code: def burstColumn(self, column, columnMatchingSegments, prevActiveCells,
                    prevWinnerCells, learn):
      
  
      start = self.cellsPerColumn * column
  
      # Strip out destroyed cells before passing along to base _burstColumn()
      cellsForColumn = [cellIdx
                        for cellIdx
                        in xrange(start, start + self.cellsPerColumn)
                        if cellIdx not in self.deadCells]
  
      return self._burstColumn(
        self.connections, self._random, self.lastUsedIterationForSegment, column,
        columnMatchingSegments, prevActiveCells, prevWinnerCells, cellsForColumn,
        self.numActivePotentialSynapsesForSegment, self.iteration,
        self.maxNewSynapseCount, self.initialPermanence, self.permanenceIncrement,
        self.permanenceDecrement, self.maxSegmentsPerCell,
        self.maxSynapsesPerSegment, learn)
docs: Activates all of the cells in an unpredicted active column, chooses a winner
      cell, and, if learning is turned on, learns on one segment, growing a new
      segment if necessary.
  
      @param column (int)
      Index of bursting column.
  
      @param columnMatchingSegments (iter)
      Matching segments in this column, or None if there aren't any.
  
      @param prevActiveCells (list)
      Active cells in `t-1`.
  
      @param prevWinnerCells (list)
      Winner cells in `t-1`.
  
      @param learn (bool)
      Whether or not learning is enabled.
  
      @return (tuple) Contains:
                        `cells`         (iter),
                        `winnerCell`    (int),