Abstract:
Developing a theoretical model (architecture) for the way the brain “computes” is a decades long research effort that still has a very long way to go. Experimentally validating such a model is commonly done via simulations on silicon-based computer systems, i.e. in silico, with the goal of demonstrating brain-like abilities and efficiencies. However, such indirect experimental validation is unsatisfying, if not unconvincing, compared with experimental validation using actual biological tissue (e.g., in vitro). Although implementing a theoretical model in vitro provides much stronger validation, it presents its own set of challenges regarding model implementation. These include intrinsic neuron reliability, synchronization, and speed. The important bottom line is that if the goal is to understand the way the brain computes, developing an accurate theoretical model is common to the research interests of both in vitro and in silico proponents. 

Bio: 
James E. Smith is Professor Emeritus in the Department of Electrical and Computer Engineering at the University of Wisconsin-Madison. He received his PhD from the University of Illinois in 1976. He then joined the faculty of the University of Wisconsin-Madison, teaching and conducting research ̶first in fault-tolerant computing, then in computer architecture. He has been involved in a number of computer research and development projects both as a faculty member at Wisconsin and in industry. Prof. Smith made a number of contributions to the development of superscalar processors. These contributions include basic mechanisms for dynamic branch prediction and implementing precise traps. He has also studied vector processor architectures and worked on the development of innovative microarchitecture paradigms. He received the 1999 ACM/IEEE Eckert-Mauchly Award for these contributions. For the past several years, he has been studying neuron-based computing paradigms at home along the Clark Fork near Missoula, Montana.