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Local inhibition algorithm

13 November 2014

In which I propose and demonstrate a new local inhibition algorithm for HTM that seems more natural than existing methods. I attempt to model the waves of inhibition around firing neurons. A target level of sparsity is maintained by an adaptive control on the stimulus threshold parameter. One could imagine other formulations and I look forward to hearing other ideas.

A crucial step in HTM is the selection of neural columns to become active, given their feed-forward inputs. Essentially, only those with most excitation will be activated, forming a sparse representation. But the details could be important. Real neural columns, I am told, on firing trigger a wave of inhibition in the surrounding area.

Inhibition algorithms in NuPIC

In NuPIC the seemingly universal choice is global inhibition of columns. The top few columns with highest excitation are selected to become active, ignoring any spatial relationships between them. The justification for this is computational performance; a comment in the code warns of a 60x performance difference between the global vs local algorithms.

The local inhibition algorithm in NuPIC works as follows. Taking the target activation density—typically 2% (as with the global algorithm)—it keeps just that number of the most active columns in each sliding window over the column space. So inhibition is applied in local spatial areas, but within each area the spatial relationships between columns are still ignored.

The local window size is not a parameter but is calculated as the average receptive field size, mapped back from the input space to the column space.

Proposed algorithm

I have tried to model the inhibitory waves around active columns, and their relative timing to some extent. Keep in mind that my neuroscience knowledge is poor.

In a nutshell, starting with the most excited columns and working down, each becomes active and inhibits all neighbours within a base distance; it may also inhibit further neighbours within an outer radius if their excitation is sufficiently low. Specifically, if the neighbour’s excitation level is below a linear ramp from the original column’s level down to zero at the outer radius. That radius is defined by the average receptive field size. As soon as any column is inhibited in this process it is removed from consideration.

That is the inhibition algorithm itself, but notice that it does not enforce any particular activation density. Also it may be overly sensitive to noise in areas lacking any real signal (a criticism that probably applies to any local algorithm). So, in addition, I have made the stimulus threshold an adaptive parameter, changing slightly each time step according to the actual vs target activation density. Columns with excitation below the stimulus threshold can not become active. This is a global mechanism, but operates at a longer time scale; perhaps there is some neurochemical process like this?

UPDATE 2014-11-25: There is a problem with the adaptive stimulus threshold. After training on familiar input for a while, it becomes well recognised with many connected synapses so the stimulus threshold rises. When new input comes in, it has fewer connected synapses, i.e. a lower stimulus value. This gives rises to too few—even zero—active columns. Bad! Anyway, we can simply take the top N most excited columns after accounting for local inhibition. That is the same as having the threshold adapt immediately within one time step.

This algorithm is a “local algorithm” in the sense that it considers local spatial interactions in its processing. But it is not a “local algorithm” in the sense that its computation is distributed. Of course one could split up the column space and run inhibition on each chunk, but that is not my focus here. Rather I am interested in whether accounting for local interactions could have some effect on information processing.

Interactive demonstration

The plot below shows 200 columns along the x axis with some generated excitation levels on the y axis. Red columns are active. Hit the Step button a few times to see the actual activation level converge on the target activation level, and to contrast this kind of local inhibition with global inhibition (mirrored below the axis):

And a similar demonstration in 2D:

Finally here is a basic example of the algorithm running in HTM:

Isolated fixed sequences 2D.

Note: Google Chrome browser recommended.


Of course local inhibition is slower to compute than the simple global approach, which scales approximately linearly by the number of columns. But it is not as bad as (number of columns) X (inhibition radius), because as columns are inhibited they are removed and ignored. The performance depends on distributional properties of the input. In one (fairly arbitrary) test I ran on Comportex, the local algorithm was about 25X slower than a simple sort. However, usually the inhibition step is not the slowest part of a time step in Comportex; rather, learning on proximal synapses takes longer.

The code

The demonstrations here were compiled from ComportexViz 0.0.6 (local-inhibition-1d.cljs, local-inhibition-2d.cljs). with Comportex 0.0.6 (inhibition code is in inhibition.cljx and tuning the stimulus threshold is in cells.cljx).

UPDATE 2014-11-25: fixed version is Comportex 0.0.7 and ComportexViz 0.0.7. Inhibition code is in inhibition.cljx.

As always, I value your advice.