Related Experiment Video
Updated: Sep 8, 2025

A Method for Growing Bio-memristors from Slime Mold
Published on: November 2, 2017
Learning by mistakes in memristor networks
Juan Pablo Carbajal1, Daniel A Martin2, Dante R Chialvo2
1Institute for Energy Technology, University of Applied Sciences of Eastern Switzerland, Oberseestrasse 10, 8640 Rapperswil, Switzerland.
Abstract:
Recent results revived the interest in the implementation of analog devices able to perform brainlike operations. Here we introduce a training algorithm for a memristor network which is inspired by previous work on biological learning. Robust results are obtained from computer simulations of a network of voltage-controlled memristive devices. Its implementation in hardware is straightforward, being scalable and requiring very little peripheral computation overhead.
More Related Videos
Related Concept Videos
MOS Capacitor
The metal gate is typically made from highly conductive materials such as aluminum or polysilicon. Beneath the metal gate lies a thin layer of...
Understanding Memory
Network Function of a Circuit
Current Growth And Decay In RL Circuits
Neural Circuits
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Resting Membrane Potential
The Inside of a Neuron is More Negative
The membrane potential of a cell can be measured by inserting a microelectrode into a cell and comparing the charge to a reference electrode in the extracellular fluid. The...

