Wavelet analysis of olfactory nerve response to stimulus.
Multiunit electrophysiological activity recorded by gross electrodes from the olfactory nerve was analyzed by wavelet decomposition, a relatively new method of signal processing. The analysis was run on data from the unstimulated olfactory system as well as on data evoked in response to six different odorant stimuli. Like Fourier analysis, wavelet analysis provides a spectral decomposition of the signal. Unlike Fourier, wavelet analysis also locates the dominant spectral features in time. The output of a wavelet analysis can be further processed to enhance selected features. The increased amplitude of the nerve response evoked by stimulation was the most obvious feature, but efforts to learn from it were unproductive. The temporal pattern of receptor cell activity was much more yielding. The analysis resolved the nerve activity into three classes of events based on duration. On wavelet maps these classes of events separate out into three shifting and overlapping but distinct bands, one of which was interpreted as being associated with individual receptor cell firings and the other two as short and somewhat longer duration bursts of activity that was attributed to the synchronized firing of a group of receptor cells. This interpretation is supported by experiments in which waveforms simulating action potentials and bursts of action potentials are added to recorded data. Stimulation of the olfactory system with odorant molecules evokes a significant increase in the number of short duration bursts, and an amplitude increase that can be related to the number of receptor cells responding. Changes in the patterns of wavelet events can be associated with synchrony of cell firing, reset times for bursts of firing, and possibly other physiological dynamics. A number of differences in activity patterns with different odorants were observed, but without sufficient repeatability to allow reliable discrimination among them. While this study is clearly preliminary in that regard, it shows the potential of the wavelet method for contributing to the understanding of olfaction.
Lewalle
J
J
Department of Mechanical Aerospace and Manufacturing Engineering, Syracuse University, NY 13244-1240, USA.
Peek
F W
FW
Murphy
S J
SJ
D000818:Animals; D004594:Electrophysiology; D008959:Models, Neurological; D009812:Odorants; D009832:Olfactory Nerve / Q000502:physiology*; D018034:Olfactory Receptor Neurons / Q000502:physiology; D011892:Rana catesbeiana; D012815:Signal Processing, Computer-Assisted