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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.
"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.
* Human neuron: an actual neuron
* Chip: nothing like a neuron. Doesn't even act like a neuron.
Just because someone calls something a "neuron" or "neural network" doesn't make it anything like a brain or even an approximation of how the brain works.
Chip: nothing like a neuron. Doesn't even act like a neuron.
No one wants airplanes that grow feathers, poop, flap, or tweet. We just want ones that fly faster, higher, and carry larger payloads than birds. Likewise, we don't want artificial neurons that age, require oxygen, or contract Alzheimer's disease. We just want ones that process information faster and more precisely. So, could you please identify something related to information processing that "real" neurons do better? It's easy to be skeptical. What's hard is to put your ideas on the line and see what you learn. That's precisely what these guys are doing. If neurons do something else, they're about to find out what it is. You'll probably get to do the I-told-you-so dance, but they will have done something to advance science while you just groused about how slow real progress is.