int getNumberOfConnectedSynapses(Neuron neuron) {
int numberOfConnectedSynapses = 0;
for (DistalSegment distalSegment : neuron.getDistalSegments()) {
numberOfConnectedSynapses += distalSegment.getConnectedSynapses().size();
}
return numberOfConnectedSynapses;
}
int getNumberOfConnectedSynapsesToCurrentActiveNeuron(
Neuron possiblyActiveInNextTimeStep, Neuron activeNeuron) {
// NOTE: This is incredibly inefficient. Fix this by making activeNeuron
// know which synapses from which neurons are connected to it.
int numberOfConnectedSynapsesToCurrentActiveNeuron = 0;
for (DistalSegment distalSegment : possiblyActiveInNextTimeStep.getDistalSegments()) {
for (Synapse synapse : distalSegment.getConnectedSynapses()) {
if (synapse.getCell().equals(activeNeuron)) {
numberOfConnectedSynapsesToCurrentActiveNeuron++;
}
}
}
return numberOfConnectedSynapsesToCurrentActiveNeuron;
}
/**
* Call this method to run PredictionAlgorithm_1 once on a Region.
*
* <p>MAIN LOGIC: For each learning neuron in an active column, connect to all previously active
* neurons.
*/
public void run() {
// Step 1) Which neurons to apply logic to?
// POSSIBLE ANSWER: Iterate through all neurons in active columns in region
Set<ColumnPosition> activeColumnPositions = this.spatialPooler.getActiveColumnPositions();
for (ColumnPosition ACP : activeColumnPositions) {
Column activeColumn = super.getRegion().getColumn(ACP.getRow(), ACP.getRow());
Neuron learningNeuron = this.getNeuronWithLeastNumberOfConnectedSynapses(activeColumn);
// Step 2) How do you allow neuronA to predict neuronB will become
// active in the next time step?
// POSSIBLE ANSWER: For each learning neuron connect to all
// previously active neurons. 1 new distal segment per learning neuron.
DistalSegment distalSegment = new DistalSegment();
for (Neuron previouslyActiveNeuron : this.wasActiveNeurons) {
distalSegment.addSynapse(
new Synapse<>(previouslyActiveNeuron, Synapse.MINIMAL_CONNECTED_PERMANENCE, -1, -1));
}
learningNeuron.addDistalSegment(distalSegment);
// Step 3) Which neurons should be active for the current time step?
// POSSIBLE ANSWER: The active neurons that best represent the
// current sensory input.
// 2
// EXAMPLE: Imagine you saw "2 - 1" and - . Although the minus
// 1
// symbol can also represent division you are not confused
// because the "2" and "1" are in different locations.
// Your brain saw the "2" and "1" SDRs as well as the SDR
// for how your eye moved while looking at "2", "1", and
// "-" in sequence so when you saw "-" you knew that it
// meant minus or division.
//
// CONCLUSION: We want the current SDR to be the active neurons that
// are most connected to all previous active SDRs. In
// this case it includes vision and eye muscle SDRs.
Neuron activeNeuron = this.computeActiveNeuron(activeColumn);
activeNeuron.setActiveState(true);
this.isActiveNeurons.add(activeNeuron);
}
// Step 4) What neurons can be used for prediction?
// POSSIBLE ANSWER: which neurons currently have the most # of connected
// (NOT active Cells)
// synapses across all distal dendrites connected to the current set of
// active neurons. This is where we reward all the competition between
// all synapses to represent an connection to a past time step.
// NOTE: connectionScores = sorted # of connected synapses for each neuron in Region
Set<Integer> connectionScores = this.getConnectionScores();
int index =
Math.max(0, connectionScores.size() - this.spatialPooler.getActiveColumnPositions().size());
int minimumConnectionScore = (Integer) connectionScores.toArray()[index];
// Step 5) How many number of predicting neurons?
// POSSIBLE ANSWER: same number of currently active neurons.
this.updateIsPredictingNeurons(minimumConnectionScore);
// Step 6) Which synapse connections should be strengthened to model
// long term potentiation?
// POSSIBLE ANSWER: Current time-step is @t=4. Strengthen the
// connection between neuronBs that isActive @t=4 and isPredicting
// @t=3 and neuronA that isActive @t=3.
for (Neuron activeNeuronBatTequals4 : this.isActiveNeurons) {
if (activeNeuronBatTequals4.getPreviousPredictingState()) {
for (DistalSegment distalSegment : activeNeuronBatTequals4.getDistalSegments()) {
for (Synapse synapse : distalSegment.getSynapses()) {
// increase permanence of connection with
// neuronAs' active @t=3.
if (synapse.getCell().getPreviousActiveState()) {
synapse.increasePermanence();
}
}
}
}
}
// Step 7) Which synapse connections should be weakened to model
// long term depression?
// POSSIBLE ANSWER: Current time-step is @t=4. Weaken the connection
// between neuronBs that isActive=False @t=4 and isPredicting @t=3
// and neuronA that isActive @t=3.
Column[][] columns = super.region.getColumns();
for (int ri = 0; ri < columns.length; ri++) {
for (int ci = 0; ci < columns[0].length; ci++) {
for (Neuron inActiveNeuronBatTequals4 : columns[ri][ci].getNeurons()) {
if (!inActiveNeuronBatTequals4.getActiveState()
&& inActiveNeuronBatTequals4.getPreviousPredictingState()) {
for (DistalSegment distalSegment : inActiveNeuronBatTequals4.getDistalSegments()) {
for (Synapse synapse : distalSegment.getSynapses()) {
// decrease permanence of connection with
// neuronA' active @t=3.
if (synapse.getCell().getPreviousActiveState()) {
synapse.decreasePermanence();
}
}
}
}
}
}
}
this.nextTimeStep();
}