rons and how those neurons communicate the brain's fundamental currency of electricity. Regardless of their apparent success, models of how we think that are not consistent with this knowledge engender skepticism among neuroscientists.
Fidelity to biology has long been a flashpoint in the debate over the usefulness of neural nets. Philosophers, some psychologists, and many in the artificial intelligence community tend to devise and to favor "top-down" theories of the mind, whereas working neuroscientists who experiment with brain tissue approach the question of how the brain works from the "bottom up." The limited nature of top-down models, and the success of neuroscientists in teasing out important insights by direct experiments on real brain tissue, have swung the balance over the last three decades, such that most modelers now pay much more than lip service to what they call "the biological constraints.'' Also at the Frontiers symposium was William New-some. Koch described him as an experimental neurophysiologist who embodies this latter approach and whose "recent work on the brains of monkeys has suggested some fascinating new links between the way nerve cells behave and the perception they generate."
Increasingly, modelers are trying to mimic what is understood about the brain's structure. Said James Bower, a session participant who is an authority on olfaction and an experimentalist and modeler also working at the California Institute of Technology, "The brain is such an exceedingly complicated structure that the only way we are really going to be able to understand it is to let the structure itself tell us what is going on." The session's final member, Paul Adams, an investigator with the Howard Hughes Medical Institute at SUNY, Stony Brook, is another neuroscientist in the bottom-up vanguard looking for messages from the brain itself, trying to decipher exactly how individual nerve cells generate the electrical signals that have been called the universal currency of the brain. On occasion, Adams collaborates with Koch to make detailed computer models of cell electrical behavior.
Together the session's participants embodied a spectrum of today's neuroscientists—Koch, Sejnowski, and Ullman working as theoreticians who are all vitally concerned with the insights from neurobiology provided by their experimentalist colleagues, such as Bower and Adams. From these diverse viewpoints, they touched on many of the elements of neural nets—primarily using studies of vision as a context—and provided an overview of the entire subject: its genesis as spurted by the promise of the computer, how traditional neuroscientists at first resisted and have slowly become more open to the promise of neural networks, how the delicate and at times hotly de-