This question has been asked and answered on the Theano mailing list, but is actually about the basics of numpy indexing.
Here are the question and answer
https://groups.google.com/forum/#!topic/theano-users/jq92vNtkYUI
For completeness, here is another explanation: slicing with None adds an axis to your array, see the relevant numpy documentation, because it behaves the same in both numpy and Theano:
http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#numpy.newaxis
Note that np.newaxis is None:
import numpy as np
a = np.arange(30).reshape(5, 6)
print a.shape # yields (5, 6)
print a[np.newaxis, :, :].shape # yields (1, 5, 6)
print a[:, np.newaxis, :].shape # yields (5, 1, 6)
print a[:, :, np.newaxis].shape # yields (5, 6, 1)
Typically this is used to adjust shapes to be able to broadcast to higher dimensions. E.g. tiling 7 times in the middle axis can be achieved as
b = a[:, np.newaxis] * np.ones((1, 7, 1))
print b.shape # yields (5, 7, 6), 7 copies of a along the second axis