  The relationship between a neuron's complex inputs and its spiking output
defines the neuron's coding strategy. This is frequently and effectively
modeled phenomenologically by one or more linear filters that extract the
components of the stimulus that are relevant for triggering spikes, and a
nonlinear function that relates stimulus to firing probability. In many sensory
systems, these two components of the coding strategy are found to adapt to
changes in the statistics of the inputs, in such a way as to improve
information transmission. Here, we show for two simple neuron models how
feature selectivity as captured by the spike-triggered average depends both on
the parameters of the model and on the statistical characteristics of the
input.
