Releasing Etaler - A very fast and flexable HTM framework with full GPU support

Engineering Implementations
5

Nice work, @marty1885 – thanks for sharing!

I thought I would mention a few observations on differences between Etaler and the official TM algorithm. I am not claiming any of this to be wrong – just some points worth noting. I am not very familiar with the specific .cl file syntax, so please correct me if I have misrepresented anything.

  1. The TM algorithm appears to only be growing one segment per cell. This is also implied by some of the variable names (such as MAX_SYNAPSE_PER_CELL). @scott commented on this optimization idea on another unrelated thread. He mentioned that this is a reasonable optimization and that the algorithm would still work. The cost would be increased likelihood of false positives once you get up to around 10-15 predicted patterns, and he mentioned that this could be mitigated with the right learning rates.

  2. The cells chosen for learning in the current timestep will grow synapses with any cells that were active in the previous timestep (see here for example). This differs slightly from the official algorithm, where they will grow synapses only with winner cells that were active in the previous timestep (see here for example).

  3. It appears that every learning cell will connect with every previous active cell, up to MAX_SYNAPSE_PER_CELL. This differs from the official algorithm, where the number of new synapses grown in a single timestep can be throttled with maxNewSynapseCount. While I haven’t tested this, I would expect that not having maxNewSynapseCount might lead to behavior similar to what is described in this thread. If so, this could probably be mitigated by keeping MAX_SYNAPSE_PER_CELL sufficiently low.

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