 		MathUtil.sigmoid(x)
 	end
 
-	def sync_function() do
-		[1, 1, 1, 1, 1]
-	end 
-
-	def weight(x) do
-		x
-	end
-
 end
 
 defrecord MockNode, pid: nil do


	test "neural net which can solve the XNOR problem.  no learning involved (class.coursera.org/ml/lecture/48)" do

		# Create values that the sensors will generate
		sensor_x1_val_generator = MathUtil.create_generator( [[0], [0], [1], [1]] )
		sensor_x2_val_generator = MathUtil.create_generator( [[0], [1], [0], [1]] )

		# Create nodes
		sensor_x1 = sensor( id: make_ref(), sync_function: sensor_x1_val_generator )
		sensor_x2 = sensor( id: make_ref(), sync_function: sensor_x2_val_generator )
		neuron_a2_1 = neuron( id: make_ref(), bias: -30, activation_function: function(sigmoid/1) )
		neuron_a2_2 = neuron( id: make_ref(), bias: 10, activation_function: function(sigmoid/1) )
		neuron_a3_1 = neuron( id: make_ref(), bias: -10, activation_function: function(sigmoid/1) )


		# Create nodes
		neuron = neuron( id: make_ref(), bias: 10, activation_function: function(identity/1) )
		sensor = sensor( id: make_ref(), sync_function: fake_sensor_data( [ [1, 1, 1, 1, 1] ] ) )
		actuator = actuator( id: make_ref() )

		# Wire up network
