This past week, the neuroscience community lost a great scientist, and role model and advocate for women in science, Allison Doupe, to cancer. As a professor at the University of California San Francisco, Allison’s research focused on mechanisms of auditory coding, vocal production and learning in birdsong. Her elegant and groundbreaking papers and seminars infected many researchers with the excitement and promise of birdsong as a fascinating system in its own right and as a model for language and motor skill learning. Allison was also a highly regarded practicing psychiatrist, specializing in the effects of hormones on female brains. At the Keck Center at UCSF, as a leader at the vibrant UCSF Sloan-Swartz Center, and through her Woods Hole lectures, Allison was responsible for inspiring many junior theorists with the amazing opportunities that labs like hers offered for collaborative and analytical approaches to neuroscience. She was a mentor to many who did not directly work with her; an anxious speaker needed only to spot her bright, warm smile in the audience to find calm and confidence. Her brilliant intellect, insight, warm encouragement and optimism will be sorely missed by all who were fortunate to interact with her. Allison leaves behind her husband, Michael Brainard, also a prominent birdsong researcher, and her young twins, Alec and Sam.
The schedule for the 2014 Sloan-Swartz meeting, to be co-hosted on the UW campus by the UW Comp Neuro Program and the Allen Institute for Brain Science, is now on-line. It should be an excellent meeting. Locals may now register to attend, although numbers may be limited.
Bill Gates has just declared this week as Mosquito Week, pointing out that the mosquito is the cause of the most human deaths per year of any creature on earth, even exceeding us. In our lab, in collaboration with Jeff Riffell and Michael Dickinson in the Department of Biology, we are working to understand the neural and computational mechanisms that enable mosquitoes to find their prey using joint signals from the heat and carbon dioxide that we exude. This problem is an especially fun one for theorists: because of turbulent transport, the source signals are complex and time-varying, with interesting statistical properties; and the two signals interact in nontrivial ways, as Leslie Vosshall’s lab has recently shown. Here’s the beautiful graphic from Bill’s blog posting.
Former lab member Mike Famulare was recently interviewed by Seattle Times’ NWJobs. After graduating with a PhD in physics in 2012, Mike joined Intellectual Ventures’ research effort on modeling disease propagation and vaccine efficacy– an excellent use of his stochastic-process chops.
The edition of Current Opinions in Neurobiology on Theoretical and Computational Neuroscience that Haim Sompolinsky and I co-edited has just been published. I’m excited about the diverse and fascinating articles our authors produced, giving a snapshot of the field and an interesting view of where we are headed. Both Haim and I wove our own impressions of what we took from this collective wisdom. Check it out.
The tech world is also in the grip of a justifiably renewed excitement about artificial intelligence. It was intriguing to see so many students from tech in our Computational Neuroscience MOOC (mentioned in this article), which ended its second session last month. I’m optimistic that this increased interest and communication will help to drive discovery in fundamental neuroscience.
Many people have been in touch with questions about how to pursue further education or careers in computational neuroscience. With the BRAIN initiative in the news and a number of companies launching into neural technologies, this is certainly an area of current opportunity. I wanted to give some perspective on possible trajectories, as the field is very diverse, spanning academic study in quantitative approaches to systems neurophysiology to robotics and industrial engineering.
It is not necessary to join a graduate program in computational neuroscience. Indeed, I am not strongly in favor of such programs and here at UW we have chosen not to go that route: I believe that computational neuroscience is the quintessential interdisciplinary program, and it is important to master at least one discipline. So I tend to favor basing oneself in a home department that provides the core skill set and knowledge base you seek: neurobiology, computer science, physics, statistics, electrical engineering, bioengineering or mathematics, but doing so in a place with opportunity and proven track record of cross-departmental collaborations and openness to interdisciplinary thesis projects. That way, you will ensure that your core coursework is a strong preparation for one academic discipline, but that you have the chance to take a variety of electives that allow you to get a broad training in other areas, and that your interdisciplinary research will be fairly and helpfully guided and evaluated.
Where? Top-tier examples of institutions in the US that provide such an environment (in no particular order) include the University of Washington (naturally!), Berkeley, UCSD, Columbia, NYU, Carnegie-Mellon/Pitt, Princeton, MIT, the University of Chicago, U. Penn, UT Austin, Northwestern. Other notable options include Duke, UT Houston, UC Davis, Brandeis, Yale, Stanford, Caltech and USC, which has an impressive retinal prosthetics group. It is important to be aware that entry into graduate programs at all of these institutions is highly competitive. As an example, the University of Washington’s Neurobiology and Behavior graduate program received nearly 300 applications this year for an anticipated class of 10, and only around 30 students from an excellent applicant pool were interviewed. So it is important not to fixate only on top programs, but to look carefully at some other places where you can find at least two or three researchers who are working in areas that interest you. You might be able to develop a novel collaboration between faculty who have not worked together before. Very few institutions have a “computational neuroscience” program. You should look to see which graduate programs (eg. Neurobiology and Behavior, Computer Science and Engineering, Applied Math) host the faculty whose work interests you, and where you will be able to do the coursework and research you have in mind: there may be multiple options. Also be careful to tailor your choice of institution and program to the direction that you are motivated by, whether that be delving into the basic mathematics of chaotic neural networks, probing decision-making behavior and/or circuitry in primates or rodents, working closely with clinical applications or developing devices. All of these require rather distinct training.
Internationally, there are centers of excellence in many countries, and this area has recently received an enormous stimulus in Europe in the form of support for Henry Markram’s brain simulation consortium, centered in Switzerland. In terms of clusters of individual labs doing high quality systems neuroscience with theory components, Germany, Great Britain and Israel have perhaps the most extensive offerings. Germany boasts the network of Bernstein Centers for Computational Neuroscience and many Max-Planck centers have been trailblazers in systems neuroscience, particularly in invertebrates (e.g. Tuebingen). Britain has a long-standing tradition of quantitative approaches to biology and the support of the Wellcome Trust for systems neuroscience; there are particularly vibrant groups at UCL (including the Gatsby Institute), Cambridge, Oxford and Edinburgh. Israel has the Weizmann Institute and Hebrew University with very strong programs, and good ones also at Ben Gurion and the Technion; the Weizmann accepts foreign students although a better all-round option for a stint in Israel may be for a postdoc. I’ll also randomly identify a few other places that I know have interactive theory/experimental groups: Ecole Normale Superieure in Paris, ETH in Zurich, the Champalimaud center in Lisbon. But again, there are many places where high quality work is going on at lower concentrations that would be fine choices.
Getting into graduate school. The admissions process focuses less on the specifics of your undergraduate training, reflecting the diverse backgrounds that are appropriate for neuroscience, but on high academic performance and interesting and productive research or work experience. Research experiences tend to be favored over work experience with the assumption that these reveal more of your independence and creativity, so if you are applying out of a work situation, your statement and letters should emphasize and give specific examples of these qualities.
Courses and Summer Schools
Of course there is our Coursera course, which we are likely to run regularly. There are also some excellent summer schools which can afford a quick boot-up in the area. One of my favorites (as I attended it and also directed it for five years) is the MBL course, Methods in Computational Neuroscience. This is a very selective course aimed at graduate students and postdocs which takes one through the mathematical methods that underlie many contemporary approaches to problems in systems neuroscience. Other options are the Okinawa course run through OIST, and Cold Spring Harbor Labs has initiated what seems to be an annual course in China. CHSL has a highly regarded Computer Vision course that counts as alumni and faculty many of the top researchers in vision neuroscience. There are also several European courses including through the Bernstein Center: watch the compneuro mailing list for opportunities.
A few companies are working on fundamental neural research with the hopes that this will lead to useful devices. One such company is Brain Corporation, in San Diego, which is building a model of the retina with a long-term view to modeling the entire brain. The Allen Institute for Brain Science, in Seattle, has also embarked on a long-term project to solve the problem of coding in the visual system using a combination of high-throughput electrophysiology, anatomy and modeling. The Redwood Center for Neuroscience, in Berkeley, was also initiated as an academic/industrial collaboration to transfer fundamental brain algorithms to device design. IBM Alameda has an ongoing project to replicate cognition in a brain model.
A much larger number of companies are entering the field of neural prosthetics and brain-computer interfaces. One way to locate such opportunities is to look for university-based centers which have industrial affiliations and partnerships; their websites can direct you to those companies. One example is UW’s Center for Sensorimotor Neural Engineering, an NSF-funded Engineering Research Center. There are perhaps a dozen comparable centers across the US. These companies may offer internships and other ways to gain experience and refine your understanding of the qualifications you require.
Thanks to information provided by our Coursera students, edited to add the following:
“UK tuition fees for postgraduate courses (if you fund them yourself) are usually about £9,000-£11,000 a year for EU and about twice that for non-EU students, but there is a lot of variability in the cost, more so than for undergraduate courses. When you look for funding, then the University is one option, but depending on where you are from, there may be additional opportunities for sponsorship.”
Please feel free to add links and information about other programs in the comments!