Project : Full-layer V1 using HTM insights

[gmirey]

@spin

it just looked like @gmirely didn’t want to just have an interesting challenge, but use his model to understand some important things about the cortex.

And you are right

@spin

That’s why it has to be the first research target

It is. The ‘target’.

@spin

I strongly believe we will find how does it work by modelling different ideas earlier […] There is enough collected data to find insights, or at least, to check the plausibility of your theoretical ideas. So, you have everything you need

‘Modelling’…‘Checking’. Yup, precisely. I don’t see a computer model as a despicable end-product. It is an integral part of our R&D (or even theorization) toolbelt now, for modelling and checking. As much as a pen and paper for drawing boxes, arrows, and/or equations.

@spin

You said you know what is the V1 output. Why don’t start from here and find a way to go higher in the hierarchy? We don’t really need another edge detector

You do realize, that what I find the most interesting known-output of all, as far as V1 goes, is the output of the ‘learning’ function itself, right ?
aka the end-state of those cells and dendrites and synapses after exposition.

Assuming that you do… I don’t quite understand over what we’re disagreeing here.

• If you think V1 formation is so complicated that it won’t work in isolation, then we’ll try to add parts of a hierarchy. I stated as much already. That endeavor could give us some evidence of this very requirement.
• If you think V1 formation is so simple that any model would do, and thus we won’t get any insight reaching it, then… well at that point I don’t think it will be that easy. But right; it is some possible concern. If that turns out to be the case we can always turn the ‘probing’ part on its head and look for models which fail. Or strip ingredients one by one to get a clue about which are the necessary ones…

We’ll learn ‘something’ either way.

I don’t know how my coding skills are relevant to the discussion, since you did understand that I don’t want to hardcode V1-like output (or didn’t you ? the purpose is not a clever edge detector for the sake of it), but let a model learn online and see if its cells tune themselves towards edge detection and stuff.

Now, if your concern is that I can’t model anything before having a well-defined model in the first place, I’ll restate that ‘let a model learn online’ in the sentence above will more likely turn out to be an iterative ‘let several, many, theorized models, in turn, learn online’. And see which of these succeeds. I may already have some insights for the first tries, granted… but I’m not putting too much confidence in them anyway, and all these models (against which to “check the plausibility of your theoretical ideas”) could very well be dug out/refined/invented as we go.

‘Invented’… Hacker-style since I’m no Einstein, sadly.

@gmirey

If it does not, adding fierce topological inhibition heatmaps into the mix. Or wave phenomenons. Or a deeper hierarchy. Whatever. Well, just throw at it all hacker-minded inspiration we may have, until it does start to organize like V1.

To conclude… I don’t know how V1 decision of forming ‘simple’ edge detection when exposed to visual stimulus is relevant to (A)GI. But I strongly bet that it is. Relevant. V1 is cortex. We both agreed on that, it seems. And I believe, that by witnessing concretely ‘how’ would V1 be driven to come to that particular choice, we’d gain insight into precisely that.
“What stands as relevant info and/or coincidences to wire to, from an (A)GI’s substrate point of view”.
Quite the nut to crack if you ask me.

@gmirey

I believe today that throwing all we have at V1 is one of our best bets.