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“Because we change the ‘bulk’ properties of the film, the conductance change is much more gradual and much more controllable,” Lu said.
In addition to making the devices behave better, the layered structure enabled Lu’s team to link multiple memristors together through shared lithium ions—creating a kind of connection that is also found in brains. A single neuron’s dendrite, or its signal-receiving end, may have several synapses connecting it to the signaling arms of other neurons. Lu compares the availability of lithium ions to that of a protein that enables synapses to grow.
If the growth of one synapse releases these proteins, called plasticity-related proteins, other synapses nearby can also grow—this is cooperation. Neuroscientists have argued that cooperation between synapses helps to rapidly form vivid memories that last for decades and create associative memories, like a scent that reminds you of your grandmother’s house, for example. If the protein is scarce, one synapse will grow at the expense of the other—and this competition pares down our brains’ connections and keeps them from exploding with signals.
Lu’s team was able to show these phenomena directly using their memristor devices. In the competition scenario, lithium ions were drained away from one side of the device. The side with the lithium ions increased its conductance, emulating the growth, and the conductance of the device with little lithium was stunted.
In a cooperation scenario, they made a memristor network with four devices that can exchange lithium ions, and then siphoned some lithium ions from one device out to the others. In this case, not only could the lithium donor increase its conductance—the other three devices could too, although their signals weren’t as strong.
Lu’s team is currently building networks of memristors like these to explore their potential for neuromorphic computing, which mimics the circuitry of the brain.
The research was supported in part by the National Science Foundation. It was done in collaboration with the group of Xiaogan Liang, U-M professor of mechanical engineering.
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