Capacity Test Results
January 5, 2015
This is an experiment that tests the capacity of the sensorimotor inference and temporal pooling algorithms.
The setup for the experiment is as follows:
- Create N worlds, each made up of M elements. Each element is a random (and unique) SDR. For example, if N=3 and M=4, the worlds would look like: ABCD, EFGH, IJKL. This is a generalized sensorimotor learning problem.
- An agent can move around a world and perceive elements.
- Set up two HTM layers, one sensorimotor layer 4, and one temporal pooling layer 3 (which pools over layer 4). When the agent perceives an element, its SDR activates columns in layer 4.
- As the agent moves around the different worlds, record the activations of layer 4 and layer 3.
- Train the layers by having the agent do an exhaustive sweep for each world, moving from every element to every other element in the world, with resets in between worlds.
- Test by having the agent move for some number of iterations randomly within each world, with resets in between worlds.
- Characterize the representations formed in the temporal pooling layer of each world as the agent moves around. We are interested in two metrics: stability and distinctness (see section "Metrics" below).
- Fix M, and increase N. Record the effect on stability and distinctness.
- Fix N, and increase M. Record the effect on stability and distinctness.
Stability is measured by looking at the temporal pooler representation at each iteration as the agent moves around a particular world, and comparing against every other iteration in that same world. It is represented as the number of cells active minus the number of cells overlapping between the representations. Perfect stability is a value of 0.
Distinctness is measured by looking at the temporal pooler representation at each iteration as the agent moves around a particular world, and comparing against every other iteration in every other world. It is represented as the number of cells overlapping between the representations. Perfect distinctness is a value of 0.
tmParams:
cellsPerColumn: 8
initialPermanence: 0.5
connectedPermanence: 0.6
permanenceIncrement: 0.1
permanenceDecrement: 0.02
maxSegmentsPerCell: 255
maxSynapsesPerSegment: 255
tpParams:
synPermInactiveDec: 0
synPermActiveInc: 0.001
synPredictedInc: 0.5
n: 1024
w: 20
tmParams:
columnDimensions: [1024]
minThreshold: 40
activationThreshold: 40
maxNewSynapseCount: 40
tpParams:
columnDimensions: [1024]
numActiveColumnsPerInhArea: 20
potentialPct: 0.9
initConnectedPct: 0.5
n: 1024
w: 20
tmParams:
columnDimensions: [1024]
minThreshold: 9
activationThreshold: 12
maxNewSynapseCount: 20
tpParams:
columnDimensions: [1024]
numActiveColumnsPerInhArea: 20
potentialPct: 0.9
initConnectedPct: 0.5
poolingThreshUnpredicted: 0.4
n: 1024
w: 20
tmParams:
columnDimensions: [1024]
minThreshold: 15
activationThreshold: 20
maxNewSynapseCount: 30
tpParams:
columnDimensions: [1024]
numActiveColumnsPerInhArea: 20
potentialPct: 0.9
initConnectedPct: 0.5
poolingThreshUnpredicted: 0.4
Note: Only ran up to 80 elements, because any higher than that hit the synapse limit. Just be aware that the scale for this chart is different from the others.
n: 1024
w: 20
tmParams:
columnDimensions: [1024]
minThreshold: 20
activationThreshold: 20
maxNewSynapseCount: 30
tpParams:
columnDimensions: [1024]
numActiveColumnsPerInhArea: 20
potentialPct: 0.9
initConnectedPct: 0.5
poolingThreshUnpredicted: 0.4
n: 2048
w: 40
tmParams:
columnDimensions: [2048]
minThreshold: 40
activationThreshold: 40
maxNewSynapseCount: 60
tpParams:
columnDimensions: [2048]
numActiveColumnsPerInhArea: 40
potentialPct: 0.9
initConnectedPct: 0.5
poolingThreshUnpredicted: 0.4
Note: Notice that the scale for these charts is different from the others, since it skips by 20 instead of by 10. This was to speed up the tests.
n: 2048
w: 40
tmParams:
columnDimensions: [2048]
minThreshold: 20
activationThreshold: 20
maxNewSynapseCount: 30
tpParams:
columnDimensions: [2048]
numActiveColumnsPerInhArea: 40
potentialPct: 0.9
initConnectedPct: 0.5
poolingThreshUnpredicted: 0.4
Note: Notice that the scale for these charts is different from the others, since it skips by 20 instead of by 10. This was to speed up the tests.
