Agreed. I just pointed that out because it looked like that in some figures in the paper shown in the recent hackers’ hangout.
Most layers receive most of their input from themselves. There could be resonance in any layer. Hex grids are a replacement for spatial pooling, which determines minicolumn states. Minicolumns exist in most layers*, so why assume hex grids are only formed in one layer?
*There’s decent evidence for minicolumns in L4, L5 TT, L5 ST, and L6a CT.
I’m not sure L2/3 is suited for resonance since its firing is sparse compared to other layers, meaning a small fraction of cells have much higher firing rates than the other cells at a given point in time (during a whisker deflection). L5 TT cells might be more suited for resonance because they have generally high firing rates. I imagine that’s more suited for network activity settling from one state into a more hex grid -like state. I don’t know though.
I’m not saying that L2/3 can’t be where hex grids form, just that I don’t see why it has to be L2/3. For example, L5 slender tufted cells are similar to L2/3 in several ways*, and they are more directly related to the thalamus** and so are more suited for utilizing cortex-thalamus resonance. They also have higher firing rates which might make them more suited for resonance. They don’t have a place in HTM theory unlike L2/3, so assigning the role of hex grids to L5 ST cells is more compatible with HTM theory.
*They are suited for voting, e.g. long lateral connections and projections to other regions.
They receive input from higher order matrix thalamus, and they receive input from one group of L6 corticothalamic cells just like L4 receives input from another group of L6 CT cells. Each of those groups of L6 CT cells forms their apical tufts mainly in the corresponding layer, either L4 or L5st (L5a in this region). L2 and L3 project to L5a (slender tufted cells in this case) way more than to L5b, and L5a projects to L2 a lot.
I hadn’t thought about learning. That seems like it could clean up the messiness caused by messy axon arbors and whatnot into something more neatly gridy.
I’m not sure maintaining topology is enough to convey a hex grid pattern. For example, if the topological axons spread out too much but still maintain a blurred topology, that might not be spatially precise enough for hex grids. It seems like it would be really hard to find strong evidence for this.
I don’t understand what you think L5 does in hex grids. Are you talking about L5 slender tufted cells or thick tufted cells?