Researchers have developed a brain-like computing device that is capable of
learning by association.
Similar to how famed physiologist Ivan Pavlov conditioned dogs to associate
a bell with food, researchers at Northwestern Engineering and the University
of Hong Kong successfully conditioned their circuit to associate light with
pressure.
The research was published April 30 in the journal Nature Communications.
The device’s secret lies within its novel organic, electrochemical “synaptic
transistors,” which simultaneously process and store information just like
the human brain. The researchers demonstrated that the transistor can mimic
the short-term and long-term plasticity of synapses in the human brain,
building on memories to learn over time.
With its brain-like ability, the novel transistor and circuit could
potentially overcome the limitations of traditional computing, including
their energy-sapping hardware and limited ability to perform multiple tasks
at the same time. The brain-like device also has higher fault tolerance,
continuing to operate smoothly even when some components fail.
“Although the modern computer is outstanding, the human brain can easily
outperform it in some complex and unstructured tasks, such as pattern
recognition, motor control and multisensory integration,” said
Northwestern’s Jonathan Rivnay, a senior author of the study. “This is
thanks to the plasticity of the synapse, which is the basic building block
of the brain’s computational power. These synapses enable the brain to work
in a highly parallel, fault tolerant and energy-efficient manner. In our
work, we demonstrate an organic, plastic transistor that mimics key
functions of a biological synapse.”
Rivnay is an assistant professor of biomedical engineering at Northwestern’s
McCormick School of Engineering. He co-led the study with Paddy Chan, an
associate professor of mechanical engineering at the University of Hong
Kong. Xudong Ji, a postdoctoral researcher in Rivnay’s group, is the paper’s
first author.
Problems with conventional computing
Conventional, digital computing systems have separate processing and storage
units, causing data-intensive tasks to consume large amounts of energy.
Inspired by the combined computing and storage process in the human brain,
researchers, in recent years, have sought to develop computers that operate
more like the human brain, with arrays of devices that function like a
network of neurons.
“The way our current computer systems work is that memory and logic are
physically separated,” Ji said. “You perform computation and send that
information to a memory unit. Then every time you want to retrieve that
information, you have to recall it. If we can bring those two separate
functions together, we can save space and save on energy costs.”
Currently, the memory resistor, or “memristor,” is the most well-developed
technology that can perform combined processing and memory function, but
memristors suffer from energy-costly switching and less biocompatibility.
These drawbacks led researchers to the synaptic transistor — especially the
organic electrochemical synaptic transistor, which operates with low
voltages, continuously tunable memory and high compatibility for biological
applications. Still, challenges exist.
“Even high-performing organic electrochemical synaptic transistors require
the write operation to be decoupled from the read operation,” Rivnay said.
“So if you want to retain memory, you have to disconnect it from the write
process, which can further complicate integration into circuits or systems.”
How the synaptic transistor works
To overcome these challenges, the Northwestern Engineering and University of
Hong Kong team optimized a conductive, plastic material within the organic,
electrochemical transistor that can trap ions. In the brain, a synapse is a
structure through which a neuron can transmit signals to another neuron,
using small molecules called neurotransmitters. In the synaptic transistor,
ions behave similarly to neurotransmitters, sending signals between
terminals to form an artificial synapse. By retaining stored data from
trapped ions, the transistor remembers previous activities, developing
long-term plasticity.
The researchers demonstrated their device’s synaptic behavior by connecting
single synaptic transistors into a neuromorphic circuit to simulate
associative learning. They integrated pressure and light sensors into the
circuit and trained the circuit to associate the two unrelated physical
inputs (pressure and light) with one another.
Perhaps the most famous example of associative learning is Pavlov’s dog,
which naturally drooled when it encountered food. After conditioning the dog
to associate a bell ring with food, the dog also began drooling when it
heard the sound of a bell. For the neuromorphic circuit, the researchers
activated a voltage by applying pressure with a finger press. To condition
the circuit to associate light with pressure, the researchers first applied
pulsed light from an LED lightbulb and then immediately applied pressure. In
this scenario, the pressure is the food and the light is the bell. The
device’s corresponding sensors detected both inputs.
After one training cycle, the circuit made an initial connection between
light and pressure. After five training cycles, the circuit significantly
associated light with pressure. Light, alone, was able to trigger a signal,
or “unconditioned response.”
Future applications
Because the synaptic circuit is made of soft polymers, like a plastic, it
can be readily fabricated on flexible sheets and easily integrated into
soft, wearable electronics, smart robotics, and implantable devices that
directly interface with living tissue and even the brain.
“While our application is a proof of concept, our proposed circuit can be
further extended to include more sensory inputs and integrated with other
electronics to enable on-site, low-power computation,” Rivnay said. “Because
it is compatible with biological environments, the device can directly
interface with living tissue, which is critical for next-generation
bioelectronics.”
Reference:
Xudong Ji, Bryan D. Paulsen, Gary K. K. Chik, Ruiheng Wu, Yuyang Yin, Paddy
K. L. Chan, Jonathan Rivnay. Mimicking associative learning using an
ion-trapping non-volatile synaptic organic electrochemical transistor.
Nature Communications, 2021; 12 (1) DOI:
10.1038/s41467-021-22680-5