Home Page for Eric Basham

Eric Basham is a Ph.D. candidate at the University of California Santa Cruz. He completed his M.S. in Electrical Engineering at San Jose State University, and his B.S. in Microbial Biotechnology at Rutgers University. His interests include analog electronics (especially field reconfigurable devices), novel computational circuits and integrated biologically compatible microelectromechanical systems. He can be contacted at ebasham AT soe dot ucsc dot edu.

Current Project

Natural (Biological) Neural Network Analysis Natural (Biological) Neural Network Analysis As the level of integration of electronic circuits grows, several fundamental issues are rising to the forefront. Among these, the development of novel computational methods, reduction of power consumption, error tolerant computation, and assembly and manufacture of ever smaller devices are primary [1]. One can observe the solution to these problems in natural systems, especially in the study of the brain. While the details remain unclear, the brain displays an enormous computational capability at both low frequency and low power operation. As a computation signal processing unit, natural neural systems are also self-assembling and alter over time to adapt to both damage and additional computational requirements. It is clear that a more in depth understanding of the structure, function and signal processing capabilities of these systems is fertile ground for innovation. This is the driving force behind neural electronics.

To enable analysis of neural communication, a system which interfaces with living neurons, can stimulate and monitor neurons and allows cell to cell connection, is required. Figure 1 shows a system diagram which would allow cell to cell communication that can be mediated electronically as well. Each of the arrows represents an engineering challenge. For example, the current state of the art in recording neural signals is multielectrode arrays. While this is a mature, commonly used technique, it suffers from technological limitations. In one project, the use of inductive stimulation is being considered as an alternative. Likewise, the state of the art for recording involves the use of sensitive amplifiers off chip and electrode pickups. In another project, the use of open gate field effect transistors which pick up the minute currents generated by the action potential is being considered. While both of these projects focus on the “read” and “write” aspects of neural interfacing, the third project focuses on the biological model. What is the appropriate biological model to study in vitro? How can it be scaled to higher densities? Is there any correlation to the system behavior in vitro and the computational abilities of intact systems in nature? These questions require both and in depth understanding of biology (tissue culture), electrophysiological techniques and engineering disciplines including device fabrication and MEMs process development. The table below summarizes some of the common models and the pitfalls, pros and cons of working with them.

The IBR group at UCSC headed by Professor Wentai Liu offers unique opportunities to study truly integrated bioengineering problems. In this lab, both medically relevant applications and theory biomimetics are pursued.

---

[1] ITRS grand challenges; specifically challenges 11, 27, 30, and 31. SRC Grand Challenges