Inverting the Core

When I was a new student at MIT, there were legends of a math class in which the professor would occasionally assign an unsolved (and possibly unsolvable) problem. And every now and then, a student would resoundingly nail it. Soon after arriving at MIT, I was successfully spending my leisure hours inventing control algorithms for underwater robots, writing physics-based computer animation engines, devising new pattern-recognition algorithms, and building new kinds of NMR spectrometers. Now, more than a decade later, and being a professor myself, it's clear that some of the most valuable learning I did at MIT occurred during the solving of real-world problems. Simply put, in the Internet age, once you learn the basic core material, perhaps the best way to direct the growth of learning is to chase down real-world problems and fix them. You learn how to wrestle with failure, and how to get the resources you need.

Every now and then, it's useful to see how seriously one takes one's ideas. So let's take the above observation to its logical end: what if we decided that all work that students do in service of their education--problem sets, homework, exams--should be aimed at having a direct impact on solving a major current real-world problem? Please note: this doesn't at all imply the abandonment of learning of core things (calculus, physics, basic chemistry and biology, signal processing); it's just that a particular piece of homework might involve, instead of proving a discovery by Einstein right for the thousandth time, the solving of a piece, however small, of something unknown and important.

Clearly, this requires a mapping process--professors and teachers must parse real-world problems into decoupled chunks that can be addressed by individuals, while still enabling learning of the core materials. There are certainly some good examples of classes like this already. Lab classes at many universities exist in which students build medical devices, create computers, design virtual worlds, write business plans for ventures in developing countries, and learn how to make autonomous robots. Here I am wondering if, in addition, it would be possible to map real-world problems into the problem sets, homework, and exams for all the other classes--perhaps even introductory core classes. It's interesting to think about whether this might help humanity solve some outstanding problems. A back-of-the-envelope calculation: if 4,000 undergraduates at a university spent 40 hours a week during the school year solving problems that map onto real-world problems, that's more than 3,000,000 extra hours a year of inventing, design work, and creation, aimed at the problems that face humanity today. Multiply that times the number of universities engaged in such fields, and the new ideas and contributions to the world could be staggering. At MIT, undergrads do a lot of research. In my group, undergrads are here nights and weekends, even on busy school weeks, innovating incredibly novel inventions and conducting complex experiments. It is interesting to think about how that passion could be harnessed during the rest of their schedules.

An open question, though, is how much work it would take to map real-world problems into the thousands of smaller pieces that would be appropriate for classwork. And then to render them in engaging, interesting ways so that students will learn their core materials while they solve them. The new field of human-based computation is beginning to explore related questions. I was particularly intrigued by a recently released game that people can play to help solve questions in the field of protein folding--but many problems are not as clearly understood, or modular enough, to be broken into many subparts in such a way. It's possible that a discipline will need to arise around the analysis of really tough problems, and the breaking down of them into smaller parts. We also need to devise new and effective strategies to engage humans (with the assistance of computers) in the solving of such problems. It'll be interesting to see how far these ideas will scale in the years to come.

Thanks to Joost Bonsen for suggesting the title of this blog post.

_{Cite as: Boyden, E. S. "Inverting the Core." Ed Boyden's Blog, Technology Review. 7/15/08. (http://www.technologyreview.com/blog/boyden/22096/).}

Training a Generation of Neuroengineers

Neurological and psychiatric disorders such as traumatic brain injury, stroke, Parkinson's disease, autism, depression, post-traumatic stress disorder, and chronic pain affect well over a billion people worldwide. These disorders steal away not only life span, but also our selves and identities. More than $1,000,000,000,000 is spent yearly in the battle against these disorders, even in the absence of effective treatments for many of them. Compared with innovations in other fields, like cancer, neurotechnologies have trickled out of labs at a relatively slow pace, yielding a handful of good drugs, a couple of methods for brain stimulation, and a few ways to image and analyze brain structure and activity. Like many innovations in medicine and bioengineering, these triumphs often emerged in no small part by chance, which makes iterative improvement tricky. Clearly, something new is needed. That's why, over the past year, we've begun experimenting with a hands-on neuroengineering curriculum at MIT, in which undergraduate and graduate students actively engage in the process of becoming neuroengineers, learning to solve intractable problems of the brain by actually doing it.

Learning neuroengineering is a hands-on process. What do you need to learn to fix problems of the brain and nervous system? The answer is, in brief: whatever it takes. The brain is complex (with well over a hundred billion interconnected circuit elements), subtle (it mediates everything we sense, feel, decide, and do), and inaccessible (packed densely inside the skull). To be a neuroengineer, you must be able to take advantage of any idea or fact that you discover that lets you get a handle on a brain process or function. Teaching neuroengineering thus means empowering people to identify problems and create solutions, connecting often distant topics in logical and intuitive ways to arrive at elegant insights. In short, our students must learn neuroengineering by making it up as they go along.

With my colleagues at MIT, I've begun teaching students how to go through the neurotechnology life cycle, from concept to validation to revelation to the world. We've concocted a series of three hands-on classes, which are still in the beta-testing stage, to teach design, laboratory, and entrepreneurship skills. Students pick projects and are mentored to make them as high-impact, feasible, and novel as possible. These classes are aimed at helping students learn the principles of operation of the nervous system from an engineering standpoint, implement their best ideas in the lab, and learn the process of translating technologies out of the lab and into the world. In the first class, Principles of Neuroengineering, students learn the basic principles governing the reading of information out from, and getting information into, the nervous system. They also, alone or in interdisciplinary teams, design and model fundamentally new technologies that gain information about, or positively alter, the operation of the brain. In the second course, Applications of Neuroengineering, students do lab work, learning how to implement, debug, and validate technologies. They make plans, revise them when failure encroaches, and learn how to find collaborators, make contingency maps, and manage time and resources. Finally, in the last course, Neurotechnology Ventures, students explore how to get their technologies out of the lab and into the world, writing up business-plan executive summaries and defending their projects in class, and attending guest lectures by entrepreneurs who are paving the way in neuroengineering. Anyone can participate--even freshmen can get involved. The ideas that yield the best neuroengineering inventions are often absurdly simple.

The classes started a beta-testing run in February 2007. Last year, in the Principles of Neuroengineering class, students designed never-before-seen methods for reading out brain activity in a wearable device, delivering therapeutic genes to specific cell types in the nervous system, and precisely measuring blood flow in the brain. Some of the students even built prototypes of their devices. For the Applications of Neuroengineering class, we just received a pilot grant from the MIT Alumni Class Funds to supply students with consumables, so that they can implement and validate their very best ideas in the lab, learning from failure and iteration. Students enter this class with concrete ideas, and get to make them reality. (We don't yet have a dedicated laboratory at MIT for teaching neuroengineering, so students who are safety- and procedure-certified to work in my lab can do their projects there. I try to help the rest find other collaborating labs on campus in which to work.) And in the first round of Neurotechnology Ventures, up to 50 people (including some professors) came to hear speakers talk about their companies (with post-talk discussions often lasting late into the night). Twenty students completed the key project, the creation of a concise business plan for a technology.

Although it's still the early days, perhaps this is the beginning of a Synthetic Neurobiology curriculum. Like many endeavors, this current set of classes has had a long, evolutionary path. Joost Bonsen and Rutledge Ellis-Behnke, my co-instructors in the Neurotechnology Ventures class, envisioned such a class almost half a decade ago. When I arrived at MIT in 2006, I was deluged by e-mails from undergraduates and graduate students eager to enter the business of engineering the brain and mind. The time had come. But our work is only beginning. We are still revising our educational vision daily, as we define the abstraction layers for engineering the brain. In the long term, I will measure the success of this mission by the number of laboratories, companies, inventions, and, ultimately, cures that are accomplished by people who pass through this class. Someday, we will understand the brain and know how to fix its problems. But for now, we must focus on jump-starting this effort by encouraging direct action by the best minds in the world, at an intellectual scale that exceeds all that has come before.

Numerical data in the first paragraph is from a recent report by NeuroInsights, LLC.

_{Cite as: Boyden, E. S. "Teaching Neuroengineers." Ed Boyden's Blog, Technology Review. 4/21/08. (http://www.technologyreview.com/blog/boyden/22055/).}

Engineering the Brain: The Panel

Tomorrow at 3 P.M., I'm going to be speaking in a session on engineering the brain at MIT's Emerging Technologies Conference. We're going to delve into new technologies that take us the first step along the path toward "engineering the matter mediating the mind"--namely, precise readout and control of neurons and other cells in the brain and peripheral nervous system. I'll talk about some unpublished work on new technologies for repairing abnormal neural computations. Other participants will include Mark Humayun, who leads a team at USC that designs and builds retinal stimulators for the blind; Robert Kirsch, who works at Case Western Reserve University and builds electrical stimulators capable of precisely controlling limbs; and Timothy Surgenor, CEO of Cyberkinetics, which implants recording arrays into the cortices of paralyzed patients so that they can communicate to the outside world. Should be exciting.