I would try to open their eyes to the career possibilities in the computing profession that lie beyond the bounds of the "usual suspects". In my mind, this is one of the messages we as educators need to emphasize: our profession pervades almost all aspects of modern life, and there are opportunities for rewarding careers in many places that would be considered "non-traditional," combining CS knowledge with knowledge in some other area (such as biology). The talk abstract was written before I had this approach completely figured out, but captures at least some of the CS interest in the field:
Five years past the end of the "Decade of the Brain", our ability to gather information about nervous systems is truly amazing. We can now conduct experiments that generate petabytes of data imaging a human brain as a person thinks. We can record from molecule-sized ion channels in nerve cell membranes. We can modify the genetic code of test animals to custom design their nervous systems for experimental purposes. And yet, for all of our advances in methods and knowledge about the brain, basic aspects of nervous system function still lay beyond our understanding.Topics: academia, research, neural networks.
In this talk, Prof. Michael Stiber will discuss nervous systems "in the small"; single neurons and networks of a relatively few cells. While incredibly simple compared to a human brain, these small systems are the building blocks of all neural computation. He will discuss the immense diversity of behaviors that these small systems exhibit; behaviors which could imply computational capabilities far in excess of that which we usually credit them. These capabilities include switching between linear and nonlinear operating modes, dynamically changing operating characteristics over time scales ranging from milliseconds to years, altering molecular or physical structure based on information encoded in the genome, responding to centrally broadcast commands, and even error correction. If the simplest components of nervous systems have such complex capabilities, what does this say for the computational power of the human brain?