def activatePredColCells(col,prevActiveCells,prevWinnerCells,colActiveCells,colMatchingCells,cells_segments):
    global TM_segActiveThresh,TM_permINC,TM_permDEC,TM_segNewSynCount,TM_learn, TM_segMaxSyns
    activeCs = list()
    winnerCs = list()
    for cell in colActiveCells:
        activeCs.append(cell)
        winnerCs.append(cell)
        if TM_learn:
            segList = cells_segments[cell]
            for seg in segList:
                segIndex = segList.index(seg)
                if len(activeSynapsesOnSegment(seg,prevActiveCells)) > TM_segActiveThresh:
                    for preSynCell,perm in seg.items():
                        if preSynCell in prevActiveCells:
                            if (perm + TM_permINC) > 1.0:
                                cells_segments[cell][segIndex][preSynCell] = 1.0
                            else:
                                cells_segments[cell][segIndex][preSynCell] += TM_permINC
                        else:
                            if (perm - TM_permDEC) < 0:
                                cells_segments[cell][segIndex][preSynCell] = 0
                            else:
                                cells_segments[cell][segIndex][preSynCell] -= TM_permDEC
                    key = str(cell) + '_' + str(segIndex)
                    if cellsSegInds_numActivePotentialSyns[key] < TM_segMaxSyns:
                        newSynCount = TM_segNewSynCount - cellsSegInds_numActivePotentialSyns[key]
                        growSynapses(seg,cell,newSynCount,prevWinnerCells)
    return activeCs,winnerCs,cells_segments

def burstCol(col,prevActiveCells,prevWinnerCells,activeSegCells,matchingSegCells,cells_segments,cellsSegInds_numActivePotentialSyns):
    global TM_segNewSynCount,TM_segMaxSyns
    activeCs = list()
    winnerCs = list()
    for cell in cellsFromCol(col):
        activeCs.append(cell)
    if len(matchingSegCells) > 0:
        learningSegment,winnerCell = bestMatchingSegmentAndCell(col,matchingSegCells,prevWinnerCells,cells_segments,cellsSegInds_numActivePotentialSyns)
    else:
        winnerCell = leastUsedCell(col,cells_segments)
        if TM_learn and len(prevWinnerCells) > 0:
            learningSegment,cells_segments = growNewSegment(winnerCell,prevWinnerCells,prevActiveCells,cells_segments)
    winnerCs.append(winnerCell)
    if TM_learn and len(prevWinnerCells) > 0:
        segIndex = cells_segments[winnerCell].index(learningSegment)
        for preSynCell,perm in learningSegment.items():
            if preSynCell in prevActiveCells:
                if (perm + TM_permINC) > 1.0:
                    cells_segments[winnerCell][segIndex][preSynCell] = 1.0
                else:
                    cells_segments[winnerCell][segIndex][preSynCell] += TM_permINC
            else:
                if (perm - TM_permDEC) < 0:
                    cells_segments[winnerCell][segIndex][preSynCell] = 0
                else:
                    cells_segments[winnerCell][segIndex][preSynCell] -= TM_permDEC
        key = str(cell) + '_' + str(segIndex)
        numActiveSynsOnSeg = 0
        if key in cellsSegInds_numActivePotentialSyns:
            numActiveSynsOnSeg = cellsSegInds_numActivePotentialSyns[key]
        newSynapseCount = max( (TM_segNewSynCount - numActiveSynsOnSeg),0)
        growSynapses(learningSegment,winnerCell,newSynapseCount,prevWinnerCells)
    return activeCs,winnerCs,cells_segments