Boosting in HTM and new ways


refractory period


If X and Y appears close in time…




If X appears far before, I don’t see how it can affect Y representation. I think that boost algorithm will be unaware of the similitud between X and Y (unless both appears within the window time used to compute the activation frequency of each minicolumn).


This is in response to “refractory period” and “If X and Y appears close in time.”
Once you bring in time you have to look at the coupling of HTM theory to time. (The T of HTM)
The entire thing about columns is change detection - x changes to y. Implicit in the HTM theory is that the column enters a predictive state and then fires triggering learning. In HTM cannon this period is set to one timestep. The refractory period is also within one timestep in HTM cannon.

Elsewhere in this forum, we discussed the decay of the predictive state over time. If there is partial depolarisation that does not result in an action potential there is a uniform decay that does not seem to be related to the refractory period; different mechanisms. In biology this was NOT restricted to a single timestep but to the best of my knowledge is not used in HTM theory in any way.

The spreading of activation (or overactivation) is a completely different thing - spatial and not temporal.


I think in SP there is no predictive state: the mini-column is active if wins the inhibition. I can’t see the relation with depolarization decay. In any case, hyper-polarization state seems to last a substantial amount of time. My understanding is that a neuron can’t fire again from such state, and only depends on their own membrane ion-pumps to recover from it.

In this particular case (intuitively) is that one cell can’t fire with a period bellow the refractory period (in either pyramidal cells or interneurons). In some way, the inhibition process is not “state-less” (like the plain SP assumes). There is a short term influence of the recent past, i.e. if you hammer the granular layer with similar “inputs” the firing pattern will spread out.

Perhaps I’m wrong…


As long as column A is over activated there will be another column which has increased chance of activation. As long as they try to compete with each other they will try to share activation among the input patterns. I am not sure about the necessity of X appearing close to Y in this case. Column A will be over activated even if only X occurs frequent enough.


Yes: i think that you are right.

Nevertheless, my point was that boost not necessarily will separate similar input values: it depends when the values are initially exposed to the system.

In any case, static tie breakers (or stable sort, like is used now) might artificially increase the activation frequency of some columns. I don’t know if the boost could be a “practical” solution to alleviate this issue…