Materials scientists at the Harvard have created a new type of transistor that mimics synaptic behavior. Exploiting unusual properties in modern materials, the synaptic transistor could mark the beginning of a new kind of artificial intelligence.
"The transistor we've demonstrated is really an analog to the synapse in our brains," says co-lead author Jian Shi, a postdoctoral fellow at SEAS. "Each time a neuron initiates an action and another neuron reacts, the synapse between them increases the strength of its connection. And the faster the neurons spike each time, the stronger the synaptic connection. Essentially, it memorizes the action between the neurons."
In principle, a system integrating millions of tiny synaptic transistors and neuron terminals could take parallel computing into a new era of ultra-efficient high performance.
While calcium ions and receptors effect a change in a biological synapse, the artificial version achieves the same plasticity with oxygen ions. When a voltage is applied, these ions slip in and out of the crystal lattice of a very thin (80-nanometer) film of samarium nickelate, which acts as the synapse channel between two platinum "axon" and "dendrite" terminals. The varying concentration of ions in the nickelate raises or lowers its conductance -- that is, its ability to carry information on an electrical current -- and, just as in a natural synapse, the strength of the connection depends on the time delay in the electrical signal.
The synaptic transistor offers several immediate advantages over traditional silicon transistors. For a start, it is not restricted to the binary system of ones and zeros.
"This system changes its conductance in an analog way, continuously, as the composition of the material changes," explains Shi. "It would be rather challenging to use CMOS, the traditional circuit technology, to imitate a synapse, because real biological synapses have a practically unlimited number of possible states -- not just 'on' or 'off.'"
The transistor modulates the information flow and at the same time physically adapts to changing signals.
Compare to the human brain, with around 80 billion neurons, the world's best supercomputers are staggeringly inefficient and energy-intensive machines. The human mind, for all its phenomenal computing power, runs on roughly 20 Watts of energy (less than a household light bulb).
Unpublished:
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