Autoencoder and Neural Network Overfitting in terms of parameter number?
Additional drop out layers do not reduce parameters. Drop out is applied after each sample independently. This is done to Limit overfitting. So you still have the same parameters.

From over 6000 dimensions to only 2 seems a bit too much. But it is hard to see. You could run a PCA Analysis and use that as a starting point for the number of hidden neurons, e.g. get the dimensionality that Accounts for 75% of the Variation in your data and go from there. Also try a second layer and add some form of regularization.