  A population of neurons typically exhibits a broad diversity of responses to
sensory inputs. The intuitive notion of functional classification is that cells
can be clustered so that most of the diversity is captured in the identity of
the clusters rather than by individuals within clusters. We show how this
intuition can be made precise using information theory, without any need to
introduce a metric on the space of stimuli or responses. Applied to the retinal
ganglion cells of the salamander, this approach recovers classical results, but
also provides clear evidence for subclasses beyond those identified previously.
Further, we find that each of the ganglion cells is functionally unique, and
that even within the same subclass only a few spikes are needed to reliably
distinguish between cells.
