Hello HTM theory -
I’ve been interested recently in exploring additional mechanisms that may enable sequence / transition learning, and in particular the possibility of learning transitions through the existing machinery of feed forward (proximal) spatial connectivity. There’s something tempting to me about leveraging a single mechanism for both spatial association and temporal prediction.
Based on a limited understanding of the cell level dynamics, mostly from UTHealth’s online neuroscience textbook (which seems to be a pretty incredible resource - http://neuroscience.uth.tmc.edu/ for those who haven’t seen it), it seems like fading activations could be biologically supported in certain contexts.
Without any further motivation than exploring what might be possible given a spatial pooler with continuous fading activations, I’ve been thinking along the lines below.
First add a few additional (not yet substantiated) assumptions:
- Fade rate can be variable per cell and influenced by connectivity, e.g. perhaps cells with substantial distal connectivity in the same region fade slower.
- An SDR X may be composed of a combination of core cells (X_c) which are highly distally connected and fade slower, and peripheral cells (X_p) which have less distal connectivity and whose activations fade faster. Some thoughts on intuition for this at end**.
Consider arbitrary SDRs A and B. A temporal transition from A -> B might look like the large grid in the lower right of the image below. Because the core cells (A_c and B_c) fade slower than the peripheral cells of A and B (A_p and B_p), A’s peripheral cells’ activations will have decreased more (or fully deactivated) by the time B is active. This leads to a resultant activation in the region with a potentially useful property: a cell in the region above could form inhibitory proximal connections with A_p and excitatory connections with B, and such a cell would detect only an explicit transition from A -> B.
To check this consider that (with the right thresholds) our cell can only fire when B is fully (currently) active, A_p has faded such that the inhibitory connections to A_p have disinhibited the cell, but A_c is still active. This can only occur in a transition from A->B.
At this point, it seems possible that with a fairly minimal hierarchy, complex and potentially high-order sequences (transitions of transitions) could be learned.
Questions on this:
- I believe I’ve seen mention of something like this on the forum at some point in the past, but nothing came up with my recent searches. Is there a standard name for this concept?
- Could spatial transition learning like this complement HTM sequence learning / temporal memory?
Would be curious to hear any thoughts on this, including “this isn’t worth pursuing or biologically feasible because of X, Y, Z”.
**Intuition on core vs. peripheral: One might think of core cells as composing the central, always present features of an SDR, possibly related to the concept of an invariant structure. Consider a tree. The verbal name ‘tree’ becomes highly associated with all features that are related to trees (leaves, wood, green), and so the SDR representing this word may contain many core / tightly connected cells. Whereas leaves and wood, independently, have a less direct association with each other (occur in close proximity to each other less frequently) and all other features, and so have less distal connections.