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Markus Meister
Can the closed eyes of an unborn baby produce messages that help normal brain development in the womb? It may seem strange to think so, but the answer is yes. MCB Professor Markus Meister made that startling discovery as a postdoc in Denis Baylor’s laboratory at Stanford, along with colleagues Rachel Wong and Carla Shatz, the current Chair of the Neurobiology Department at Harvard Medical School. The finding launched a prolific career in neurobiology. Meister has since become a leading figure in the field—a pioneer who tracks and deciphers neuronal circuits in the retina and their intimate connections with brain physiology. His studies have revealed numerous, and often surprising, aspects of the retina’s capacity to organize and process information. Due largely to his efforts, scientists now know the eyes play dynamic roles in the hardwiring of the nervous system.

Now, Meister wants to establish basic principles for neurobiology that could simplify views of the brain and its neural circuitry. How so? Meister points to molecular biology, proposing that its “fantastic success” can be attributed in part to some core principles that guide researchers in the field. “By this, I mean the idea that DNA makes RNA, and RNA makes proteins, and proteins bind to DNA, and so forth,” Meister explains. “That sort of central dogma allows people to orient their efforts . . . and to collaborate—what happens in a yeast lab might have direct consequences to what happens in a cancer lab.” By comparison, neurobiology has no central dogma, he says, and consequently, scientists in the field don’t work together as much as they could. “Researchers are split among different lines depending on what animals they work with, or what parts of the brain they’re working on,” Meister says. “So, there may be 35,000 members in the national society but only 50 or so are talking to each other at any given time.”

To Harvard via California

What principles might be applied to neurobiology? Meister’s answer to that question draws on an academic history that began in Europe. Meister was born in Germany, grew up in Italy, and then returned to Germany to attend the Technical University of Munich, where he studied physics. He stayed in Munich only three years, however, leaving in 1980 for a year at Caltech that ultimately turned into a PhD in biophysics. At Caltech, Meister studied bacterial locomotion under Howard Berg (now at MCB), who provided his 12-year-old son Henry as a collaborator. “This was when tabletop computers were just coming out,” Meister recalls. “We were working with the Apple 2; Henry knew the contents of every memory address for that machine and he was very funny. He hadn’t even gone through the voice change yet, so I had this squeaky kid telling me what to do. There was no way around him.”

In 1987, Meister left Caltech and came to Stanford for the notable postdoc that produced his groundbreaking research on the retina. That work was done using electrode arrays that Meister still uses now to probe information processing in large sets of neurons. The technology allows him to record neuronal signals in parallel, and helped identify activity patterns in the developing retina that have since been shown to be highly sophisticated. Meister says recent research indicates that interfering with this early neural activity alters hardwiring in the brain.

Meister himself, meanwhile, has turned from developmental studies towards investigations of the adult retina, which he finds more satisfying. He came to Harvard at Berg’s urging, and once here, began to focus on how the adult retina processes visual images. “I want to understand how retinal output relates to visual input,” he explains. “So, we study what neuronal computations happen in the retina and more recently, we’ve gotten into the underlying mechanisms.”

A Principled View

As an example of a basic principle of brain function, Meister cites a phenomenon called lateral inhibition. By this process, adjacent neurons in a circuit inhibit each other’s activity to accentuate “edges” in a given stimulus. To illustrate how this works, consider what you see around you—visual images comprise areas of contrasting light intensity, some uniformly dark, and others brighter. Through the process of lateral inhibition, a retinal neuron computes differences between the intensity in a local patch and that in the surrounding region. Neurons within uniformly intense regions send little or no output signal to the brain. Neurons located at a contrast edge, on the other hand, report large difference signals. Meister compares the process to image compression algorithms used by computers. “In storing a compressed image, the computer doesn’t write down the intensity of every pixel, it just notes changes at a contrast edge,” he explains. “The retina does the same thing: it uses lateral inhibition to emphasize changes, and that saves a lot of neural signaling. This relates to an ‘efficient coding’ hypothesis that we can apply to many areas of the brain.”

Acknowledging that biological principles should apply “in at least two places,” Meister has begun to explore how lateral inhibition discriminates among scents in the olfactory bulb—the part of the brain that processes odors. His findings show olfaction and vision share remarkable similarities: for instance, the light-sensing molecule called rhodopsin, which resides on retinal photoreceptors, is structurally nearly identical to frontline smell receptor molecules in the olfactory system. Other parallels abound, among them lateral inhibition among the output neurons of the olfactory bulb, called mitral cells. Meister suspects odors have contrast edges just as visual images do. “An animal might need to distinguish ripe from rotten fruits,” he says. “Both give off odors with similar components, and the olfactory bulb may serve to compute the differences. This ‘contrast edge’ discriminates one odor from the other and triggers a specific behavioral response: eat or don’t eat.”

Meister predicts that lateral inhibition holds true in higher neural functions, including decision making. Circuits that govern decision making likely contain pools of neurons that dictate binary reactions, such as “jump or don’t jump,” or “stay or don’t stay,” he explains. “And lateral inhibition provides a mechanism that forces these decision processes and makes them crisper,” he adds. “That way, pools of neurons that start to win out in terms of a given reaction inhibit those for the opposite reaction.”

At present, two projects play an important role in Meister’s research. One investigates how environmental features influence lateral inhibition in the retina, and how the retina in turn “selects” messages to send to the brain. His results show the retina adapts to its visual environment and maintains some flexibility in terms of its message response patterns. When these results were published last year in Nature, reporters seized on the concept of “thinking eyeballs,” an analogy Meister won’t dismiss. “This is an exciting trend that we’re following up on,” he says. “It puts a different picture on the retina—it’s not a static processing machine that images go through before they reach the brain, but a dynamic prefilter that can be adjusted according to environmental needs.”

In another pending project, Meister collaborates with Alan Litke from the University of California at Santa Cruz on efforts to create a “wireless rat.” With this project, he and his colleague will affix wireless transmitters directly to rat neurons, allowing them to monitor brain function among free ranging, unanaesthetized animals. “Increasing evidence shows that what we observe in the anaesthetized brain is a pale shadow of what the brain does when it’s fully engaged,” Meister says.

Meister credits his Harvard collaborators with helping to keep his research on track. Colleagues in psychology, he says, keep him focused on what animals do with their nervous systems while Harvard physicists provide input on neuronal modeling. Meanwhile, neurobiology offers Meister a broad expanse of uncharted territory, in which—he suggests—many of the simplest questions remain unanswered.