Growing bio-inspired polymer brains for artificial neural networks


Date:
July 5, 2023
Source:
Osaka University
Summary:
A new method for connecting neurons in neuromorphic wetware has been
developed. The wetware comprises conductive polymer wires grown in
a three-dimensional configuration, done by applying square-wave
voltage to electrodes submerged in a precursor solution. The
voltage can modify wire conductance, allowing the network to be
trained. This fabricated network is able to perform unsupervised
Hebbian learning and spike-based learning.


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FULL STORY ==========================================================================
A new method for connecting neurons in neuromorphic wetware has
been developed by researchers from Osaka University and Hokkaido
University. The wetware comprises conductive polymer wires grown in a three-dimensional configuration, done by applying square-wave voltage to electrodes submerged in a precursor solution. The voltage can modify wire conductance, allowing the network to be trained. This fabricated network
is able to perform unsupervised Hebbian learning and spike-based learning.

The development of neural networks to create artificial intelligence in computers was originally inspired by how biological systems work. These 'neuromorphic' networks, however, run on hardware that looks nothing
like a biological brain, which limits performance. Now, researchers from
Osaka University and Hokkaido University plan to change this by creating neuromorphic 'wetware'.

While neural-network models have achieved remarkable success in
applications such as image generation and cancer diagnosis, they still
lag far behind the general processing abilities of the human brain. In
part, this is because they are implemented in software using traditional computer hardware that is not optimized for the millions of parameters
and connections that these models typically require.

Neuromorphic wetware, based on memristive devices, could address this
problem.

A memristive device is a device whose resistance is set by its history
of applied voltage and current. In this approach, electropolymerization
is used to link electrodes immersed in a precursor solution using wires
made of conductive polymer. The resistance of each wire is then tuned
using small voltage pulses, resulting in a memristive device.

"The potential to create fast and energy-efficient networks has been shown using 1D or 2D structures," says senior author Megumi Akai-Kasaya. "Our
aim was to extend this approach to the construction of a 3D network."
The researchers were able to grow polymer wires from a common polymer
mixture called 'PEDOT:PSS', which is highly conductive, transparent,
flexible, and stable. A 3D structure of top and bottom electrodes was
first immersed in a precursor solution. The PEDOT:PSS wires were then
grown between selected electrodes by applying a square-wave voltage
on these electrodes, mimicking the formation of synaptic connections
through axon guidance in an immature brain.

Once the wire was formed, the characteristics of the wire, especially
the conductance, were controlled using small voltage pulses applied
to one electrode, which changes the electrical properties of the film surrounding the wires.

"The process is continuous and reversible," explains lead author Naruki Hagiwara, "and this characteristic is what enables the network to be
trained, just like software-based neural networks." The fabricated
network was used to demonstrate unsupervised Hebbian learning (i.e.,
when synapses that often fire together strengthen their shared connection
over time). What's more, the researchers were able to precisely control
the conductance values of the wires so that the network could complete
its tasks. Spike-based learning, another approach to neural networks that
more closely mimics the processes of biological neural networks, was also demonstrated by controlling the diameter and conductivity of the wires.

Next, by fabricating a chip with a larger number of electrodes and
using microfluidic channels to supply the precursor solution to each
electrode, the researchers hope to build a larger and more powerful
network. Overall, the approach determined in this study is a big step
toward the realization of neuromorphic wetware and closing the gap
between the cognitive abilities of humans and computers.

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========================================================================== Journal Reference:
1. Naruki Hagiwara, Tetsuya Asai, Kota Ando, Megumi Akai‐Kasaya.

Fabrication and Training of 3D Conductive Polymer Networks for
Neuromorphic Wetware. Advanced Functional Materials, 2023; DOI:
10.1002/ adfm.202300903 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2023/07/230705105850.htm

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