"Bridging Electrical Engineering and Neuroscience"

Here in my lab at the University of Illinois, in cooperation with Todd Coleman and Rhanor Gilette, who are my advisors, we look into bridging neuroscience and electrical engineering. What we think is that in the brain one of the theories of addiction assumes that there is a rewiring in the brain; or that addiction results in the same pathway as learning, and the same pathway as reward for feeding habits. This is the hypothesis we are using with our animals (Sea Slugs). We are trying to see, when we have these addicted animals, if what is happening in the reward network and in the reward pathway is really changing. We have these very simple animals (sea slugs) that have very simple brains and they have a set of behaviors; they are not as complex as humans that have many behaviors. They have a very finite set of behaviors and we try to understand how their simple brains coordinate that behavior. The slugs are a good choice for us to study compared with the complex human brain because those animals have a limited number of neurons, they are bigger than human neurons, and you can really track down what's activating what and what's directing what behavior.

We have this idea that an animal is trying to solve or optimize his cost function, we are using both Markov processes and optimization theories - so that we can model the animal's behavior. What we want to do is to observe both the normal animals and then have an addicted animal and see how that cost function changes. So if the animal is going around - and we work with sea slugs, they are natural predators, so if we can alter its behavior through addiction we want to understand how that cost function is changing and how the neural processes are changing. So that's when we are bridging electrical engineering with the math of Markov processes and inverse optimal control theory with neuroscience and neurobiology.

I think one of the first things that would come out is if we could show how these optimizations are happening and how the cost function and the rewards of the animal change when he is addicted versus when is not addicted. These algorithms we are working on in collaboration with other students, I think that we really could bring a different approach to the way we analyze. We are also planning on bringing neural recordings so that it will also be unique.

The model we are using - the inverse optimal control model - can be used not only for this specific animal, but it is a very generic, per se, algorithm that can be applied to different animals and different models.

I've found that when you start working with inter-disciplinary research, you have to be comfortable with going beyond your boundaries. For me, as an electrical engineer, I think everything in terms of logic and math - either yes or no, on or off, binary in essence- and when you go into science, you have this continuum that you have to be comfortable working with and experiments that are not a simulation in a computer and you have to be there present; it can work or it can fail and you just have to be comfortable with that. But it is also very challenging; it is one of the things I enjoy the most: just being able to, for me as an electrical engineer, doing dissections and separating neurons. I think it's really amazing. I would welcome discussion, questions, and comments on my work in the Center. Thanks!