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/).}

Open Philanthropy

How can you solve the problems that really matter to you? If you're like most people, among these problems are terrifying, often fatal diseases: Parkinson's, Alzheimer's, cancer, stroke. Other problems you care about concern understanding and improving our humanity--understanding our consciousness, empowering citizens in democracies, and preserving dying languages, just to name a tiny few. One obvious way to focus resources on a problem you care about is to become rich and create or steer an institute--placing bets on specific researchers and on specific research agendas. But if you're like most people, you're not a billionaire. That means you have to be entrepreneurial about your idea, raising the funds through visionary leadership and strategic planning--a full-time job if there ever was one. Many such organizations are creatively tackling or rewarding efforts to cure diseases like ALS, cancer, and multiple sclerosis. But wouldn't it be great if you could focus your own resources--the ones you have right now--to solve the problems that you most care about?

One possibility is that if there were an open market for philanthropy, which would connect dollars with ideas, then you might be able to find researchers working on the problems you care most about, evaluate the investigators and their approaches, and empower them directly with funding. As resources pool, naturally, the most widespread and severe problems would receive the largest sums of money, providing a sort of free market for research philanthropy. Furthermore, your pool's funding members could vote to restrict the way your pool's money is used, adding in requirements to help you optimize the process, as you see fit. For example, your pool might decide to award cash only to scientists who agree to openly share data and samples with other awardees.

Such a system would also mean that you could solve the problems you care about earlier in your life, allowing you to benefit from them directly. You wouldn't have to wait until you're old and wealthy to start funding research to solve the Big Questions. Consider the following example. About 60 million Americans currently fit the criteria for obesity, which puts them at risk for diabetes (and dozens of other medical conditions). If 5 percent of these people desired to pool their resources to study ways of tackling metabolic impairments, and thereby improve health, and each contributed just $1.25 a month to researchers who focus exactly on the problems of most pressing interest, that would be $45 million a year. While that may not seem like much, note that the American Diabetes Association gave out about the same amount--$46.4 million--in research dollars in 2006.

What would this pooling system look like? Perhaps online microlending site Kiva could offer some inspiration. This site allows anyone browsing online to become a microlender--observing business plans of aspiring entrepreneurs around the world, evaluating the plans and gauging the risk involved, and then making loans--to specific individuals in countries from Honduras to Tajikistan. The lenders can monitor their investments over time (hopefully they return a profit), and they can provide feedback on the entrepreneurs, which modulates future investors' opinions. Individuals can lend an entrepreneur part of what he or she requests, or even distribute resources over many entrepreneurs. Perhaps it's time to start experimenting with a similar system for the channeling of funding to researchers. Research projects could be announced to the world on such a website, and anyone could donate as they see fit. By opening up the process to the world, not only would a market for philanthropy and discovery emerge, but people would take a greater interest in solving the problems they care most about. In this way, we can all start to steer the future of humanity from our own chairs, fixing the impossible and figuring out the unknowable.

^{Cite as: Boyden, E. S. "Open Philanthropy." Ed Boyden's Blog. Technology Review. 9/24/07. (http://www.technologyreview.com/blog/boyden/21850/).}