new patterns of brain activity. Jeremy Freeman (Janelia) led an electrifying discussion and workshop on coding practices for collaborative and reproducible science that students immediately incorporated into their project teamwork. In an extended Friday night thoughtfest, philosopher Pat Churchland described the neurological origins of ethics and morality, and Google engineer Blaise Aguera y Arcas discussed developments in artificial intelligence, relationships with biological perception and creativity, and the pending social implications of AI. Christof Koch then joined the speakers in a panel and lively group discussion about machines, consciousness and ethics.

swim selfie

Students are going home with new and solid skills—Python coding, data analysis and modeling tools, familiarity with the datasets and atlases, and ideas and work practices that they can immediately inject into their research at home. But the most exciting aspect of the course is that students have access to TB upon TB of data—brain images and neuronal activity patterns—never before analyzed and seen by a human eye. The projects vividly demonstrated the new insights waiting to be extracted from the Allen Institute resources.

All participants are deeply grateful to Paul Allen for funding the course, and to the Simons Foundation for additional support.

fhl2016 party

So much talent!

Seminar: Kendrick Kay (March 11, 2016)

Friday, March 11, 2pm, Data Science Seminar Room, 6th floor Physics/Astronomy Tower
Dr. Kendrick Kay, Assistant Professor, Center for Magnetic Resonance Research, University Minnesota
“A fully computable model of stimulus-driven and top-down effects in high-level visual cortex”