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.
`neo` doesn't, but I am trying to keep things as generic as possible.  Considering we get support for the `axis` argument for free just by using `numpy`, I think it is more a question of why we wouldn't support it.  

I think the fewer assumptions we make about the format of the users' data the better.  Of course we shouldn't go crazy and support any imaginable use-case, but when we can keep things generic for free like here I think it is good to do so.

A simple reason to have multidimensional spike trains would be performance.  It is a lot faster for large datasets to combine them into a single large array and do the calculation on that than to loop through a lot of 1D arrays.