Comments (3)
👍 for changing the name. I think sampleSize is better (we are sampling and it's a shorter name). I assume we are always sampling new synapses, even when learning on existing segments?
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When learning on an existing segment, we will only grow synapses if the segment has fewer than 'sampleSize' active synapses. Segments want to have 'sampleSize' active synapses, and they'll grow synapses to make up the difference. They'll sample the active bits that they haven't already synapsed.
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Finally fixed by #761
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Related Issues (20)
- SP adaptSynapses: Experiment with discouraging shared inputs
- Experiment with topology support in the TM
- Python ETM: When learning is disabled, a bunch of empty segments are grown
- Python ETM: Apical segments are grown in bursting columns even when there's no apical input
- Python ETM: Apical segments on predicted cells are reused even if they're not matching
- Update the Python ETM to use the new "walk the columns" strategy HOT 3
- Remove phases implementation of temporal memory HOT 2
- Convert sequence_learning to new TM HOT 4
- Remove _phases files
- Port bug fixes in temporal_memory_phases to new temporal_memory HOT 1
- Make sequence learning script easily replicate results
- ETM suggestion: Use apical segments when determining the learning cell HOT 1
- syntax error: missing comma in 'frameworks/sensorimotor/sensorimotor_experiment_runnner.py'
- ImportError: No module named research.connections with virtualenv HOT 9
- maybe a bug in ColumnPooler.py? HOT 1
- column_cooler.py HOT 2
- how can I test lstm model on data extracted by sensor kinect (3d skelton) HOT 1
- Unionpooler only for predictedActiveCells
- Python.exe crashes when using large UnionTemporalPooler HOT 6
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