More functions and cleanups for elephant

This pull request does 4 main things:

1. It implements the discussed improvements in the `isi` function.
2. It adds a `firing_rate` function.  Its purpose should be self-explanatory.
3. It adds a `conversion` module, which is intended for various functions that convert between different representations.  The only function right now is `binarize`, which was discussed previously.
4. It moves the `cv` function into its own `compat` module, where we can put other NeuroTools-related compatibility functions.

All of these, including API and names (and whether we should do 4 at all) are open to discussion.

I am also working on a `pandas` interface for `elephant`, with conversion between `neo` data types and `pandas` ones, as well as helper functions for using `elephant` functions with these and other `pandas` objects.  The functions are written but I don't have unit tests yet, so I will save that for a later pull request.
Ok, that's a valid point. I haven't used pandas, what's the advantage of of having spike trains in a `DataFrame`? In this case, supporting multiple axes is only a very minor change. There are many more complex functions however, where it would be more work and/or make the interface more complex. `binarize` already doesn't include an axis argument. How do we decide which functions get an axis argument - those that use a numpy function that has one? What about functions that always work with multiple spike trains - do they take a list of spike trains, an array, both?