Artificial synapses 10k times faster than real thing
spectrum.ieee.org
spectrum.ieee.org
These 'synapses' aren't actually synapses and they don't emulate even a fraction of the processes you find in their biological namesakes.
Comparing them with human synapses is like comparing cellphones to servers. Not exactly apples to apples.
Or I could do the same thing but name it an “artificial heart”, noting that it’s 10,000 times faster than a human heart.
That makes sense because artificial just means made by humans?
I agree that the speed comparison seems like a very misguided metric though.
You wouldn't call them artificial dogs nor artificial arms (unless they were being used as a prosthetic I guess, but then it's just a robotic prostethic arm)
Not necessarily, though primarily. The core point is in "crafting with an aim".
It is interesting that the first recognized use of the term is for "artificial day", meaning "dawn to dusk", which is the period in the "natural day" (here the term already starts to suggest an opposition - "artificial vs natural") in which you can "purposefully craft" (light allows).
neuron1 -> neuron2
Neuron1 receives an input signal across a synapse, “processes” that signal, and then either does or does not pass along an output signal to neuron2. I’m sure this is an incredibly deep field of research with a lot of nuance, but I think it remains a reasonable approximation to say that neurons “fire” or not in a binary manner. A lot of the magic takes place within the neuron itself, where unimaginably complex biochemistry dictates how likely a neuron is to fire in response to an input signal. As far as I understand it, this is analogous to the application of a weight to an input in a neural network.
A decent example along these lines is how opiates influence breathing. Neurons exist at a resting negative electrical state, which can be shifted to a sufficiently positive state in response to an input that the signal propagates down the neuron resulting in the passing on of that signal to the next neuron. Opiates drive that resting negative electrical state to be even more negative, and so in response to a normal “we’re running low on oxygen here!” input, a neuron will fail to become sufficiently positive to pass that signal along the chain. In NN parlance, it’s weight has been changed.
This piece describes a memristor that replicates this weighting of inputs to produce outputs through a material that stores these weights in a material that can be adjusted electrically rather than through biochemistry. There was actually a paper[0](released just two days ago!) that uses memristors to meaningfully create an artificial neuron with biochemical synapses. Of course, there’s a lot of extra machinery involved to actually be biologically useful, but nonetheless this tech can be used as a very simplified drop-in. Of course, as you say it’s like step one in a 10 billion step process, but I don’t think it’s totally dishonest to call it an “artificial” neuron, or at least a component of one.
Of course, bragging about how fast and small it is compared to a neuron and synapse is a bit like an elementary school teacher setting up a cool grow-lamp garden to teach kids about sunlight and photosynthesis and then bragging about how they produced an ultra-minutare sun that’s so efficient it runs off an outlet :)
For one, some neurons, when activated, don't just send a signal to specific other neurons, but instead release a chemical in an area, that affects the activation chances of other nearby neurons. I believe there are also other modes of activation, and other consequences of neuron activation, that make the brain far more complex. It should be remembered that the brain can also activate other glands in the body, which in turn change how the brain works - e.g. when releasing adrenaline, testosterone, oxytocin etc.
For another, as far as we know right now, each neuron itself is deciding whether to fire or not based on much more sophisticated logic than "sum(input*weight) > threshold". In fact, it seems that computation happens quite a bit in individual neurons, not only at the NN level. At the very least, the neuron activation function is not fixed, like in an ANN after training, it changes constantly for various reasons.
I will say that my mental model does hinge on the idea that the action of a single neuron at a single point in time in a single context can actually be equated to "sum(input*weight) > threshold". Doing the actual computation to figure out a principled measure of weight (and input, context, and maybe even time for that matter) is way outside our ability, but it seems like something that could be approximated in a simple experimental model!
https://www.ikea.com/gb/en/cat/artificial-plants-flowers-204...
Artificial just means made by humans and at some part are a copy of the original, not a 100% replacement
It isn't expected that e.g. an artificial heart would emulate all processes of the heart. It just needs to cover the function of the heart to some degree, sometimes (usually?) they don't even have a beat.
That is correct. But they are not supposed to: natural nervous systems are just an inspiration. That perspective is only valid in _nervous systems emulations_ - which is just a subset of what we do.
> using the term
'Artificial', in use, stands for "non natural, non spontaneous"; it means "done with an aim" (cpr. Sanskirt arth, artha, arthin, arya; Greek aretē, àristos). Those items are. They are called metaphorically - but evidently so - 'synapses', which means "joinings, interfastenings" (see in engineering 'haptic', "touching"), because they join the elements of a "neural network", where 'neuron' remains "a string" - a "connection significant of a relation".
So: at some point some thought: "What if we obtain something through joining similar elements in a network... Yes, similarly to the other one". It fits, because the model is this model, a model of the natural thing. You have natural neural networks, their model ("just a network"), and such model (of the natural thing) is used as a model (for designing further things).
> made by people, often as a copy of something natural
There is absolutely no requirement for the "copy" to be functional. See: artificial flowers. Things like "artificial hearts" are the exception and not the norm.
As long as they provide the same resulting behavior, it doesn't matter the processes are the same. If the processes were the same, it'd be an electrochemical process and would be 10K times slower.
Thing they're describing: a bullet fired from a gun
A horse is not a car, it does not even bear a passing resemblance, and yet both can be used as a mean of transportation.
If this is what we expect, the analogy holds and the implementation details don't matter.
That is, for the same input at different times, a real neuron will have different outputs; and its own ouptus may change its state. In contrast, the memristor, once programmed, always applies the same function to its inputs. Even if it can be reprogrammed with a different weighting, it can't do so based on its own output - at least not with any current neural network architechture.
This is it.
To begin approximating what a lone spherical synapse would actually do you'd need to solve 2^n coupled second order differential equations where n is the number of ions used.
That is before you throw in things like neuro transmitters and the physical volume of a cell. Simulating a single neuron accurately is beyond any super computer today. The question is how inaccurately can we simulate one and still get meaningful answers.
Then how we do it 100e9 more times.
I'm looking forwards to all the "easy" things it will figure out and stick us in a loop of "why didn't anyone think of that?" Something like the nth generation ML offspring solving the building of viable neurons at scale by breeding some single cell organism.
Kites do none of those things.
How?
> Colour me pineapples
That's a nice phrase, I like it.
If synapses worked like that we d all be epileptic, or dead