  Neuronal responses are conspicuously variable. We focus on one particular
aspect of that variability: the precision of action potential timing. We show
that for common models of noisy spike generation, elementary considerations
imply that such variability is a function of the input, and can be made
arbitrarily large or small by a suitable choice of inputs. Our considerations
are expected to extend to virtually any mechanism of spike generation, and we
illustrate them with data from the visual pathway. Thus, a simplification
usually made in the application of information theory to neural processing is
violated: noise {\sl is not independent of the message}. However, we also show
the existence of {\sl error-correcting} topologies, which can achieve better
timing reliability than their components.
