A new organic artificial synapse devised at Stanford University could support computers that better recreate the processing that occurs in the human brain. It could also lead to improvements in brain-machine technologies. These results have been published in Nature Materials.
First authors are Yoeri van de Burgt and
UG Master student Ewout Lubberman, who have carried out their research at Stanford University.
Scientist aspire to create devices that match the human brain’s ability to learn and remember information while using little energy. Brainier computers could boost the capabilities of many up and coming technologies, including visual-based search, voice-controlled interfaces and driverless cars. Researchers have already built high-performance neural networks supported by artificially intelligent algorithms but these are still distant imitators of the brain. They are especially hindered by their dependence on the traditional computer hardware, which requires large amounts of power in order to store information.
When we learn, electrical signals are sent between neurons in our brain. The most energy is needed the first time a synapse is traversed. Every time afterward, the connection requires less energy. This is how synapses efficiently facilitate both learning something new and remembering what we’ve learned. The artificial synapse, unlike most other versions of brain-like computing, also fulfills these two tasks simultaneously, and does so with substantial energy savings.
The artificial synapse is based off a battery design. It consists of two thin, flexible films with three terminals, connected by an electrolyte of salty water. The device works as a transistor, with one of the terminals controlling the flow of electricity between the other two.
Like a neural path in a brain being reinforced through learning, the researchers program the artificial synapse by discharging and recharging it repeatedly. Through this training, they have been able to predict within 1 percent of uncertainly what voltage will be required to get the synapse to a specific electrical state and, once there, it remains at that state. In other words, unlike a common computer, where you save your work to the hard drive before you turn it off, the artificial synapse can recall its programming without any additional actions or parts.
Only one artificial synapse has been produced but researchers at Sandia National Laboratory used 15,000 measurements from experiments on that synapse to simulate how an array of them would work in a neural network. They tested the simulated network’s ability to recognize handwriting of digits 0 through 9. Tested on three datasets, the simulated array was able to identify the handwritten digits with an accuracy between 93 to 97 percent.
Although this task would be relatively simple for a person, traditional computers have a difficult time interpreting visual and auditory signals.
Whereas digital transistors can only be in two states, such a 0 and 1, the researchers showed they could successfully program 500 states in artificial synapse. In switching from one state to another they only used 10 picojoules of energy . They only tested the synapse in large devices and believe they could attain neuron-level energy efficiency once it’s tested in smaller devices. These two features make the device extremely well-suited for the kind of signal identification and classification that traditional computers struggle to perform.
Every part of the device is made of inexpensive, organic materials. These aren’t found in nature but they are largely comprised of hydrogen and carbon and are compatible with the brain’s chemistry. Cells have been grown on these materials and they have even been used to make artificial pumps for neural transmitters. The voltages applied to train the artificial synapse are also the same as those that move through human neurons.
All this means it’s possible that the artificial synapse could communicate with live neurons, leading to improved brain-machine interfaces. The softness and flexibility of the device also lends itself to being used in biological environments.
Yoeri van de Burgt now is a researcher at Eindhoven University of Technology. After finishing this research, Ewout Lubberman has been working on a solar system in Madagascar. He works (internship) at an investment company in Amsterdam.
‘A non-volatile organic electrochemical device as a low-voltage artificial synapse for neuromorphic computing’, Nature Materials, DOI 10.1038/nmat4856
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