Journal
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
Volume 24, Issue 9, Pages 1437-1448Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNNLS.2013.2261545
Keywords
Adaptive frequency; amoeba; dynamical systems; learning; memcapacitive system; memory; memristor; synchronization
Categories
Funding
- Spanish government [TEC2011-14253-E]
- NSF [DMR-0802830, ECCS-1202383]
- Center for Magnetic Recording Research at UCSD
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1202383] Funding Source: National Science Foundation
Ask authors/readers for more resources
Adaptive response to varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. We consider memory models inspired by an intriguing ability of slime molds to both memorize the period of temperature and humidity variations and anticipate the next variations to come, when appropriately trained. Effective circuit models of such behavior are designed using: 1) a set of LC contours with memristive damping and 2) a single memcapacitive system-based adaptive contour with memristive damping. We consider these two approaches in detail by comparing their results and predictions. Finally, possible biological experiments that would discriminate between the models are discussed. In this paper, we also introduce an effective description of certain memory circuit elements.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available