code: def _updateInhibitionRadius(self):
      
      if self._globalInhibition:
        self._inhibitionRadius = int(self._columnDimensions.max())
        return
  
      avgConnectedSpan = numpy.average(
                            [self._avgConnectedSpanForColumnND(i)
                            for i in xrange(self._numColumns)]
                          )
      columnsPerInput = self._avgColumnsPerInput()
      diameter = avgConnectedSpan * columnsPerInput
      radius = (diameter - 1) / 2.0
      radius = max(1.0, radius)
      self._inhibitionRadius = int(radius + 0.5)
docs: Update the inhibition radius. The inhibition radius is a measure of the
      square (or hypersquare) of columns that each a column is "connected to"
      on average. Since columns are are not connected to each other directly, we
      determine this quantity by first figuring out how many *inputs* a column is
      connected to, and then multiplying it by the total number of columns that
      exist for each input. For multiple dimension the aforementioned
      calculations are averaged over all dimensions of inputs and columns. This
      value is meaningless if global inhibition is enabled.