I have a question about a bullet point in the WhitePaper (p. 27):
- Avoid extra connections
If we aren’t careful, a column could form a large number of valid synapses. It would then respond strongly to many different unrelated input patterns. Different subsets of the synapses would respond to different patterns. To avoid this problem, we decrement the permanence value of any synapse that isn’t currently contributing to a winning column. By making sure non-contributing synapses are sufficiently penalized, we guarantee a column represents a limited number input patterns, sometimes only one.
What does this exaxtly mean?
before reading this, I thought, that only the permanence values of synapses on the dendrite segments of ACTIVE mini-columns get changed.
A cite from
The HTM Spatial Pooler—A Neocortical Algorithm for Online Sparse Distributed Coding
summs this well:
For each active SP mini-column, we reinforce active input connections by increasing the synaptic permanence by p + , and punish inactive connections by decreasing the synaptic
permanence by p − .
Sooo, now…when I read the 4th bullet point (shown above) I am not sure about this anymore.
I understand this bullet point like this:
besides updating synaptic permanence for ACTIVE mini-columns, we do another thing:
In every time step ALL the synaptic permanence of INACTIVE mini-columns will be decreased.
Do I understand this right??
I would really appreciate some clarification.
Thanks a lot in advance.