Hi I was studying the TP algorithm and didn't understand one point.
In the beginning, permanence values are assigned aleatory to distal dendrites segments of all cells (as in the initialization of the SP). In time t, the region predicts a bunch of cells based on the activation of their segments. A segment is active if it have a minimum number of active synapses connected with active cells in time t. In time t + 1, the cells that were predicted and are in active columns (and just that in active columns) will have their synapses permanence values updated according. But, what about the false positives, the cells that were predicted ocasionaly just because they had an active segment that fit well with input in time t, but they never occur. Don't they will be predicted again and again when the input in time t occur? I couldn't see where the algorithm treat this issue. In the last part, it does for c, i in cells 55. if learnState(c, i, t) == 1 then => update just the cells that are in an active column 56. adaptSegments (segmentUpdateList(c, i), true) 57. segmentUpdateList(c, i).delete() Doesn't necessary to decrement the permanence value of those connected synapses in the distal dentrite segments of cells that don't occur? Christian
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