Research as a Community-Building Activity

Community activities, especially those that bring together people of all ages, seem to be increasingly rare and brief lived in this busy world. Yet they provide a lot of meaning in people's lives, for reasons ranging from the deeply personal to the broadly impactful. I used to volunteer for the San Francisco Symphony, selling discounted concert tickets to college students. This activity brought together people of all ages to contribute to the survival of the arts, and to learn from one another about topics ranging from fundraising to musical composition to the role of music in health. And it yielded many enduring friendships, formed in the act of pursuing a common goal. From this experience and others, I learned that community participation in an activity gives people a stake in it, ensuring its endurance and prominence. It also increases the diversity of people who contribute to the activity, beyond just the specialists, thus broadening the scope of the activity and increasing its power and relevance. Finally, such activities enrich the meaning of the lives of the people participating, enabling them to contribute to the well-being of the world and building communities of interaction and support, which is perhaps why the absence of such activities in one's life can be palpable at times.

Scientific research, when compared with other areas in which people can volunteer their time, seems to be relatively unexplored as a community-building activity. The conventional wisdom implies that scientific research is something you do when you are training or in school, so that afterward you can go off to make useful products and provide valuable services. To caricature only slightly: the public is often painted as a confused, and sometimes suspicious, consumer of scientific information, and the production of science is often painted as an abstruse art, and occasionally a dangerous one. And the two sides--the public and the producers of science--meet only occasionally, through journalists and explicit outreach efforts. It is, however, widely accepted in this interdisciplinary age that scientific discovery, to be the engine of change that is needed, broadly benefits from the interaction of people from diverse backgrounds. Thus, I believe that we should think about ways to involve all community members broadly in the act of research itself, working in groups to discover and share knowledge.

Involvement of the public in the act of science would shape the kind of science being done, perhaps increasing the impact of science on daily life. Community involvement in the act of research would also make science more understandable, and perhaps more familiar, to the public, because people would be engaged in its framing and communication. What better way to increase scientific literacy, make the benefits of science clear, and quell myths and spread facts than to give all people a stake in the act of discovering science? Maybe the way the world sees some currently controversial topics--stem cells, climate change, energy sources--would be different if more people engaged in the act of testing hypotheses and examining data. Community participation in science would also be enormously personally enriching, providing exercise in thinking and problem solving (something that is useful in all problem domains, throughout life) and empowering people to contribute directly to the betterment of society in a broadly impactful way.

More and more fields are being democratized by strategies that make it easy for people to create: bloggers can write news stories, teenagers can film movies and upload them, and anyone can compose a novel and get it in front of millions of readers. People talk about "participatory media," but what about "participatory science"? The opening of science is occurring slowly--led by the open-access journals, perhaps, and by some groups sharing their data and insights in increasingly informal ways. But the opening up of the act of scientific research itself is still not widespread. I propose that we begin to create programs in which members of the public, of all ages, can meaningfully volunteer in laboratories, working together on problems--perhaps only for a few hours a week, but over an extended period of time, to achieve depth. If you are a lab head, think about inviting someone new into the fold. If you are interested in participating in scientific research, reach out to people nearby and see if you can help.

Most scientific funding agencies currently focus on the training and development of young people. "K-12, undergraduate science majors, non-science majors, and graduate students," the NSF helpfully suggests as key demographics to focus on in order to broaden the impact of one's research. But it seems to me that programs that engage other demographics--for example, retired individuals who want to create and mentor, with their wealth of knowledge, practical experience, and wisdom--would not only provide new perspectives for young people entering science, but also enrich the lives of a segment of the population that is not actively recruited to the intellectual process by many current institutions. Working with other scientists is just fun. It is highly interactive and engaging, and can cover vast intellectual and emotional ranges, as well as bring people together, as with any meaningful community activity. And there will always be important problems to solve.

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/).}

How to Think

When I applied for my faculty job at the MIT Media Lab, I had to write a teaching statement. One of the things I proposed was to teach a class called "How to Think," which would focus on how to be creative, thoughtful, and powerful in a world where problems are extremely complex, targets are continuously moving, and our brains often seem like nodes of enormous networks that constantly reconfigure. In the process of thinking about this, I composed 10 rules, which I sometimes share with students. I've listed them here, followed by some practical advice on implementation.

1. Synthesize new ideas constantly. Never read passively. Annotate, model, think, and synthesize while you read, even when you're reading what you conceive to be introductory stuff. That way, you will always aim towards understanding things at a resolution fine enough for you to be creative.

2. Learn how to learn (rapidly). One of the most important talents for the 21st century is the ability to learn almost anything instantly, so cultivate this talent. Be able to rapidly prototype ideas. Know how your brain works. (I often need a 20-minute power nap after loading a lot into my brain, followed by half a cup of coffee. Knowing how my brain operates enables me to use it well.)

3. Work backward from your goal. Or else you may never get there. If you work forward, you may invent something profound--or you might not. If you work backward, then you have at least directed your efforts at something important to you.

4. Always have a long-term plan. Even if you change it every day. The act of making the plan alone is worth it. And even if you revise it often, you're guaranteed to be learning something.

5. Make contingency maps. Draw all the things you need to do on a big piece of paper, and find out which things depend on other things. Then, find the things that are not dependent on anything but have the most dependents, and finish them first.

6. Collaborate.

7. Make your mistakes quickly. You may mess things up on the first try, but do it fast, and then move on. Document what led to the error so that you learn what to recognize, and then move on. Get the mistakes out of the way. As Shakespeare put it, "Our doubts are traitors, and make us lose the good we oft might win, by fearing to attempt."

8. As you develop skills, write up best-practices protocols. That way, when you return to something you've done, you can make it routine. Instinctualize conscious control.

9. Document everything obsessively. If you don't record it, it may never have an impact on the world. Much of creativity is learning how to see things properly. Most profound scientific discoveries are surprises. But if you don't document and digest every observation and learn to trust your eyes, then you will not know when you have seen a surprise.

10. Keep it simple. If it looks like something hard to engineer, it probably is. If you can spend two days thinking of ways to make it 10 times simpler, do it. It will work better, be more reliable, and have a bigger impact on the world. And learn, if only to know what has failed before. Remember the old saying, "Six months in the lab can save an afternoon in the library."

Two practical notes. The first is in the arena of time management. I really like what I call logarithmic time planning, in which events that are close at hand are scheduled with finer resolution than events that are far off. For example, things that happen tomorrow should be scheduled down to the minute, things that happen next week should be scheduled down to the hour, and things that happen next year should be scheduled down to the day. Why do all calendar programs force you to pick the exact minute something happens when you are trying to schedule it a year out? I just use a word processor to schedule all my events, tasks, and commitments, with resolution fading away the farther I look into the future. (It would be nice, though, to have a software tool that would gently help you make the schedule higher-resolution as time passes...)

The second practical note: I find it really useful to write and draw while talking with someone, composing conversation summaries on pieces of paper or pages of notepads. I often use plenty of color annotation to highlight salient points. At the end of the conversation, I digitally photograph the piece of paper so that I capture the entire flow of the conversation and the thoughts that emerged. The person I've conversed with usually gets to keep the original piece of paper, and the digital photograph is uploaded to my computer for keyword tagging and archiving. This way I can call up all the images, sketches, ideas, references, and action items from a brief note that I took during a five-minute meeting at a coffee shop years ago--at a touch, on my laptop. With 10-megapixel cameras costing just over $100, you can easily capture a dozen full pages in a single shot, in just a second.

^{Cite as: Boyden, E. S. "How to Think." Ed Boyden's Blog. Technology Review. 11/13/07. (http://www.technologyreview.com/blog/boyden/21925/).}

Synthetic Neurobiology

When you program a computer, you don't have to steer individual electrons around manually; if you did, the complexity of performing even the simplest calculation would be daunting. It's clear that building or fixing a complex thing requires a layer of abstraction, so you can solve the specific problem at hand while ignoring the underlying complexity. Problems of the brain must also be addressed at appropriate levels of abstraction. During the last week of September, I participated in a neurotechnology panel at MIT. One theme quietly emerged: different neurological and psychiatric problems demand neural control technologies that operate over different spatial and temporal scales. Critical design choices must be made: do you go for an invasive, spatially focal neural stimulator or for a noninvasive but spatially cruder one?

Consider the question of how you might augment cognition and mood by stimulating selected neural circuits. You'd probably want maximum flexbility--the ability to tune mood, decision-making, judgment, and so on, independent of one another. Researchers have attempted to alter cognitive functions by noninvasive stimulation of cortical brain regions, each a few cubic centimeters in volume. It's become clear, however, that these brain regions are not the most elementary of brain circuit elements. For example, manipulation of one specific brain region can change many cognitive and emotional functions, in parallel. Consider the concrete example of transcranial magnetic stimulation (TMS) of the right prefrontal cortex. In the last few years, studies have shown that TMS of this brain region with a standard protocol (one pulse per second for 10 to 30 minutes) can alter decision-making in the face of unfairness, improve the symptoms of depression, and increase risk-taking behavior. Thus, it may be difficult to induce a specific, desired brain state, without inducing other (perhaps undesired) brain states, when the primitives under consideration are all "brain regions." Clearly, this convenient abstraction layer, which has been prominent across centuries of neuroscience, will need to be refined in order to develop a fully flexible architecture for cognitive augmentation.

The hard part of neuroengineering is the "neuro" part. Our job is to sculpt neural-circuit activity so that it accomplishes a desired computation or behavior, without inducing alterations that are non-optimal. A few weeks back, Biological Engineering department chair Doug Lauffenburger declared to me, "What you're doing is synthetic neurobiology," drawing parallels between my lab's work and the work of labs that do synthetic biology. If you've been following the field of synthetic biology, you'll know that a major premise is the creation of abstraction layers for biological engineering. This agenda includes the development of standardized sets of basic engineering parts (i.e., standard pieces of DNA that encode precisely defined functions), and design rules for building complex systems out of similar ones (i.e., ways of connecting gene networks to accomplish desired organismal outcomes). By following the design rules, and using the standardized parts, biological engineers can create novel biological systems from scratch--systems that make sense and work in a predictable way.

In our lab, we have begun to assemble a toolbox of methods for precisely controlling specific neural-circuit primitives. We are now using these tools to learn how to control behavioral outputs, with great precision and power. Hopefully, in this way we will learn what the neurobiological primitives are for engineering the brain and develop design rules for the optimal control of neural-circuit output, especially in disease states. We're at an early stage. The synthetic biologists started off with the strong hypothesis that genes were the right abstraction layer. After all, the genome is fundamental, and DNA is easy to generate, manipulate, and read. But for neural computation, we don't know what the DNA equivalent is. Are the primitives dendritic elements? Single neurons? Synaptic connections? Cell types? Small networks? Large networks? And at what nervous-system scales should we be reading? Writing?

Light-controllable neurons. Credit: J. Cardin, X. Han, X. Qian, C. Moore, E. Boyden.

Most likely, the abstraction layer for synthetic neurobiology will vary greatly across the different neurological and psychiatric disorders for which we're engineering solutions. A key task in the years to come will be to develop a methodology for assessing the level of description appropriate for solving a specific problem. Although much of my lab's work is focused on controlling very specific neural-circuit elements, using pulses of light to turn individual cell types on or off with high precision, it's clear that very powerful tools can exist at much higher levels of abstraction. For example, cognitive behavioral therapy, in which patients learn how to debug negative thoughts that contribute to depressed feelings, is a profound and powerful neurotechnology. And it is entirely based on language. Language-based neurotechnologies activate sets of neurons, distributed across the whole brain, in very precise patterns--and in ways that can cause changes capable of enduring throughout a lifetime. Language can induce precise changes in the brain that move people to happiness, teach them skills, lead them into war, and make them feel empathy or hatred or exhilaration. As John Hockenberry pointed out to me this past spring, language is the original brain interface. Perhaps the complexity of synthetic neurobiology arises from the fact that brain engineering is, in some ways, what we all do, all the time.

^{Cite as: Boyden, E. S. "Synthetic Neurobiology." Ed Boyden's Blog. Technology Review. 10/9/07. (http://www.technologyreview.com/blog/boyden/21871/).}

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.