IBM Creates World's First Artificial Phase-Change Neurons (arstechnica.com)

An anonymous reader writes from a report via Ars Technica: IBM has created the world's first artificial nanoscale stochastic phase-change neurons and has already created and used a population of 500 of them to process a signal in a similar manner as the brain. Ars Technica reports: "Like a biological neuron, IBM's artificial neuron has inputs (dendrites), a neuronal membrane (lipid bilayer) around the spike generator (soma, nucleus), and an output (axon). There's also a back-propagation link from the spike generator back to the inputs, to reinforce the strength of some input spikes. The key difference is in the neuronal membrane. In IBM's neuron, the membrane is replaced with a small square of germanium-antimony-tellurium (GeSbTe or GST). GST, which happens to be the main active ingredient in rewritable optical discs, is a phase-change material. This means it can happily exist in two different phases (in this case crystalline and amorphous), and easily switch between the two, usually by applying heat (by way of laser or electricity). A phase-change material has very different physical properties depending on which phase it's in: in the case of GST, its amorphous phase is an electrical insulator, while the crystalline phase conducts. With the artificial neurons, the square of GST begins life in its amorphous phase. Then, as spikes arrive from the inputs, the GST slowly begins to crystallize. Eventually, the GST crystallizes enough that it becomes conductive -- and voila, electricity flows across the membrane and creates a spike. After an arbitrary refractory period (a resting period where something isn't responsive to stimuli), the GST is reset back to its amorphous phase and the process begins again." The research has been published via the journal Nature.

Comments

Something that always bothers with these stories..

[RyanFenton]

Neurons work primarily in terms of communicating - I'd say they're basically communicating machines as much as muscles are movement machines. They store states, query other neurons, take external inputs, and work together to do virtually everything an animal can do, as a macroscopic being. As they grow, they have to figure out their particular role based on their inputs and outputs.

So, why can't we just query them for their contents? With stories like this, we're making artificial nerves - shouldn't there be some way we can signal the nerves, push some simple neurotransmitters, and experiment until we get enough singnal+noise to figure out the 'language'? Even in simple creatures, it seems like we should be able to do this enough to ask a neuron its contents, then query neighbors, until we at least get a loose map of queryable resources.

Every once in a while I search google scholar and the like to see what folks are doing along these lines, and I never seem to see anyone take this approach, or even attempt to reach for mechanisms of this form. But if we can see, learn, imagine in real-time, and so on, there has to at least some analogue of an informational query system we can use, static purpose neuron maps just wouldn't make sense even with the scale, even with specialization.

Ryan Fenton

Re:It's a bit difficult

[wierd_w]

I seem to remember some research that showed small spicule structures inside the axons leading to the terminating dendrites, which seemed to be the physical medium of data storage and decision making inside individul neurons.

If that is the case, then a combination of a novel signaling method (say, an artificially imposed communication protocol using an assortment of photon emission spectra, created using seveal biotag luminescence proteins attached to different parts of this spice assemblage) then having a small sensor array stuck on the top of the cortex is not such a liability. You can get deep signal data without having to jam a huge electrode in there and severing the structures you are trying to examine in operation, by observing the emitted energy at the surface. Rather than an electrical interface, it is a photo multiplier based amplifier, which filters noise with multiple sensor columns (needles).

Bonus if you can include a photomultiplier mechanism inside the axon itself to make it flash its activity states more brightly. It may be necessary to increase the metabolic activity of the animal neurons through further genetic manipulation in order to get enough optical signal without degrading the activity going on inside the axon to do that though.

Another radical idea may be to "stake" a single, custom engineered neuron onto such a phototamplifying sensor needle, by coating the needle in cellular membrane proteins, gaining direct structural connections to this spicule structure in the process, and letting this staked neuron migrate its own dendrites into the region of animal neural tissue being examined. that solves the wiring problem, and possibly some of the power generation problem for the photoamplification, and some others as well.

Re:Inferior compared to my brain ...

[Rei]

Firing rates:
  * Human neuron: a couple hundred milliseconds
  * Chip: A couple dozen nanoseconds. (note: not microseconds!)

Size:
  * Human neuron: 4-100um on each axis
  * Chip: Currently 100nm square on a thin wafer, with a 90nm process; scalable to 14nm process.

Now, let's not get ahead of ourselves: they are far from demonstrating the ability to emulate a human brain here. But if they do manage to implement a system that properly models human neural activity, the potential to vastly outperform the brain should be obvious. The number of neurons that make up the human brain could be packed into a single layer chip a third of a square centimeter (times some factor to account for the interconnects) operating at ten million times the speed. To say nothing of the ease of integrating it directly with storage, networking, and general purpose computing hardware.

And there is motive to advance this field, too. Neural nets are starting to have direct consumer applications (leaps and bounds improvements in image recognition, image enhancement, bandwidth reduction, etc). And we're talking about neural net chips that could readily be sized as a coprocessor in a phone. If there's a market, they'll make them. And advance them with time.

No, IBM is far from having a "brain on a chip". But it's very interesting research, to say the least.