Todd P. Coleman

As he was finishing his doctoral work at MIT, Todd Coleman's friends from his undergraduate days at the University of Michigan were urging him to put his scientific knowledge and academic credentials to use in ways that would benefit people.

"There were a bunch of people from Michigan who ended up in Boston, at Harvard Medical School or MIT, and almost all of them were working on something that had some sort of biological angle to it," Coleman said. "They were doing M.D./Ph.D.s or doing biological imaging. They all encouraged me to take all this hard core math I was learning for my Ph.D. and apply it toward helping out mankind."

Upon earning his Ph.D. in Electrical Engineering, Coleman was offered a faculty position at the University of Illinois but his Master's and Ph.D. advisor, MIT Professor Muriel Medard, suggested he spend a postdoctoral year in an area different than the disciplines of computer science and electrical engineering he had excelled at during his academic career.

So Coleman listened to all the advice and investigated possibilities in the life sciences, discovering a research path rarely taken by electrical and computer engineers: neuroscience. One of the people Coleman talked with was Emery Brown, a doctor and well-known Professor of Computational Neuroscience from MIT's Department of Brain and Cognitive Science and the Neuroscience Statistics Research Laboratory at the famed Massachusetts General Hospital. After meeting with Brown, Coleman decided Medard was right.

"I knew that I was coming here to get the job and (Muriel) strongly encouraged me not to come right away but to postdoc a year in something completely different," Coleman said. "That would give me more time to mature as a thinker, to work on a completely different class of problems. So I decided to try and pursue something biological and that's how I ended up working with Emery Brown."

In Brown, Coleman found a researcher who is considered a leader in the area of mathematical modeling of neural systems and a mentor he could look up to.

"I had already chatted with him once so I knew him and heard very good things about him, heard people called him on a first name basis," Coleman said. "He had a very down to earth personality and was a first rate scientist and worked on very interesting, cool problems at the intersection of statistics and biology. So I noticed there was a natural opportunity for mentorship and as I got to know him it just naturally evolved in that direction. I would love to be on his level someday; that's quite something to aspire to."

Coleman is well on his way toward that goal. As a member of the Beckman Institute's Artificial Intelligence group and Assistant Professor of Electrical and Computer Engineering at Illinois, Coleman's research focuses on more traditional computer engineering topics but also includes, thanks to his postdoctoral stint at MIT, a large measure of computational neuroscience. He is applying his computer science and electrical engineering skills to neuroscience by using statistical and computational approaches toward understanding brain function and toward the development of applications like novel non-invasive brain-machine interfaces.

The work involving neuroscience could end up satisfying his friends at MIT who were urging him to take on work that would benefit people. Applications from Coleman's research in computational neuroscience could lead to applications such as a brain-machine interface that guides a prosthetic limb.

Coleman said it was during his post-doctoral work with Brown that he studied the probabilistic structure of how neurons in the brain communicate based on what are called spikes and discovered that the same theory could be applied to artificial systems.

"Any neuroscientist is trying to understand how the brain represents information," Coleman said. "There are lots of ways that one can think about doing that and the rubric that I am using primarily, which I learned in my postdoc, is by virtue of how the neuron spike trains encode information."

Coleman said that process "involves statistical reasoning with point processes.

"We know that neurons generate these little flickers of energy called action potentials and it's basically the timing at which they generate all these spikes that is carrying all the information," he added. "What I am interested in understanding is how is information about the environment, or information about intent, or information about sound, how is that specifically encoded in the timing of the spike trains and I like to use statistical principles to do that."