Memristive networks: from graph theory to statistical physics

A simple solvable model of memristive networks suggests a correspondence between the asymptotic states of memristors and the Ising model.

EPL 125, 1 (2019)

A. Zegarac, F. Caravelli

Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics
Memristive networks: from graph theory to statistical physics

We provide an introduction to a very specific toy model of memristive networks, for which an exact differential equation for the internal memory which contains the Kirchhoff laws is known. In particular, we highlight how the circuit topology enters the dynamics via an analysis of directed graph. We try to highlight in particular the connection between the asymptotic states of memristors and the Ising model, and the relation to the dynamics and statics of disordered systems.

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