Gaussian-binary RBM
Rectified linear unit RBM
Added Noisy LIF one-layer networks

- nlif-rbm.py does a pseudo-RBM using NLIFs
- nlif-auto.py does a denoising autoencoder using NLIFs
Added deep NLIF autoencoder
Got it working in Nengo
Training combination autoencoder/classifier

- adding noise doesn't seem to help robustness :(
Added hinge loss code from James
Got hinge loss working

- unfortunately, no significant improvement
Added encoder learning - doesn't work
Playing around with clustering and LSH
Cleaned up clustering for Aaron
Added classical sigmoid RBM
Added LIF deep autoencoder (in separate folder)
Playing around with encoder learning using PES

- doesn't work
Renamed sigmoid rbm directory
Comparision of sigmoid and LIF in spiking

- results for Chris for CogSci 2014
Added no-code net with shifted training set

- No-code means there is no linear code layer, just LIFs until
  the classification output layer.
- Shifted training images significantly decrease the error on
  the test set.
Added info about clustering paper
Added a couple other classifiers to nocode