Synaptic electronic circuits that learn and forget like neural processes
December 27, 2012
(a): Volatile (short-term) memory property of two terminal device before the forming process. Current change observed by applying sequence of positive voltage pulses at intervals of 40 s and widths of 0.5 s. (b): Nonvolatile (long-term) memory property in the device after the forming process following application of a sequence of positive and negative pulses with widths of 0.1 ms. (c): Schematic illustration of the device structures before and after forming process. (Credit: MANA, NIMS)
Rui Yang, Kazuya Terabe and colleagues at the National Institute for Materials Science (NIMS), and the International Center for Materials Nanoarchitectonics (MANA) in Japan and at the California NanoSystems Institute/UCLA have developed “nanoionic” (processes connected with fast ion transport in all-solid-state nanoscale systems) devices capable of a broad range of neuromorphic and electrical functions.
Background
Such a device would allow for fabrication of on-demand configurable circuits, analog memories, and digital-neural fused networks in a single device architecture.
Synaptic devices that mimic the learning and memory processes in living organisms are attracting interest as an alternative to standard computing elements to help extend performance beyond current physical limits. However, artificial synaptic systems have been hampered by complex fabrication requirements and limitations in the learning and memory functions they mimic.
How it works
The device is based on a platinum-tungsten trioxide (WO3–x) device using oxygen ions migrating in response to voltage sweeps. Accumulation of the oxygen ions at the electrode leads to Schottky diode-like potential barriers and resulting changes in resistance and rectifying characteristics. The stable bipolar switching behavior at the platinum-tungsten trioxide-based device is attributed to the formation of a conductive filament and oxygen absorbability of the platinum electrode.
The researchers noted that the device properties* — volatile and non-volatile states and current fading following positive voltage pulses — are similar to neural behavior — that is, short- and long-term memory and forgetting processes.
The device was found to possess a wide range of time scales of memorization, resistance switching, and rectification varying from volatile to permanent in a single device.
“These capabilities open a new avenue for circuits, analog memories, and artificially fused digital neural networks using on-demand programming by input pulse polarity, magnitude, and repetition history,” the researchers conclude.
* In its initial pristine condition the system has very high resistance values. Sweeping both negative and positive voltages across the system decreases this resistance nonlinearly, but it soon returns to its original state, indicating a volatile state. Applying either positive or negative pulses at the top electrode introduces a soft breakdown, after which sweeping both negative and positive voltages leads to non-volatile states that exhibit bipolar resistance and rectification for longer periods of time.
Comments (9)
by uday
i would like to give a seminar on this topic in my college can any one send me the report on this topic
by asiwel
Hi, @uday. Good idea you have. I just went through my college library on-line journal access system and downloaded this article and it reports on a really interesting piece of research. But I can’t email that to you, unfortunately. ACS publications – like ACS Nano – are widely available and it seems very likely that your college library would subscribe to an on-line service that provides access to this publication.
by eldras
you can engineer two ways:
1) get a similar output to the real thing.
2) reverse engineer the real thing.
We cant reverse engineer the synapse yet and dont understand it. We have a superficial knowledge of what/how it’s working. When we crack it that is most of the brain cracked IMO.
It;s devilishly complicated.
by John
Seems like trying to simulate feathers structure in order to build flying machines.
by asiwel
We have two similar articles today, one on memristors and this one. Simulation is one of the best routes to achieve and demonstrate understanding. However, engineering is often much better for designing useful devices and systems. For example, an unanswered question in this article has to do with the operating speed of these nanoionic devices. If they only work as fast as the objects (neurons, etc.) they simulate … well, that would not be much of an engineering breakthrough.
by Editor
Operating speed: I will be getting the original papers and will pass that spec on.
by Bri
Speed is superfluous, though I’ll bet it’s faster than our aqueous brains. The big news is thier nonlinearity, and configurability. It certainly would get rid of 1′s and 0′s. Much more like a dynamic, organic brain. This really is phenomenal and could become quite useful. It’s going to beinteresting to see what type of programming language they would have to come up with. I wonder if it might be holographic or interference based.
by melajara
@Bri” It’s going to be interesting to see what type of programming language they would have to come up with”.
IMHO, It will be more and more programming by (showing) examples, the basic tenet of human teaching.
Why? Because the sub-systems will be too complex to be explicitly taught what to do. As for us, in some way they’ll have to auto-configure.
The progressive transition from explicit to implicit programming in the coming decades will be fascinating to watch, stay tuned ;-)
by melajara
Excellent, a very promising news indeed, welcome to the positronic brain, LOL
Maybe some day ca++ ions will be used in such circuits like in biological synapses.