I mean, it is thought to be stored in the form of persistent patterns in neurons and we know certain areas of the brain that are vital for the formation of memories. But there is no clear model of how memories are formed.
There has been rapid increase in understanding of such things lately and I would say neural networks give some insight and ways to explore and experiment with different configurations further.
A physical model like this could probably serve as a proof of concept and maybe help further knowledge in the are, but I suspect computerized simulations will serve this purpose better.
tldr; as much of a layman I believe we know too little about the brain to make a proper physical brain.
Right now there are broadly speaking two camps in neuroscience, those who are connectionists and believe that memories are overall rather non-volatile/physically localized, with an MIT group that showed particular (individual ) neurons linked to particular memories, at the extreme of that school. On the other hand, you have neuroscientists who subscribe to a more plastic conception- in which mass synchronizations and redundant information is consistently combined and recombined.
My take is that it is no contradiction to admit the our brain is clearly volatile, and non-volatile in different modes. The transition from STP (short term memory) to LTP (long term memory) probably involves different conformational changes in neurons, dendrites, who knows even on the epigentic scale. But we do know the volatility is important and interesting-- which is precisely what makes this diffusive memristive study so important!
I thought maybe research was farther along than that.
To give some background, Leon Chua made certain claims about a hypothetical fourth circuit element and these debates largely stem back to claims about circuit analysis and mathematics. Basically his models predict a perfect device which, to my knowledge , has not been experimentally realized (to the contrary of HP's claims).
However , the funny thing is it doesn't really matter. We don't need a perfect memristor to build interesting and useful nanoionic and nano-redox circuits performing non-linear computational tasks. As modelers though, we do need to be careful making ideal claims about eternal non-volatility and device life (of course). Many point out that to the contrary of being ideal, these devices are extremely variable and imperfect- which is true. Anything built using nanofab techniques at the academic level (excluding semi-con industrial processes) will be..
Btw, If you want more physics depth on this , I can recommend any paper or book by Waser. They are all good. http://eu.wiley.com/WileyCDA/WileyTitle/productCd-3527334173...
Edit: adding a link to the first book chapter of aforementioned book I found which is already rather good. https://application.wiley-vch.de/books/sample/3527334173_c01...
While this gives you a way to do that in hardware, and so potentially much faster and denser than the software systems, the missing bit is the system when connects these things together and feeds them inputs and pulls off outputs such that the system can be trained. Still looking for that paper.
