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/).}

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.

In Pursuit of Human Augmentation

When you're sick, it's obvious that you and your doctor should work, if possible, to help you get better. Nobody would argue against a treatment that restores normal function to a sick or disabled individual. But the consequences of going further than that--going beyond "normal"--are not commonly studied, nor endorsed by many in medicine. Indeed, in any medical procedure, there is risk. If you are already normal, then conventional wisdom dictates that that's enough. "Do no harm," the old aphorism says--we should focus on altering the body and mind only when the risk of the alteration is justified, preferably by the hope of solving a deficit of vastly greater magnitude.

Science has endorsed something of a parallel attitude in its pursuit of biological and biomedical research. Namely, in biology, many key insights have emerged via study of the absence of the phenomenon of interest. For example, throughout the 20th century, many insights about the brain arose from the knocking out of specific genes, or the ablation of specific neurons in animals, or the examination of human patients who have suffered the loss of brain regions from conditions such as war or medically mandated surgery. In this way, we learned that patients without their hippocampi become unable to form new memories; humans and experimental animals with prefrontal-cortex damage make bad decisions and lack impulse control; subjects without dopamine-producing neurons exhibit symptoms of Parkinson's disease. These studies are good at demonstrating the necessity of a specific neural circuit, or brain region, to the normal state. The idea that biomedical science is supposed to bring us up to normal is embedded, to a degree, in the very structure of the experiments we commonly do in the laboratory.

There is nothing wrong with this line of thought. This angle of research is fully consistent with our medical goal. It has brought us many of the triumphs of the last century, and it continues to yield insights into the vast range of diseases that plague us throughout life. When researchers leave this line of thought, though, they point toward the possibility that going beyond normal may change us in new and unprecedented ways, improving our lives in ways that are hard to even imagine. One fascinating line of research over the past few decades has revealed that life span, which long appeared to be solidly set in stone, can be lengthened through pharmacological and genetic manipulations--at least in worms, yeast, and, most recently, mice. This work may someday (quite possibly soon) lead to drugs that can extend human life span. Or note that this past summer, double amputee Oscar Pistorius won second place in a race against able-bodied runners, racing with his prosthetic carbon-fiber legs. Now he is hoping to take on the Olympics, which has led international athletic bodies to worry that augmented humans may be better at running than normal ones are. As a final example, cognitive-augmentation drugs such as modafinil, which enhances alertness even after long hours of wakefulness, are becoming widespread. (Broadly interpreted, good ole coffee might be considered the original neurotechnology, having augmented attention, alertness, and memory in tired humans for millennia.)

It's arguably time for a discipline to emerge around the idea of human augmentation. At the MIT Media Lab, we are beginning to search for principles that govern the use of technology to augment human abilities--that make the idea of normal obsolete. As a codirector of the Center for Human Augmentation, I lead a lab, the Neuroengineering and Neuromedia Lab, that is developing devices that will hopefully eventually allow us to enhance memory, creativity, and happiness in humans. One interesting observation that has emerged is that it's much easier to know when something is gone than it is to characterize it in its intact state. For example, it's far easier to demonstrate that an animal can form no new memories than it is to characterize the trajectory that memories take as they are learned, consolidated, and forgotten throughout the lifetime of that animal. And whereas many measures of depression and sadness have been defined, a coherent description of happiness remains elusive. How can you augment something if you can't define it? One of the first things we are doing is developing better, measurable definitions of such phenomena. Another issue is that radically new tools are needed to augment the mind. We are developing new kinds of neural stimulators, for example, that enable highly targeted manipulations of the brain. Some of our inventions, like the ability to turn specific sets of neurons on and off with brief pulses of blue and yellow light, may be used chiefly in animals for the next few years, but I think they will find many compelling uses in humans in the years to come, as their power becomes manifest through the efforts of a great many neuroscientists and engineers.

Cognitive augmentation will require new technologies.

One argument in favor of going for optimality, and forgetting about normal, is that it's becoming harder and harder to know what is normal. For example, it's been demonstrated that two-thirds of all people have at least one copy of a DNA sequence that makes them more likely to become depressed after a stressful life event. The rest of all people, a minority of one-third, are more resilient to stress than the other two-thirds are. Thus, it could be argued that becoming depressed in response to stress is the normal state. As a neuroengineer, I think it's easier just to develop neurotechnologies that will enable us to make people as happy and intelligent as possible, and perhaps to even go farther: taking on the questions that philosophy struggles with, such as how to find meaning in one's life. (More on that last point in a future post.)

What is a problem, and what is a feature of the human condition? They are not necessarily distinct. But that doesn't mean we shouldn't continue to find better ways to make life better. In that way, we'll hopefully move, in the century to come, from "Do no harm" to "Do good."

^{Cite as: Boyden, E. S. "In Pursuit of Human Augmentation." Ed Boyden's Blog. Technology Review. 9/17/07. (http://www.technologyreview.com/blog/boyden/21839/).}