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I had the same initial reaction, but after digging in a little I don’t think “artificial” is the worst description in the world here. I do think the innovation here is much more like an artificial neuron than an artificial synapse though. Fair warning, I’m no neuroscientist and I basically don’t know what neural networks are, but here’s what I’ve gathered. 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 :) [0] https://www.nature.com/articles/s41928-022-00803-0 |
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.