Neural network built in plastic

February 04, 2016 // By Peter Clarke
A team of scientists from Russia and Italy have created a neural network made from plastic memristors.

Kurchatov Institute, MIPT, the University of Parma , Moscow State University, and St. Petersburg State University – has demonstrated that it is possible to create simple polyaniline-based neural networks that are able to learn and perform logical operations. The work is reported in Organic Electronics.


A memristor is an electrical element with a variable resistance that depends on the charge passing through it and which displays memory. It has long-been noted that a memristor is similar to a biological synapse – a connection between two neurons in the brain that is able to modify the efficiency of signal transmission between neurons under the influence of the transmission itself.

To date studies of synapse-like behavior have been done in silicon-based memristor-type devices.

Using a polyaniline solution, a glass substrate, and chromium electrodes, team created a polymeric memristor at a scale of about 1 millimeter and multiple memristors were then connected in a single neuromorphic network. Under supervised learning the memristive network is capable of performing NAND or NOR logical operations.

This perceptron node is of macroscopic dimensions and with a characteristic reaction time of tenths or hundredths of a second, but the researchers state that this should scale with dimensions and the neural network is made using inexpensive materials. In addition, polyaniline can be used in attempts to make a three-dimensional structure by placing the memristors on top of one another in a multi-tiered structure.

Typical applications for neuromorphic computers include machine vision, hearing, and emulating other sensory organs, and also intelligent control systems for various devices, including autonomous robots and drones.

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