  We consider signal transaction in a simple neuronal model featuring intrinsic
noise. The presence of noise limits the precision of neural responses and
impacts the quality of neural signal transduction. We assess the signal
transduction quality in relation to the level of noise, and show it to be
maximized by a non-zero level of noise, analogous to the stochastic resonance
effect. The quality enhancement occurs for a finite range of stimuli to a
single neuron; we show how to construct networks of neurons that extend the
range. The range increases more rapidly with network size when we make use of
heterogeneous populations of neurons with a variety of thresholds, rather than
homogeneous populations of neurons all with the same threshold. The limited
precision of neural responses thus can have a direct effect on the optimal
network structure, with diverse functional properties of the constituent
neurons supporting an economical information processing strategy that reduces
the metabolic costs of handling a broad class of stimuli.
