A parametric study of the stimulation variables affecting the magnitude of the olfactory nerve response

• Published in: The Journal of general physiology Vol. 83; no. 2; pp. 233 - 267
• Main Authors: MOZELL, M. M, SHEEHE, P. R, SWIECK, S. W. JR, KURTZ, D. B, HORNUNG, D. E
• Format: Journal Article
• Language: English
• Published: New York, NY Rockefeller University Press 01.02.1984; The Rockefeller University Press
• Subjects: Analysis of Variance; Animals; Biological and medical sciences; Central Nervous System - physiology; Fundamental and applied biological sciences. Psychology; Humans; Mathematics; Models, Biological; Odorants; Olfactory Mucosa - physiology; Olfactory Nerve - physiology; Olfactory Pathways - physiology; Olfactory system and olfaction. Gustatory system and gustation; Rana catesbeiana; Smell - physiology; Vertebrates: nervous system and sense organs; Amphibia; Electrophysiology; Vertebrata; Salientia; Frog; Olfactory nerve
• ISSN: 0022-1295; 1540-7748
• DOI: 10.1085/jgp.83.2.233

The magnitude of olfactory responses can be related to three primary variables [number of odorant molecules (N), sniff volume (V), and sniff duration (T)] and three derived variables [concentration (C = N/V), flow rate (F = V/T), and delivery rate (D = N/T)]. To evaluate the effects of these interdependent variables upon the olfactory response, the summated multiunit discharges were recorded from the olfactory nerves of nine frogs in response to octane presented at two levels (in 2:1 ratio) of each primary variable. This presentation defined eight "sniff" combinations representing three levels of each derived variable. In an ANOVA of the logs of the responses, the effect of each primary variable was highly significant, with no significant interactions. A multiplicative regression model incorporating the effects of the three primary variables represented responses exceedingly well, with positive effects of N and T and a negative effect of V. When, with this model, the effect of each of the derived variables was isolated from the effects of all other variables, the analysis showed a positive effect for C, a near-zero positive effect for D, and a negative effect for F. Placing certain constraints upon the model parameters generates 13 distinct one- and two-variable models (e.g., the [C, T] model requires N and V to have equal but opposite effects). In ranking these reduced models in terms of their ability to predict the neural response, the predictive ability of [F, N] and [C, T] was at least as good as that of the three-variable model.