English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks

Sigala, R., Smerieri, A., Schüz, A., Camorani, P., & Erokhin, V. (2013). Modeling and simulating the adaptive electrical properties of stochastic polymeric 3D networks. Modelling and Simulation in Materials Science and Engineering, 21(7): 075007, pp. 1-17. doi:10.1088/0965-0393/21/7/075007.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-001A-133B-2 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-3DD5-B
Genre: Journal Article

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Sigala, R1, 2, Author              
Smerieri, A, Author
Schüz, Almut1, 2, Author              
Camorani, P, Author
Erokhin, V, Author
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

Content

show
hide
Free keywords: -
 Abstract: Memristors are passive two-terminal circuit elements that combine resistance and memory. Although in theory memristors are a very promising approach to fabricate hardware with adaptive properties, there are only very few implementations able to show their basic properties. We recently developed stochastic polymeric matrices with a functionality that evidences the formation of self-assembled three-dimensional (3D) networks of memristors. We demonstrated that those networks show the typical hysteretic behavior observed in the 'one input-one output' memristive configuration. Interestingly, using different protocols to electrically stimulate the networks, we also observed that their adaptive properties are similar to those present in the nervous system. Here, we model and simulate the electrical properties of these self-assembled polymeric networks of memristors, the topology of which is defined stochastically. First, we show that the model recreates the hysteretic behavior observed in the real experiments. Second, we demonstrate that the networks modeled indeed have a 3D instead of a planar functionality. Finally, we show that the adaptive properties of the networks depend on their connectivity pattern. Our model was able to replicate fundamental qualitative behavior of the real organic 3D memristor networks; yet, through the simulations, we also explored other interesting properties, such as the relation between connectivity patterns and adaptive properties. Our model and simulations represent an interesting tool to understand the very complex behavior of self-assembled memristor networks, which can finally help to predict and formulate hypotheses for future experiments.

Details

show
hide
Language(s):
 Dates: 2013-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: -
 Identifiers: DOI: 10.1088/0965-0393/21/7/075007
BibTex Citekey: SigalaSSCE2013
 Degree: -

Event

show

Legal Case

show

Project information

show

Source 1

show
hide
Title: Modelling and Simulation in Materials Science and Engineering
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 21 (7) Sequence Number: 075007 Start / End Page: 1 - 17 Identifier: -