Noise and neuronal populations conspire to encode simple waveforms reliably.
Sensory systems rely on populations of neurons to encode information transduced at the periphery into meaningful patterns of neuronal population activity. This transduction occurs in the presence of intrinsic neuronal noise. This is fortunate. The presence of noise allows more reliable encoding of the temporal structure present in the stimulus than would be possible in a noise-free environment. Simulations with a parallel model of signal processing at the auditory periphery have been used to explore the effects of noise and a neuronal population on the encoding of signal information. The results show that, for a given set of neuronal modeling parameters and stimulus amplitude, there is an optimal amount of noise for stimulus encoding with maximum fidelity.