Research Article Open Access

Impedance Analysis of Heat Treated Polyethylene Oxide Polymeric Material for a Neural Storage Application

Mahmoud Z. Iskandarani

Abstract

Problem statement: From the early days of, researchers have developed electronic models of neurons designed to emulate neural behavior with electrical signals that mimic in some ways the measured potentials of biological neurons. Researchers interested in fabricating artificial neurons have long sought a simple and techniques to produce devices that efficiently store synaptic weights, which is behind holding a particular state in relation to conductance parameters. As Engineers become closer to realizing accurate hardware models of neurons, the need for a simple analog memory device grows correspondingly. To determine the storage characteristics of polyethylene oxide based polymer as the base material for high charge storage analogue neural switch. Approach: Various devices prepared under controlled conditions. Each device tested for its impedance characteristics as a function of both frequency and temperature. Mathematical model developed to account for the obtained characteristics. Results: The heat treated devices showed stability, repeatability and ability to store enough charge for long time periods. Impedance analysis proved a similar response to the actual neural switches. Conclusion: The symmetrical behavior for such devices opened a wide application area for the manufacturing of low and high frequency analogue devices for intelligent system applications.

American Journal of Applied Sciences
Volume 6 No. 7, 2009, 1364-1367

DOI: https://doi.org/10.3844/ajassp.2009.1364.1367

Submitted On: 19 March 2009 Published On: 31 July 2009

How to Cite: Iskandarani, M. Z. (2009). Impedance Analysis of Heat Treated Polyethylene Oxide Polymeric Material for a Neural Storage Application . American Journal of Applied Sciences, 6(7), 1364-1367. https://doi.org/10.3844/ajassp.2009.1364.1367

  • 3,369 Views
  • 2,330 Downloads
  • 0 Citations

Download

Keywords

  • Neural
  • modeling
  • memory
  • information processing
  • polymers
  • storage