Nanowire network is trained to solve a simple problem by mimicking neural pathways
From: University of Sydney
June 29,
2021 -- Some neuroscience theories suggest the human brain operates best 'at
the edge of chaos'. Now scientists have found that keeping a nanowire network
at the edge of becoming chaotic is the best state for it to produce useful
signals to solve problems.
Scientists
at the University of Sydney and Japan's National Institute for Material Science
(NIMS) have discovered that an artificial network of nanowires can be tuned to
respond in a brain-like way when electrically stimulated.
The
international team, led by Joel Hochstetter with Professor Zdenka Kuncic and
Professor Tomonobu Nakayama, found that by keeping the network of nanowires in
a brain-like state "at the edge of chaos," it performed tasks at an
optimal level.
This,
they say, suggests the underlying nature of neural intelligence is physical,
and their discovery opens an exciting avenue for the development of artificial
intelligence.
The
study is published today in Nature Communications.
"We
used wires 10 micrometres long and no thicker than 500 nanometres arranged
randomly on a two-dimensional plane," said lead author Joel Hochstetter, a
doctoral candidate in the University of Sydney Nano Institute and School of
Physics.
"Where
the wires overlap, they form an electrochemical junction, like the synapses
between neurons," he said. "We found that electrical signals put
through this network automatically find the best route for transmitting
information. And this architecture allows the network to 'remember' previous
pathways through the system."
ON
THE EDGE OF CHAOS
Using
simulations, the research team tested the random nanowire network to see how to
make it best perform to solve simple tasks.
If the
signal stimulating the network was too low, then the pathways were too
predictable and orderly and did not produce complex enough outputs to be
useful. If the electrical signal overwhelmed the network, the output was
completely chaotic and useless for problem solving.
The
optimal signal for producing a useful output was at the edge of this chaotic
state.
"Some
theories in neuroscience suggest the human mind could operate at this edge of
chaos, or what is called the critical state," said Professor Kuncic from
the University of Sydney. "Some neuroscientists think it is in this state
where we achieve maximal brain performance."
Professor
Kuncic is Mr Hochstetter's PhD adviser and is currently a Fulbright Scholar at
the University of California in Los Angeles, working at the intersection
between nanoscience and artificial intelligence.
She
said: "What's so exciting about this result is that it suggests that these
types of nanowire networks can be tuned into regimes with diverse, brain-like
collective dynamics, which can be leveraged to optimise information
processing."
OVERCOMING
COMPUTER DUALITY
In the
nanowire network the junctions between the wires allow the system to
incorporate memory and operations into a single system. This is unlike standard
computers, which separate memory (RAM) and operations (CPUs).
"These
junctions act like computer transistors but with the additional property of
remembering that signals have travelled that pathway before. As such, they are
called 'memristors'," Mr Hochstetter said.
This
memory takes a physical form, where the junctions at the crossing points
between nanowires act like switches, whose behaviour depends on historic
response to electrical signals. When signals are applied across these
junctions, tiny silver filaments grow activating the junctions by allowing
current to flow through.
"This
creates a memory network within the random system of nanowires," he said.
Mr
Hochstetter and his team built a simulation of the physical network to show how
it could be trained to solve very simple tasks.
"For
this study we trained the network to transform a simple waveform into more
complex types of waveforms," Mr Hochstetter said.
In the
simulation they adjusted the amplitude and frequency of the electrical signal
to see where the best performance occurred.
"We
found that if you push the signal too slowly the network just does the same
thing over and over without learning and developing. If we pushed it too hard
and fast, the network becomes erratic and unpredictable," he said.
The
University of Sydney researchers are working closely with collaborators at the
International Center for Materials Nanoarchictectonics at NIMS in Japan and
UCLA where Professor Kuncic is a visiting Fulbright Scholar. The nanowire
systems were developed at NIMS and UCLA and Mr Hochstetter developed the
analysis, working with co-authors and fellow doctoral students, Ruomin Zhu and
Alon Loeffler.
REDUCING
ENERGY CONSUMPTION
Professor
Kuncic said that uniting memory and operations has huge practical advantages
for the future development of artificial intelligence.
"Algorithms
needed to train the network to know which junction should be accorded the
appropriate 'load' or weight of information chew up a lot of power," she
said.
"The
systems we are developing do away with the need for such algorithms. We just
allow the network to develop its own weighting, meaning we only need to worry
about signal in and signal out, a framework known as 'reservoir computing'. The
network weights are self-adaptive, potentially freeing up large amounts of
energy."
This,
she said, means any future artificial intelligence systems using such networks
would have much lower energy footprints.
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