Intelligence emerging from random polymer networks

Date:
October 6, 2021
Source:
Osaka University
Summary:
A team of researchers assembled a sulfonated polyaniline (SPAN)
organic electrochemical network device (OEND) for use in reservoir
computing.

SPAN was deposited on gold electrodes which formed a disordered
network providing humidity-dependent electrical properties. The
SPAN OEND was tested for reservoir computing using benchmark tasks
and spoken-digit classification, which showed 70% accuracy. The
device has the potential to be applied to a wide range of artificial
intelligence tasks including speech recognition.



FULL STORY ========================================================================== Reservoir computing (RC) tackles complex problems by mimicking the way information is processed in animal brains. It relies on a randomly
connected network that serves as a reservoir for information and
ultimately leads to more efficient outputs. For realizing RC directly
in matter (instead of simulating it in a digital computer), numerous
reservoir materials have been investigated to date. Now a team including researchers from Osaka University has designed a sulfonated polyaniline
network for RC.


========================================================================== Neural networks in the brain use electrochemical signals carried by ions.

Therefore, an electrochemical approach is a logical choice when choosing a material system for RC. Organic electrochemical field-effect transistors (OECFETs) are popular in bioelectronics; however, they have not yet been
widely used for RC.

The key to the reservoir material is that it has rich (time-dependent)
behavior and is disordered, which makes polymer materials an excellent
option as they form random networks by themselves.

Polyaniline is a promising polymer for RC applications, because it is easy
to polymerize, has good stability in the atmosphere, and has reversible doping/de- doping behavior, which means its conduction can be altered.

The researchers investigated sulfonated polyaniline (SPAN), which, in
addition to the advantages of polyaniline, has high water-solubility
and self-doping behavior. These make SPAN easier to work with and the
doping more uniform.

"Atmospheric protons are injected directly into the polymer chain
of SPAN, which causes it to conduct," explains study lead author Yuki
Usami. "This conduction can then be controlled by adjusting the humidity."
The researchers used a simple drop-casting method to assemble the SPAN on
gold electrodes to give an organic electrochemical network device (OEND).

The SPAN OEND was tested for RC by checking the waveform and assessing
its performance in short-term memory tasks. Results of a test to see how
well speech could be recognized achieved 70% accuracy. This ability of
SPAN OEND was comparable with a software simulation of RC.

"We have shown that our SPAN OEND system can be applied in RC," says study corresponding author Takuya Matsumoto. "Future steps to establish systems
that do not rely on humidity will provide more practical options; however,
the success of our SPAN-based system is a positive step for material-based reservoir computing, which is expected to have a significant
impact on the next generation of artificial intelligence devices." ========================================================================== Story Source: Materials provided by Osaka_University. Note: Content may
be edited for style and length.


========================================================================== Journal Reference:
1. Yuki Usami, Bram Ven, Dilu G. Mathew, Tao Chen, Takumi Kotooka, Yuya
Kawashima, Yuichiro Tanaka, Yoichi Otsuka, Hiroshi Ohoyama, Hakaru
Tamukoh, Hirofumi Tanaka, Wilfred G. Wiel, Takuya Matsumoto.

In‐Materio Reservoir Computing in a Sulfonated Polyaniline
Network.

Advanced Materials, 2021; 2102688 DOI: 10.1002/adma.202102688 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2021/10/211006104421.htm

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