Clustering and backpropagation
	• We need to tie the input->hidden weights to be the same as the hidden->output weights. – Usually, we cannot backpropagate through binary hidden units, but in this case the derivatives for the input- >hidden weights all become zero! • If the winner doesn’t change – no derivative • The winner changes when two hidden units give exactly the same error – no derivative • So the only error-derivative is for the output weights. This derivative pulls the weight vector of the winning cluster towards the data point. When the weight vector is at the center of gravity of a cluster, the derivatives all balance out because the c. of g. minimizes squared error.