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Conducting-insulating transition in adiabatic memristive networks
Forrest C Sheldon1, Massimiliano Di Ventra1
1Department of Physics, University of California San Diego, La Jolla, California 92093, USA.
Physical Review. E
|February 18, 2017
Summary
Memristive networks show a conductivity shift at high memory levels, impacting their use in neuromorphic and unconventional computing. This study explores network dynamics and current-voltage behavior.
Area of Science:
- Materials Science
- Condensed Matter Physics
- Computational Neuroscience
Background:
- Neuromorphic systems require understanding memristive network dynamics.
- Memristive elements possess memory, crucial for network behavior.
Purpose of the Study:
- To investigate the collective dynamics of disordered memristive networks.
- To analyze the impact of memory (ON-OFF ratio) on network conductivity.
- To explore current-voltage characteristics and switching processes.
Main Methods:
- Utilizing an experimentally inspired model of 2D disordered memristive networks.
- Applying a slowly ramped voltage stimulus.
- Conducting simulations and theoretical analysis.
- Investigating current-voltage duality.
Main Results:
- A discontinuous transition in conductivity was observed for high memristive memory values.
- The study characterized the current-voltage behavior associated with this transition.
- Current-voltage duality was shown to link forward and reverse switching.
Conclusions:
- The findings illuminate the statistical properties of memristive networks.
- This research is vital for advancing unconventional computing and neural network models.
- Understanding network transitions is key for developing functional neuromorphic systems.
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