diff --git a/SpatialConvolutionMap.lua b/SpatialConvolutionMap.lua
index 390ace03c..fcce69d1f 100644
--- a/SpatialConvolutionMap.lua
+++ b/SpatialConvolutionMap.lua
@@ -29,14 +29,14 @@ function nn.tables.random(nin, nout, nto)
    local tbl = torch.Tensor(nker, 2)
    local fi = torch.randperm(nin)
    local frcntr = 1
-   local nfi = math.floor(nin/nto) -- number of distinct nto chunks 
+   local nfi = math.floor(nin/nto) -- number of distinct nto chunks
    local totbl = tbl:select(2,2)
    local frtbl = tbl:select(2,1)
    local fitbl = fi:narrow(1, 1, (nfi * nto)) -- part of fi that covers distinct chunks
    local ufrtbl= frtbl:unfold(1, nto, nto)
    local utotbl= totbl:unfold(1, nto, nto)
    local ufitbl= fitbl:unfold(1, nto, nto)
-   
+
    -- start filling frtbl
    for i=1,nout do -- fro each unit in target map
       ufrtbl:select(1,i):copy(ufitbl:select(1,frcntr))
@@ -52,37 +52,6 @@ function nn.tables.random(nin, nout, nto)
    return tbl
 end
 
-local function constructTableRev(conMatrix)
-   local conMatrixL = conMatrix:type('torch.LongTensor')
-   -- Construct reverse lookup connection table
-   local thickness = conMatrixL:select(2,2):max()
-   -- approximate fanin check
-   if (#conMatrixL)[1] % thickness == 0 then 
-      -- do a proper fanin check and set revTable
-      local fanin = (#conMatrixL)[1] / thickness
-      local revTable = torch.Tensor(thickness, fanin, 2)
-      for ii=1,thickness do
-	 local tempf = fanin
-	 for jj=1,(#conMatrixL)[1] do
-	    if conMatrixL[jj][2] == ii then
-	       if tempf <= 0 then break end
-	       revTable[ii][tempf][1] = conMatrixL[jj][1]
-	       revTable[ii][tempf][2] = jj
-	       tempf = tempf - 1
-	    end
-	 end
-	 if tempf ~= 0 then 
-	    fanin = -1
-	    break
-	 end
-      end
-      if fanin ~= -1 then
-	 return revTable
-      end
-   end
-   return {}
-end
-
 function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
    parent.__init(self)
 
@@ -94,14 +63,13 @@ function SpatialConvolutionMap:__init(conMatrix, kW, kH, dW, dH)
    self.dW = dW
    self.dH = dH
    self.connTable = conMatrix
-   self.connTableRev = constructTableRev(conMatrix)
    self.nInputPlane = self.connTable:select(2,1):max()
    self.nOutputPlane = self.connTable:select(2,2):max()
    self.weight = torch.Tensor(self.connTable:size(1), kH, kW)
    self.bias = torch.Tensor(self.nOutputPlane)
    self.gradWeight = torch.Tensor(self.connTable:size(1), kH, kW)
    self.gradBias = torch.Tensor(self.nOutputPlane)
-   
+
    self:reset()
 end