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Comment by yontherubicon | Hacker News Reader

Part of the trouble is that the processor, main memory and such are not really discrete. The brain and nervous system is far more interconnected than that. So what's happening isn't that the something in a discrete memory changes, but the circuits themselves change.

What you are suggesting is, IIRC, far more high level than where the field has been. What the author is suggesting is that they now have the ability to gain larger samples than from a single neuron, so they're suggesting use cases for the new data. This is somewhat akin to examining what is happening at several logic gates within a microprocessor, probably quite a bit lower level than what you're suggesting.

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Have to take analogies with current computers with a large shovel full of salt.

The OP says more than once that they are trying to understand "behavior". To me that is like the kitty cat chasing a mouse, and in that case I suggested starting at a level lower than that. Sure, maybe as you suggest that level is higher than neuro-science has achieved so far, but I was responding to the OP.

A few days ago I did conjecture that building models of brain activity might give some clues to how the brain actually works, maybe.

If memory in brains involved rewiring, than maybe could treat that memory just as memory, although based on rewiring. In that case, maybe would want to study the wiring and, if some memory changed, the new wiring and, thus, see how memory writing is implemented by neuron rewiring. Then, maybe, the more times that memory is read and the length of time the reading is active determines how strong the connections are in the rewiring and, thus, how persistent the memory is. Maybe.

You may be interested in this paper: https://www.ncbi.nlm.nih.gov/pubmed/20700495

They take basic neuronal growth "laws" inferred by Ramon y Cajal just by looking at a crapload of stained neurons in the late 19th to early 20th century and apply modern computational techniques to grow realistic neurons based on randomly placed "growth signals" and an extremely simple rule.

Of course the next step is to grow groups of these "neurons" together with non-randomly placed signals based on some sort of input and allowing one to input on the other. I haven't followed up on it for a few years so maybe they've gotten into that.

To me this looks like successful science in action. Something that looks very complex (dendritic arbors) turns out to be explainable by a very simple to understand process/rule/principle.

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