Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Speed and accuracy of olfactory discrimination in the rat

Abstract

The sense of smell is typically thought of as a 'slow' sense, but the true temporal constraints on the accuracy of olfactory perception are not known. It has been proposed that animals make finer odor discriminations at the expense of additional processing time. To test this idea, we measured the relationship between the speed and accuracy of olfactory discrimination in rats. We found that speed of discrimination was independent of odor similarity, as measured by overlap of glomerular activity patterns. Even when pushed to psychophysical limits using mixtures of two odors, rats needed to take only one sniff (<200 ms at theta frequency) to make a decision of maximum accuracy. These results show that, for the purpose of odor quality discrimination, a fully refined olfactory sensory representation can emerge within a single sensorimotor or theta cycle, suggesting that each sniff can be considered a snapshot of the olfactory world.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Rapid odor discrimination in monomolecular discrimination.
Figure 2: Odor sampling times are independent of odor similarity.
Figure 3: Parametric manipulation of odor quality and discrimination difficulty using a binary odor mixture discrimination task.
Figure 4: Odor sampling times are fast and largely independent of discrimination difficulty.
Figure 5: Odor discrimination accuracy shows asymptotic curve after brief odor sampling time.
Figure 6: Peak discrimination accuracy requires one sniff, regardless of difficulty.

References

  1. Korsching, S.I. Odor maps in the brain: spatial aspects of odor representation in sensory surface and olfactory bulb. Cell. Mol. Life Sci. 58, 520–530 (2001).

    Article  CAS  Google Scholar 

  2. Leon, M. & Johnson, B.A. Olfactory coding in the mammalian olfactory bulb. Brain Res. Brain Res. Rev. 42, 23–32 (2003).

    Article  Google Scholar 

  3. Mori, K. Grouping of odorant receptors: odour maps in the mammalian olfactory bulb. Biochem. Soc. Trans. 31, 134–136 (2003).

    Article  CAS  Google Scholar 

  4. Rubin, B.D. & Katz, L.C. Spatial coding of enantiomers in the rat olfactory bulb. Nat. Neurosci. 4, 355–356 (2001).

    Article  CAS  Google Scholar 

  5. Linster, C., Johnson, B.A., Morse, A., Yue, E. & Leon, M. Spontaneous versus reinforced olfactory discriminations. J. Neurosci. 22, 6842–6845 (2002).

    Article  CAS  Google Scholar 

  6. Yokoi, M., Mori, K. & Nakanishi, S. Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb. Proc. Natl. Acad. Sci. USA 92, 3371–3375 (1995).

    Article  CAS  Google Scholar 

  7. Mori, K., Nagao, H. & Yoshihara, Y. The olfactory bulb: coding and processing of odor molecule information. Science 286, 711–715 (1999).

    Article  CAS  Google Scholar 

  8. Adrian, E.D. The electrical activity of the mammalian olfactory bulb. Electroencephalography. Clin. Neurophysiol. 2, 377–388 (1950).

    Article  CAS  Google Scholar 

  9. Gelperin, A. & Tank, D.W. Odour-modulated collective network oscillations of olfactory interneurons in a terrestrial mollusc. Nature 345, 437–440 (1990).

    Article  CAS  Google Scholar 

  10. Freeman, W.J., Skarda, C.A. How brains make chaos in order to make sense of the world. Behav. Brain Sci. 10, 161–195 (1987).

    Article  Google Scholar 

  11. Laurent, G. Olfactory network dynamics and the coding of multidimensional signals. Nat. Rev. Neurosci. 3, 884–895 (2002).

    Article  CAS  Google Scholar 

  12. Rabinovich, M. et al. Dynamical encoding by networks of competing neuron groups: winnerless competition. Phys. Rev. Lett. 87, 068102 (e-pub, 2001).

    Article  CAS  Google Scholar 

  13. Brody, C.D. & Hopfield, J.J. Simple networks for spike-timing-based computation, with application to olfactory processing. Neuron 37, 843–852 (2003).

    Article  CAS  Google Scholar 

  14. Laurent, G. & Davidowitz, H. Encoding of olfactory information with oscillating neural assemblies. Science 265, 1872–1875 (1994).

    Article  CAS  Google Scholar 

  15. Wehr, M. & Laurent, G. Odour encoding by temporal sequences of firing in oscillating neural assemblies. Nature 384, 162–166 (1996).

    Article  CAS  Google Scholar 

  16. Stopfer, M., Bhagavan, S., Smith, B.H. & Laurent, G. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 390, 70–74 (1997).

    Article  CAS  Google Scholar 

  17. Kashiwadani, H., Sasaki, Y.F., Uchida, N. & Mori, K. Synchronized oscillatory discharges of mitral/tufted cells with different molecular receptive ranges in the rabbit olfactory bulb. J. Neurophysiol. 82, 1786–1792 (1999).

    Article  CAS  Google Scholar 

  18. Macrides, F. & Chorover, S.L. Olfactory bulb units: activity correlated with inhalation cycles and odor quality. Science 175, 84–87 (1972).

    Article  CAS  Google Scholar 

  19. Chaput, M.A. EOG responses in anesthetized freely breathing rats. Chem. Senses 25, 695–701 (2000).

    Article  CAS  Google Scholar 

  20. Margrie, T.W. & Schaefer, A.T. Theta oscillation coupled spike latencies yield computational vigour in a mammalian sensory system. J. Physiol. 546, 363–374 (2003).

    Article  CAS  Google Scholar 

  21. Cang, J. & Isaacson, J.S. In vivo whole-cell recording of odor-evoked synaptic transmission in the rat olfactory bulb. J. Neurosci. 23, 4108–4116 (2003).

    Article  CAS  Google Scholar 

  22. Welker, W.I. Analysis of sniffing of the albino rat. Behavior 22, 223–244 (1964).

    Article  Google Scholar 

  23. Laurent, G. & Naraghi, M. Odorant-induced oscillations in the mushroom bodies of the locust. J. Neurosci. 14, 2993–3004 (1994).

    Article  CAS  Google Scholar 

  24. Friedrich, R.W. & Laurent, G. Dynamic optimization of odor representations by slow temporal patterning of mitral cell activity. Science 291, 889–894 (2001).

    Article  CAS  Google Scholar 

  25. Meredith, M. Patterned response to odor in mammalian olfactory bulb: the influence of intensity. J. Neurophysiol. 56, 572–597 (1986).

    Article  CAS  Google Scholar 

  26. Stopfer, M. & Laurent, G. Short-term memory in olfactory network dynamics. Nature 402, 664–668 (1999).

    Article  CAS  Google Scholar 

  27. Ambros-Ingerson, J., Granger, R. & Lynch, G. Simulation of paleocortex performs hierarchical clustering. Science 247, 1344–1348 (1990).

    Article  CAS  Google Scholar 

  28. Karpov, A.P. Analysis of neuron activity in the rabbit's olfactory bulb during food-acquisition behavior in Neural Mechanisms of Goal-directed Behavior (eds. Thompson, R.F., Hicks, L.H. & Shvyrkov, V.B.) 273–282 (Academic, New York, 1980).

    Chapter  Google Scholar 

  29. Laing, D.G. Identification of single dissimilar odors is achieved by humans with a single sniff. Physiol. Behav. 37, 163–170 (1986).

    Article  CAS  Google Scholar 

  30. Goldberg, S.J. & Moulton, D.G. Olfactory bulb responses telemetered during an odor discrimination task in rats. Exp. Neurol. 96, 430–442 (1987).

    Article  CAS  Google Scholar 

  31. Slotnick, B.M. Olfactory perception in Comparative Perception (eds. Stebbins, W. & Berkley, M.) 155–244 (Wiley, New York, 1990).

    Google Scholar 

  32. Wise, P.M. & Cain, W.S. Latency and accuracy of discriminations of odor quality between binary mixtures and their components. Chem. Senses 25, 247–265 (2000).

    Article  CAS  Google Scholar 

  33. Uchida, N., Takahashi, Y.K., Tanifuji, M. & Mori, K. Odor maps in the mammalian olfactory bulb: domain organization and odorant structural features. Nat. Neurosci. 3, 1035–1043 (2000).

    Article  CAS  Google Scholar 

  34. Luce, R.D. Response Times: Their Role in Inferring Elementary Mental Organization (Oxford Univx. Press, New York, 1986).

    Google Scholar 

  35. Parker, A.J. & Newsome, W.T. Sense and the single neuron: probing the physiology of perception. Annu. Rev. Neurosci. 21, 227–277 (1998).

    Article  CAS  Google Scholar 

  36. VanRullen, R. & Thorpe, S.J. The time course of visual processing: from early perception to decision-making. J. Cogn. Neurosci. 13, 454–461 (2001).

    Article  CAS  Google Scholar 

  37. Johnson, B.N., Mainland, J.D. & Sobel, N. Rapid olfactory processing implicates subcortical control of an olfactomotor system. J. Neurophysiol. 90, 1084–1094 (2003).

    Article  Google Scholar 

  38. Atema, J. Chemical signals in the marine environment: dispersal, detection, and temporal signal analysis. Proc. Natl. Acad. Sci. USA 92, 62–66 (1995).

    Article  CAS  Google Scholar 

  39. Vickers, N.J. & Baker, T.C. Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths. Proc. Natl. Acad. Sci. USA 91, 5756–5760 (1994).

    Article  CAS  Google Scholar 

  40. Nevitt, G.A. Do fish sniff? A new mechanism of olfactory sampling in pleuronectid flounders. J. Exp. Biol. 157, 1–18 (1991).

    CAS  PubMed  Google Scholar 

  41. Chaput, M.A. Respiratory-phase-related coding of olfactory information in the olfactory bulb of awake freely-breathing rabbits. Physiol. Behav. 36, 319–324 (1986).

    Article  CAS  Google Scholar 

  42. Spors, H. & Grinvald, A. Spatio-temporal dynamics of odor representations in the mammalian olfactory bulb. Neuron 34, 301–315 (2002).

    Article  CAS  Google Scholar 

  43. Macrides, F., Eichenbaum, H.B. & Forbes, W.B. Temporal relationship between sniffing and the limbic theta rhythm during odor discrimination reversal learning. J. Neurosci. 2, 1705–1717 (1982).

    Article  CAS  Google Scholar 

  44. Hopfield, J.J. Odor space and olfactory processing: collective algorithms and neural implementation. Proc. Natl. Acad. Sci. USA 96, 12506–12511 (1999).

    Article  CAS  Google Scholar 

  45. Mackay-Sim, A. & Kesteven, S. Topographic patterns of responsiveness to odorants in the rat olfactory epithelium. J. Neurophysiol. 71, 150–160 (1994).

    Article  CAS  Google Scholar 

  46. Meister, M. & Bonhoeffer, T. Tuning and topography in an odor map on the rat olfactory bulb. J. Neurosci. 21, 1351–1360 (2001).

    Article  CAS  Google Scholar 

  47. Wichmann, F.A. & Hill, N.J. The psychometric function: fitting, sampling, and goodness of fit. Percept. Psychophys. 63, 1293–1313 (2001).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank S. Edgar, H. Zariwala, E. Friedman and G. Agarwal for behavioral training and testing, and R. Gasperini for development of instruments. We thank members of our group and colleagues at CSHL for discussion, as well as T. Zador, C. Brody, R. Malinow, A. Kepecs, M. DeWeese, M. Tanifuji and Y. Yoshihara for comments on a previous version of the manuscript. Supported by the National Institute on Deafness and Other Communication Disorders (5R01DC006104-02), Searle Scholars Program, Packard Foundation and Burroughs Wellcome Fund (Z.F.M.), as well as by a fellowship from the Japan Society for the Promotion of Science and the Cold Spring Harbor Laboratory Association (N.U.).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zachary F Mainen.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Fig. 1.

Optical imaging of activity evoked by odorants used in the behavioral study. Representative images obtained using aliphatic alcohols (hexanol, heptanol, S(+)-octanol and R(-)-2-octanol) and acids (caproic acid and butyric acid). Each image shows a thresholded, pseudo-colored response image superimposed on an image of the vasculature. Note that odorants having the same functional group activated highly overlapping glomeruli. The color scale at the right indicates the mapping signal (relative range in reflectance, see Supplementary Methods). P, posterior; L, lateral. (PDF 2136 kb)

Supplementary Fig. 2.

Rapid performance holds in different experimental conditions. (a, b) Rapid performance is insensitive to interleaving of eight stimuli. Four rats were tested in a series of experiments using single complementary pairs of mixture ratios (80/20 and 20/80, etc.) in individual sessions using the odor pair, caproic acid versus hexanol (two sessions per condition). Odor sampling times obtained when only a single pair of mixture ratios was tested in a session (red) were similar to those obtained in the standard interleaved condition (black). (c, d) Rapid performance is insensitive to odor concentration. In order to test whether odor concentration would affect sampling strategy or accuracy, four rats were tested using S(+)- and R(-)-2-octanols at 100-fold lower concentration (mineral oil dilution). Performance accuracy and odor sampling times were similar between standard (black) and low concentration (red) conditions. (e, f) Rapid performance is insensitive to foreperiod. It has been observed that the variance of reaction times can be affected by the foreperiod (Roitman, J. D. & Shadlen, M. N. J. Neurosci. 22, 9475-89, 2002), the time between when the subject initiates the trial and the onset of the stimulus. In standard conditions, a uniform random delay of 0-100 ms between the detection of the nose poke and opening of the odor valve was used to prevent anticipation of odor onset. Three additional rats were tested using longer, exponentially distributed odor onset delays (i.e. a flat hazard function (Luce, R. D. Response Times: Their Role in Inferring Elementary Mental Organization 1986) with mean of 300 ms) on caproic acid versus hexanol. Odor sampling times were similar in standard (black) and longer foreperiod (red) conditions. (PDF 177 kb)

Supplementary Fig. 3.

Design of custom olfactometer. An olfactometer was constructed using small diameter (1/32" inner diameter) Teflon (PTFE) tubing and compression fittings to minimize dead space and delay times. (i) Flow rates of two air streams were independently controlled by mass flow controllers (range 2-100 ml/min) (100). A carrier air stream was controlled by an third flow controller (range: 20-1,000 ml/min) (1000) to produce 10:1 or greater dilution at a total flow rate of 1,000 ml/min. By mixing odorized air streams with defined flow rates, different mixture ratios were achieved. (ii) Two-way micro-solenoid valves controlled the timing of odor delivery. (iii) Saturated odor vapor was produced by flowing air across syringe filters loaded with liquid odorants (A, B). To maintain constant flow rates, valves for blank filters were actuated when odor delivery were closed. (iv) Odor and carrier streams were mixed at a manifold downstream of all valves immediately before the odor sampling port. The manifold was constructed out of chemically-inert polyetheretherketone (PEEK) material. (PDF 162 kb)

Supplementary Fig. 4.

Intrinsic signal imaging and analysis. (a) Intrinsic signals evoked by aliphatic acids and alcohols (as indicated above images). The top left panel shows a negative control image (pure air). All images were taken from the same rat. Signal intensity scale is indicated on the right, where negative values indicate darkening (activation). (b) Positions of identified glomeruli (yellow circles) superimposed on the vasculature image. P, posterior; L, lateral. (c) Summary of patterns of intrinsic signals in identified glomeruli. Black circles indicate average signal intensity and red circles indicate standard deviation (SD). Scales for average signal and SD are the same and shown at the upper right. Note that signal intensities have been inverted to positive values. Glomeruli were numbered by the position from anterior (A) to posterior (P) in (b). (PDF 1851 kb)

Supplementary Fig. 5.

Cluster analysis of glomerular activation patterns calculated using different similarity metrics. The glomerular activity pattern evoked by each odorant (rows in Supplementary Fig. 4c) was treated as a vector and cluster analysis was performed using normalized and non-normalized methods for calculating vector distance (dissimilarity). (a) Clustering calculated using a normalized distance metric, 1 - cos(α), where α is the angle between the two vectors (b) Clustering calculated using a Euclidian distance metric. Note that the two methods produced similar patterns with the exception of a minor difference in the alcohol sub-cluster. (PDF 167 kb)

Supplementary Methods (PDF 23 kb)

Supplementary Video.

Rat performing the odor mixture discrimination task. Binary mixtures of stereoisomers, S(+)-2-octanol (odor A) and R(-)-2-octanol (odor B), were delivered from the center port. The correct choice (the dominant component in the mixture) in each trial is indicated by a letter at the center which appears from the beginning of each trial to the beginning of nose poke. The rat sampled the odor at the central odor port and made a choice poke into left or right choice port (indicated by A and B, respectively). An interval of 4 s was imposed between choice poke and the beginning of the next trial. Note that the third trial choice was incorrect. (MOV 2801 kb)

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Uchida, N., Mainen, Z. Speed and accuracy of olfactory discrimination in the rat. Nat Neurosci 6, 1224–1229 (2003). https://doi.org/10.1038/nn1142

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nn1142

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing