This site needs JavaScript to work properly. Please enable it to take advantage of the complete set of features!
Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation

Save citation to file

Add to Collections

Name must be less than 100 characters
Unable to load your collection due to an error
Please try again

Add to My Bibliography

Unable to load your delegates due to an error
Please try again

Your saved search

Would you like email updates of new search results?
Saved Search Alert Radio Buttons
()

Create a file for external citation management software

Your RSS Feed

. 2009 Aug;80(2 Pt 1):021926.
doi: 10.1103/PhysRevE.80.021926. Epub 2009 Aug 21.

Memristive model of amoeba learning

Affiliations

Affiliation

  • 1 Department of Physics, University of California, San Diego, La Jolla, California 92093-0319, USA. pershin@physics.sc.edu

Memristive model of amoeba learning

Yuriy V Pershin et al. Phys Rev E Stat Nonlin Soft Matter Phys. 2009 Aug.
. 2009 Aug;80(2 Pt 1):021926.
doi: 10.1103/PhysRevE.80.021926. Epub 2009 Aug 21.

Affiliation

  • 1 Department of Physics, University of California, San Diego, La Jolla, California 92093-0319, USA. pershin@physics.sc.edu

Erratum in

  • Phys Rev E Stat Nonlin Soft Matter Phys. 2010 Jul;82(1 Pt 2):019904

Abstract

Recently, it was shown that the amoebalike cell Physarum polycephalum when exposed to a pattern of periodic environmental changes learns and adapts its behavior in anticipation of the next stimulus to come. Here we show that such behavior can be mapped into the response of a simple electronic circuit consisting of a LC contour and a memory-resistor (a memristor) to a train of voltage pulses that mimic environment changes. We also identify a possible biological origin of the memristive behavior in the cell. These biological memory features are likely to occur in other unicellular as well as multicellular organisms, albeit in different forms. Therefore, the above memristive circuit model, which has learning properties, is useful to better understand the origins of primitive intelligence.

PubMed Disclaimer

Publication types

LinkOut - more resources

Cite
Morty Proxy This is a proxified and sanitized view of the page, visit original site.