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Home » Brain Like Computing Device Developed By Researchers That Simulates Human Learning

Brain Like Computing Device Developed By Researchers That Simulates Human Learning

Source : theweek

Washington [US], April 30 (ANI): During a recent study, researchers from Northwestern University & the University of Hong Kong developed a brain-like computer that’s capable of learning by association.The study was published in journal Nature Communications.

Similar to how famed physiologist Ivan Pavlov conditioned dogs to associate a bell with food, the researchers successfully conditioned their circuit to associate light with pressure.

The device’s secret lies within its novel organic, electrochemical synaptic transistors,” which simultaneously process and store information a bit like the human brain. The researchers demonstrated that the transistor can mimic the short-term & long-term plasticity of synapses within the human brain, building on memories to find out over time.

With its brain-like ability, the novel transistor & circuit could potentially overcome the restrictions of traditional computing, including their energy-sapping hardware and limited ability to perform multiple tasks at same-time. The brain-like device also has higher fault tolerance, continuing to work smoothly even when some components fail.

“Although new generation computer is outstanding, the human brain can easily outperform it in some complex and unstructured tasks, like pattern recognition, control and multisensory integration,” said Northwestern’s Jonathan Rivnay, a senior author of the study. “This is because of the plasticity of the synapse, which is that the basic building block of the brain’s computational power. These synapses enable the brain to work in 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 professor of biomedical engineering at Northwestern’s McCormick School of Engineering. He co-led the study with Paddy Chan, an professor of engineering at the University of Hong Kong . Xudong Ji, a postdoctoral researcher in Rivnay’s group, is that 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.

“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 whenever you would like to retrieve that information, you’ve got to remember it . If we will bring those two separate functions together, we can save space & save on energy costs.”

Currently, the memory resistor, or memristor,” is that the most well-developed technology which will perform combined processing and memory function, but memristors suffer from energy-costly switching and fewer 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.

To overcome these challenges, the Northwestern and University of Hong Kong team optimized a conductive, plastic material within the organic, electrochemical transistor which will trap ions. within the brain, a synapse may be a structure through which a neuron can transmit signals to a different neuron, using small molecules called neurotransmitters. within the synaptic transistor, ions behave similarly to neurotransmitters, sending signals between terminals to make a man-made 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 2 unrelated physical inputs (pressure and light) with each other .

Perhaps the foremost famous example of associative learning is Pavlov’s dog, which naturally drooled when it encountered food. For the neuromorphic circuit, the researchers activated a voltage by applying pressure with a finger press.

Future applications

Because the synaptic circuit is formed of soft polymers, like plastic, it are often readily fabricated on flexible sheets and simply integrated into soft, wearable electronics, smart robotics and implantable devices that directly interface with living tissue and even the brain.

“While our application may be a proof of concept, our proposed circuit are often further extended to incorporate more sensory inputs and integrated with other electronics to enable on-site, low-power computation,” Rivnay said. “Because it’s compatible with biological environments, the device can directly interface with living tissue, which is critical for next-generation bioelectronics.” (ANI)

The findings are reported in ANI