I’m quite invested in the matter of conceiving diverse topological approaches and evaluating the computation requirements for those. As scott replied in that post you linked to, taking into account topology does not *have* to be very costly. I’d bet your “Another reason we don’t use topology much in our examples is because we focused on scalar anomaly detection applications for several years” is closer to the mark, than ‘performance’.

That and, until your simulations hit the scale of several dozens minicolumns on a side, there was no much point considering topology. Many biological axonal arbors would indeed potentially innervate all minicolumns in that kind of range (some may not, and we can also model topological effects at smaller scales, but that’s already wanting to depart from high, HTM-level abstractions).

My handful of different takes range from very complicated to quite straightforward, with different performance vs realism profiles. I’d try to make a post with these at some point, once I have something more concrete to show…

What I want to point out for now is that, most of the complexity of the very intricate schemes I came up with, stem from the additional self-imposed requirement of decreasing synaptic addresses weights. If you relax this requirement, an HTM implementation with 16b-worth of potential presynaptic cells is already ‘almost there’… and if considering only binary activation, not-so-costly after all.

First, ‘Euclidean distance’ computations are maybe overkill to start seeing interesting effects using topology. Second, those distance-anything need only be computed at the limited times where a synapse needs to get newly connected (mostly when growing a new distal segment, for the TM part). Third, the reason why I believe a 16b HTM is ‘almost there’ already is that 16b addressing on a 2D bitmap is homing on the required axonal ranges, given biologically realistic space between minicolumns, on a more or less thin subset of a layer:

Quite importantly, you (simply) need to divide the problem along the vertical axis (by vertical I mean, the one perpendicular to surface of cortical sheet). Some activated axons would thus have to be repeated to a handful of these thin layers, but once you’ve done that, you already have all the building blocks for a first, simple, straightforward, topological model.

After that, you may consider a position of distal segments relative to the soma position, but that’s adding bells and whistles, already. While in the bells and whistles realm, you can go quite nuts with it (as I like to do, almost as much as flooding @bitking with my crazy designs every other day) and consider more and more spatial divisions and stuff, but… it does not *have* to be very complex to start with.