First, I wonder how you got access to the article? It is behind a
paywall and not yet uploaded to the sites I usually find paywalled
articles on.
Second, there is no need to compare brains to neural networks
because brains are neural networks. Neurons form vertices and axons
edges connecting the aforementioned. What you are perhaps thinking of
are artificial neural networks - most of which are very dissimilar
to brains. But even then you are wrong. Artificial Izhikevich and
Hodgkin-Huxley neural networks attempts to closely mimic the behavior
of real neurons.
While deep, hierarchical artificial neural networks have been more
successful than biologically plausible ones, that may be because the
technology isn't ready yet. After all, the perceptron was invented in
the 1950's but didn't become prominent until the 2010's (or
so). Perhaps we need new memories that better map to (real) neural
network topologies, or perhaps 3d chips that can pack transistors in
the same way brains pack neurons.
Changes in mechanical pressure, electric field, other molecules attachment, photon absorption, can control the conductivity.
Organic semiconductors designed to fit like lego bricks to naturally build the desired structure are IMHO the way to go to produce 3d circuits, rather than layered silicone litography.
I've seen this particular mistake a lot recently. New and exciting auto-corrupt from the latest version of iOS?
Given that our brains rewire themselves live, which ANNs can only do by being excessively connected and updating weights to/from zero, silicone (I'm thinking mainly the oil form) may be a better inspiration than lego.
Second, there is no need to compare brains to neural networks because brains are neural networks. Neurons form vertices and axons edges connecting the aforementioned. What you are perhaps thinking of are artificial neural networks - most of which are very dissimilar to brains. But even then you are wrong. Artificial Izhikevich and Hodgkin-Huxley neural networks attempts to closely mimic the behavior of real neurons.
While deep, hierarchical artificial neural networks have been more successful than biologically plausible ones, that may be because the technology isn't ready yet. After all, the perceptron was invented in the 1950's but didn't become prominent until the 2010's (or so). Perhaps we need new memories that better map to (real) neural network topologies, or perhaps 3d chips that can pack transistors in the same way brains pack neurons.