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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.
with millions of neurons doing a way better job.
But IBM's attempt can probably be patented.
It's coming. Before the end of 2017.
Best get ready. Whatever that means.
Curious as to why they started with 500 as opposed to say a billion when working at a 90nm fab technology.
IBM made a video a while ago which was a pretty interesting watch if you're interested in this stuff:
"From BrainScales to Human Brain Project: Neuromorphic Computing Coming of Age"
https://www.youtube.com/watch?v=g-ybKtY1quU
IBM actually put out quite a lot of interesting tech related videos :)
https://www.youtube.com/user/IBMLabs
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
Artificial solid state versions of neurons are soooooo last millenium!
http://link.springer.com/chapter/10.1007%2F11427391_76
80s-90s was the time of the last artificial neurons on semiconductors, IBM has dusted off that research now that AI is becoming relevent again (e.g. Google), for patent purposes it'll get a fresh lick of paint, but it isn't new and certainly IBM were never the first to do it.
Personally I feel that breakthrough in artificial brains may be doomed to be locked with advances in understanding of the medical field; as you've pointed out we've yet to understand an individual neuron's role in the grand scheme or what makes all this mush of neurons self-aware. So far we've only examined cause and effect by pumping chemicals and electrical jolts into a live brain which seems almost primordial compared to what we don't know.
"singnal+noise to figure out the 'language'"
Why would there be one language? We understand how the basic neuron works, the back propagation reinforcement, the weighting of inputs and so on, BUT how they configure themselves depends on the data they are fed.
That is what Google's deep dream is, it's a look into the layers in a neural network so you can see how its configured. But the dream is different depending on the training data set. Even the same training data set, fed in a different order, ends up with a different configuration.
So sure, stuff next to the eye neurons will tend to process visual data, and stuff next to the spinal cord will tend to handle sleep (since it needs to cut the signal to the muscles, it needs to be in that area). At a macro scale we can determine how the brain will be laid out, because that is fixed. But the microdetail of the brain organization will differ and vary dramatically.
i.e. there is no language, there is no perfect understanding of N brains, that lets you predict the N+1th brain's exact configuration, no Hablo Neuronish.
I've got a secret I've been hiding under my skin
My heart is human, my blood is boiling, my brain IBM.
Never answer an anonymous letter. - Yogi Berra
So, why can't we just query them for their contents?
(I'm an AI researcher by day.)
It's a very good idea, and something that many researchers have thought about. The problem is that it's very difficult to do, and so far no one's been able to figure out a good way to do it.
The cerebral cortex is composed of "columns", where each column is about the thickness of a human hair. If you could peel the cortex off and lay it flat, it would be about as thick as a business card. To all appearances, the cortex is composed of identical columns, with some slight variations for I/O columns and such.
Each column contains roughly 100 neurons in a handful of types. Any individual neuron makes between 2000 and 15000 connections with other neurons, and some neurons in a column make connections with neurons in other columns.
So you have 100 cells in a column the thickness of a human hair and length which is the thickness of a business card.
It's difficult to make a wire thin enough to contact one neuron, it's impossible to manoeuvre such a wire to get it in place to touch one neuron, it has to have insulation everywhere except the tip to avoid signals from other neurons, and the very faint signals have to be amplified close to the source to avoid noise.
It's just about impossible to map the connections between neurons because there are so many of them and the connections are much *much* smaller than the nerves themselves. Also, you have to do this without killing the nerve, and killing other nerves you have to go through to get the connections.
And this has to be done while the organism is living, and keeping it living while drilling into the head cavity is a trick in itself (and dealing with the resulting pain, blood loss, &c.). You can get some information from non-mammals (such as sea worms), but then none of those have a mammalian cortex to study.
Every once in awhile I read about new techniques using fiber optics and related technologies, but there's still the issue of routing the sensor (whatever it may be) to the neurons in a way that doesn't chop through other nerves.
One technology I read about has a pad with tiny needles laid down on the cortex. The needles can be made using chip fabrication technology, and you can have amplifiers on the chip at the base of the needles... but this still can only be applied to the *surface* of the cortex, and only connects to those nerves which are physically at the top of the column, and not the ones inside.
All in all, it's an extremely difficult problem that no one's figured out yet.
I dunno... we figured out how atoms worked by smashing them...
Never answer an anonymous letter. - Yogi Berra
With a tire iron just to see if it bleeds.
Things that we have yet to see actually used in something practical.
Slashdot, fix the reply notifications... You won't get away with it...
I get the feeling that you are attempting to understand these pseudo-neurons as if they are a special case of a logic gate. Based on what has been published they seem more like sensors tuned to react to a specific range of stimulus. Cascade enough of these and the resulting device has the required sensitivity to detect conditions which tell you things like what string of seemingly random entropy was used to encipher a communications channel, what sort of molecules will bind to a specific type of cancerous cell, how do I solve this bitcoin hash with sufficient precision to earn a payout, etc. Its not all fun and games though, you've still got keep track of the stimulus that was applied to find the solution, otherwise you could end up with the answer 42 without understanding what the question was.
My body is available as a host
Concentrating on the individual neurons might not be the most useful thing. The usefulness of neurons comes from the way that they are "wired" together - the network is key. It's analogous to "the network is the computer".
You're a temporary arrangement of matter sliding towards oblivion in a cold, uncaring universe
re your first paragraph
that's just speculation, right? not on your part, necessarily
i don't follow advances in neurobiology/neuroscience (i'm a physicist) but last time i checked we have pretty much now idea how these things work
Because the brain doesn't work that way. It isn't just a collection of queryable neurons in a known state. No one really knows how it works. This type of AI research is a joke. They just call what they built to be "neurons", but they aren't anything like neurons in the brain.
I think I've heard this before...
https://www.youtube.com/watch?v=eRepe3U3ePM
Bang, bang, Maxwell's silver hammer
Came down upon her head
believe me!
Orpheus: It's powered by a forsaken child?!?
Dr Venture: I mean, I didn't use the whole thing...
https://youtu.be/D8aBP-JOZsU
I agree that there is no language. The closest you could come to a language is an understanding of what makes neurons make the connections they do, and why they strengthen/weaken signals from other neurons. As the AI researcher below stated about finding these things out, " it's very difficult to do, and so far no one's been able to figure out a good way to do it.". It actually sounds damn near impossible to do, at least with out present level of technology. If we could make microscopic self assembling (in situ) insulated wires, then it might happen.
...artificial neurons will buy IBM stock.
With no doubts we are quite near to big big events..
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.
With an approach like this, it might be possible to read your very thoughts and test the truthfulness of your loyalty to the Party.
I was an undergrad at NMSU in 1992 working for a team developing artificial neural network processing elements in the 1990's. By 1992 a paper was published for using the PEs for pulse streamling filtering. The specific PE used for that example had electrically isolated dendritic inputs and axionic output. The team had preciously developed a PE that had dendritic input and axionic output on the same circuit. These behaved exactly the same as the ones from IBM, without the need for fancy phase-change materials, from what I can tell from the short article (grammar fail, I know.). The problem faced at the time was automated training of the PEs when connected. I'm glad to see someone has overcome that problem, but this sort of artificial neural network processing element has been around for over 20 years.
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As far as a 'brain' goes, though, it still doesn't account for consciousness (we are not our brain. We *use* our brains. Without 'us' even our brains are just dead cauliflower). It would still only have instructions it was given moving between said neurons. Nevertheless, could have interesting applications in some interesting ways.
https://youtu.be/hXeO8Kzz3bo In this work, we present an all-memristive neuromorphic architecture comprising neurons and synapses realized by using the physical properties and state dynamics of phase-change memristors. The architecture employs a novel concept of interconnecting the neurons in the same layer, resulting in level-tuned neuronal characteristics that preferentially process input information. We demonstrate the proposed architecture in the tasks of unsupervised learning and detection of multiple temporal correlations in parallel input streams. The efficiency of the neuromorphic architecture along with the homogenous neuro-synaptic dynamics implemented with nanoscale phase-change memristors represent a significant step towards the development of ultrahigh-density neuromorphic co-processors. http://dx.doi.org/10.1088/0957...
They always have such a colorful way of slashing their stock value (and "overhead").
I'd say for the large part, we know somewhere north of 95% of "how" it works. The problem is we will probably never have a good approximation of its FPGA layout.
The magic of the brain isn't that its core functionality is complicated or hard to grasp- it's in the sheer scale of the connectivity of the network. A single brain dwarfs all human made networks of any kind, combined.