Added back in a simple changing acceleration for the pendulum. This was done to test the new temporal pooling algorithm.
First commit of the new temporal pooling method.
Tried to implement temporal pooling by increasing overlap to previous columns that where active but not bursting. Needs more investigation.
Implemented temporal pooling using the spatial pooler overlap function. This temporal pooling relies on the potential radius of columns to be large enough to cover the next input.
A bug also needs fixing in the inhibition function, more then the desired local activity number of cells can become active in certain situations.
Fixed a bug where sometimes more then the desired number of columns could be activated by the spatial pooler. Deleted the columns.activeState variable as this has now been replaced by the columnActive array variable.
Added profiling
Began converting all bumpy array appends into python standard list appends which are quicker. This means bumpy arrays are being replaced by standard arrays.
Converted the numpy synapse lists used for the creation and update of synapses to normal lists.
Increased the speed of the neighbours function by changing to using standard arrays.
Increased the speed of the updateConeectedSynapses function by converting to an standard list to call append on. This has greatly increased the speed of execution.
Changed test parameters slightly. Updated the name of the sequence pooling methods to sequence pooling not temporal pooling.
Fixed the sequence pooler so cells aren't updated every timeStep. They are only updated when the column changes state from not active to active. The new temporal pooling algorithm works bester now. Much more testing is needed to prove this.
Disabled boost for testing purposes. Cleaned up some old commented out code.
Added spacebar to initiate next step.
Fixed displaying synapses active times bug #7
Fixed the columns activation for bursting cells. If the column was bursting and the next timeStep the column is still active then the columns cells will all still be bursting.
 Added first test and removed unused input variable to the constructor of the Column class
fixed problem with sequence pooling where the learning cells would not stay active if the column stayed active.
Added another level for testing.