• October Special Meeting: "SPACE-TIME COMPUTATION AND THE BRAIN"

• Event:  UW-Madison ECE Department/IEEE Meeting
• Date/Time: Tuesday, October 24th,  from 4:00 PM until 5:00 PM
• Speaker: Dr. James E. Smith, Emeritus Professor
  
• Location: 
      UW-Madison Computer Science
      Room CS1240
      1210 West Dayton Street
      Madison,  WI, 53706
  
• RSVP: Please Register at the IEEE Madison Section event page.

Topic:

For more than a decade, a grand challenge posed to computer researchers has been to understand, and eventually replicate, the way the brain computes – “reverse engineer the brain”, so to speak. Despite its universally recognized importance, computer researchers have made little forward progress. In fact, theoretical neuroscientists have assumed leadership in architecting plausible computing models and consequently have taken significant first steps toward solving the problem. The challenge needn’t be cast in strictly neuroscience terms. It can also be addressed in computer architecture terms.

The research reported here supports the computer architecture perspective by proposing a computation model that includes an important class of neuroscientific models as a subset and which possesses at least some of the look-and-feel of conventional computer design methods. Rather than being based on logical principles, however, it is a radically different model based on temporal principles. This talk first describes biologically-based neuron models commonly used by the neuroscience community. Biological neurons communicate and compute using information encoded as voltage pulses, or spikes.

The focus here is on an important class of spiking neuron models in which information is conveyed and processed via precise spike timing relationships measured across multiple communication paths. Then, a “space-time” algebra is proposed as a way of capturing the essential features of the spiking neural networks that we are targeting. The algebra models the passage of time among inter-operating spatial computing elements (e.g., neurons ). Spiking neuron models, as envisioned by neuroscience researchers, can be implemented using the primitives of the proposed space-time algebra. T

his construction and modeling approach is aligned with conventional computer design methods and is very different from the current neuroscience approach of discretizing real-valued biologically-based models. Finally, potential applications of space-time algebra may be much broader than spiking neural networks. It is shown that space-time algebra also supports a generalization of “race logic”. A key feature of race logic is that it can be directly implemented with off-the-shelf CMOS digital circuits with times of signal transitions (edges) representing temporally coded values. An important implication is that we may be able to design cognitive systems in the spiking neural network domain, and then, by representing temporal events as voltage edges rather than spikes, implement them directly using off-the-shelf CMOS.

Speaker:

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 early 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.  

Currently, he is studying new computing paradigms at home along the Clark Fork near Missoula, Montana.