Skip to main content

Thank you for visiting 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.

Spatial maps in piriform cortex during olfactory navigation


Odours are a fundamental part of the sensory environment used by animals to guide behaviours such as foraging and navigation1,2. Primary olfactory (piriform) cortex is thought to be the main cortical region for encoding odour identity3,4,5,6,7,8. Here, using neural ensemble recordings in freely moving rats performing an odour-cued spatial choice task, we show that posterior piriform cortex neurons carry a robust spatial representation of the environment. Piriform spatial representations have features of a learned cognitive map, being most prominent near odour ports, stable across behavioural contexts and independent of olfactory drive or reward availability. The accuracy of spatial information carried by individual piriform neurons was predicted by the strength of their functional coupling to the hippocampal theta rhythm. Ensembles of piriform neurons concurrently represented odour identity as well as spatial locations of animals, forming an odour–place map. Our results reveal a function for piriform cortex in spatial cognition and suggest that it is well-suited to form odour–place associations and guide olfactory-cued spatial navigation.

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.


All prices are NET prices.

Fig. 1: Odour-cued allocentric spatial choice task.
Fig. 2: Spatial representations in the piriform cortex.
Fig. 3: Spatial representations are robust and stable across task events.
Fig. 4: Location neurons are coupled to hippocampal theta rhythms.

Data availability

Data will be made available upon reasonable request to the corresponding authors.


  1. Baker, K. L. et al. Algorithms for olfactory search across species. J. Neurosci. 38, 9383–9389 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Jacobs, L. F. From chemotaxis to the cognitive map: the function of olfaction. Proc. Natl Acad. Sci. USA 109, 10693–10700 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Miura, K., Mainen, Z. F. & Uchida, N. Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity. Neuron 74, 1087–1098 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Poo, C. & Isaacson, J. S. Odor representations in olfactory cortex: ‘sparse’ coding, global inhibition, and oscillations. Neuron 62, 850–861 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Illig, K. R. & Haberly, L. B. Odor-evoked activity is spatially distributed in piriform cortex. J. Comp. Neurol. 457, 361–373 (2003).

    PubMed  Google Scholar 

  6. Haberly, L. B. Single unit responses to odor in the prepyriform cortex of the rat. Brain Res. 12, 481–484 (1969).

    CAS  PubMed  Google Scholar 

  7. Rennaker, R. L., Chen, C.-F. F., Ruyle, A. M., Sloan, A. M. & Wilson, D. A. Spatial and temporal distribution of odorant-evoked activity in the piriform cortex. J. Neurosci. 27, 1534–1542 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Stettler, D. D. & Axel, R. Representations of odor in the piriform cortex. Neuron 63, 854–864 (2009).

    CAS  PubMed  Google Scholar 

  9. Gire, D. H., Kapoor, V., Arrighi-Allisan, A., Seminara, A. & Murthy, V. N. Mice develop efficient strategies for foraging and navigation using complex natural stimuli. Curr. Biol. 26, 1261–1273 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Eichenbaum, H. Using olfaction to study memory. Ann. NY Acad. Sci. 855, 657–669 (1998).

    ADS  CAS  PubMed  Google Scholar 

  11. Johnson, D. M. G., Illig, K. R., Behan, M. & Haberly, L. B. New features of connectivity in piriform cortex visualized by intracellular injection of pyramidal cells suggest that ‘primary’ olfactory cortex functions like ‘association’ cortex in other sensory systems. J. Neurosci. 20, 6974–6982 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Franks, K. M. et al. Recurrent circuitry dynamically shapes the activation of piriform cortex. Neuron 72, 49–56 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Hagiwara, A., Pal, S. K., Sato, T. F., Wienisch, M. & Murthy, V. N. Optophysiological analysis of associational circuits in the olfactory cortex. Front. Neural Circuits 6, 18 (2012).

    PubMed  PubMed Central  Google Scholar 

  14. Hasselmo, M. E. & Bower, J. M. Afferent and association fiber differences in short-term potentiation in piriform (olfactory) cortex of the rat. J. Neurophysiol. 64, 179–190 (1990).

    CAS  PubMed  Google Scholar 

  15. Poo, C. & Isaacson, J. S. A major role for intracortical circuits in the strength and tuning of odor-evoked excitation in olfactory cortex. Neuron 72, 41–48 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Haberly, L. B. & Price, J. L. Association and commissural fiber systems of the olfactory cortex of the rat. I. Systems originating in the piriform cortex and adjacent areas. J. Comp. Neurol. 178, 711–740 (1978).

    CAS  PubMed  Google Scholar 

  17. Haberly, L. B. & Bower, J. M. Olfactory cortex: model circuit for study of associative memory? Trends Neurosci. 12, 258–264 (1989).

    CAS  PubMed  Google Scholar 

  18. Hasselmo, M. E. & Linster, C. in Models of Cortical Circuits (eds Ulinski, P. S. et al.) 525–560 (Springer, 1999).

  19. Barkai, E., Bergman, R. E., Horwitz, G. & Hasselmo, M. E. Modulation of associative memory function in a biophysical simulation of rat piriform cortex. J. Neurophysiol. 72, 659–677 (1994).

    CAS  PubMed  Google Scholar 

  20. Haberly, L. B. Parallel-distributed processing in olfactory cortex: new insights from morphological and physiological analysis of neuronal circuitry. Chem. Senses 26, 551–576 (2001).

    CAS  PubMed  Google Scholar 

  21. Wang, L. et al. Cell-type-specific whole-brain direct inputs to the anterior and posterior piriform cortex. Front. Neural Circuits (2020).

  22. Bolding, K. A. & Franks, K. M. Complementary codes for odor identity and intensity in olfactory cortex. eLife 6, e22630 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. Roland, B., Deneux, T., Franks, K. M., Bathellier, B. & Fleischmann, A. Odor identity coding by distributed ensembles of neurons in the mouse olfactory cortex. eLife 6, e26337 (2017).

    PubMed  PubMed Central  Google Scholar 

  24. Iurilli, G. & Datta, S. R. Population coding in an innately relevant olfactory area. Neuron 93, 1180–1197.e7 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Calu, D. J., Roesch, M. R., Stalnaker, T. A. & Schoenbaum, G. Associative encoding in posterior piriform cortex during odor discrimination and reversal learning. Cereb. Cortex 17, 1342–1349 (2007).

    PubMed  Google Scholar 

  26. Haddad, R. et al. Olfactory cortical neurons read out a relative time code in the olfactory bulb. Nat. Neurosci. 16, 949–957 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Zelano, C., Mohanty, A. & Gottfried, J. A. Olfactory predictive codes and stimulus templates in piriform cortex. Neuron 72, 178–187 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Howard, J. D., Plailly, J., Grueschow, M., Haynes, J.-D. & Gottfried, J. A. Odor quality coding and categorization in human posterior piriform cortex. Nat. Neurosci. 12, 932–938 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang, D. et al. Task–demand-dependent neural representation of odor information in the olfactory bulb and posterior piriform cortex. J. Neurosci. 39, 10002–10018 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Lak, A. et al. Reinforcement biases subsequent perceptual decisions when confidence is low, a widespread behavioral phenomenon. eLife 9, e49834 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Wallace, D. G., Gorny, B. & Whishaw, I. Q. Rats can track odors, other rats, and themselves: implications for the study of spatial behavior. Behav. Brain Res. 131, 185–192 (2002).

    PubMed  Google Scholar 

  32. Millman, D. J. & Murthy, V. N. Rapid learning of odor-value association in the olfactory striatum. J. Neurosci. 40, 4335–4347 (2020).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Tolman, E. C. Cognitive maps in rats and men. Psychol. Rev. 55, 189 (1948).

    CAS  PubMed  Google Scholar 

  34. O’Keefe, J. & Nadel, L. The Hippocampus as a Cognitive Map (Clarendon Press, 1978).

  35. O’Keefe, J. & Recce, M. L. Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317–330 (1993).

    PubMed  Google Scholar 

  36. Kepecs, A., Uchida, N. & Mainen, Z. F. Rapid and precise control of sniffing during olfactory discrimination in rats. J. Neurophysiol. 98, 205–213 (2007).

    PubMed  Google Scholar 

  37. Martin, C., Beshel, J. & Kay, L. M. An olfacto-hippocampal network is dynamically involved in odor-discrimination learning. J. Neurophysiol. 98, 2196–2205 (2007).

    PubMed  Google Scholar 

  38. Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. P. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021).

    ADS  CAS  PubMed  Google Scholar 

  39. Aqrabawi, A. J. & Kim, J. C. Hippocampal projections to the anterior olfactory nucleus differentially convey spatiotemporal information during episodic odour memory. Nat. Commun. 9, 2735 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  40. Igarashi, K. M., Lu, L., Colgin, L. L., Moser, M.-B. & Moser, E. I. Coordination of entorhinal–hippocampal ensemble activity during associative learning. Nature 510, 143–147 (2014).

    ADS  CAS  PubMed  Google Scholar 

  41. Agster, K. L. & Burwell, R. D. Cortical efferents of the perirhinal, postrhinal, and entorhinal cortices of the rat. Hippocampus 19, 1159–1186 (2009).

    PubMed  PubMed Central  Google Scholar 

  42. Chapuis, J. et al. Lateral entorhinal modulation of piriform cortical activity and fine odor discrimination. J. Neurosci. 33, 13449–13459 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Allen Institute for Brain Science. Allen Mouse Brain Connectivity Atlas (2011).

  44. Insausti, R., Herrero, M. T. & Witter, M. P. Entorhinal cortex of the rat: cytoarchitectonic subdivisions and the origin and distribution of cortical efferents. Hippocampus 7, 146–183 (1997).

    CAS  PubMed  Google Scholar 

  45. Stachenfeld, K. L., Botvinick, M. M. & Gershman, S. J. The hippocampus as a predictive map. Nat. Neurosci. 20, 1643–1653 (2017).

    CAS  PubMed  Google Scholar 

  46. Anderson, M. I. & Jeffery, K. J. Heterogeneous modulation of place cell firing by changes in context. J. Neurosci. 23, 8827 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Moita, M. A. P., Rosis, S., Zhou, Y., LeDoux, J. E. & Blair, H. T. Hippocampal place cells acquire location-specific responses to the conditioned stimulus during auditory fear conditioning. Neuron 37, 485–497 (2003).

    CAS  PubMed  Google Scholar 

  48. Knierim, J. J. Dynamic interactions between local surface cues, distal landmarks, and intrinsic circuitry in hippocampal place cells. J. Neurosci. 22, 6254–6264 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Radvansky, B. A. & Dombeck, D. A. An olfactory virtual reality system for mice. Nat. Commun. 9, 839 (2018).

    ADS  PubMed  PubMed Central  Google Scholar 

  50. Fischler-Ruiz, W. et al. Olfactory landmarks and path integration converge to form a cognitive spatial map. Neuron 10.1016/j.neuron.2021.09.055 (2021).

    Article  PubMed  Google Scholar 

  51. Muller, R. U. & Kubie, J. L. The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells. J. Neurosci. 7, 1951–1968 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Moita, M. A. P., Rosis, S., Zhou, Y., LeDoux, J. E. & Blair, H. T. Putting fear in its place: remapping of hippocampal place cells during fear conditioning. J. Neurosci. 24, 7015–7023 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. Saleem, A. B., Diamanti, E. M., Fournier, J., Harris, K. D. & Carandini, M. Coherent encoding of subjective spatial position in visual cortex and hippocampus. Nature 562, 124–127 (2018).

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  54. Town, S. M., Brimijoin, W. O. & Bizley, J. K. Egocentric and allocentric representations in auditory cortex. PLoS Biol. 15, e2001878 (2017).

    PubMed  PubMed Central  Google Scholar 

  55. Fiser, A. et al. Experience-dependent spatial expectations in mouse visual cortex. Nat. Neurosci. 19, 1658–1664 (2016).

    CAS  PubMed  Google Scholar 

  56. Choi, G. B. et al. Driving opposing behaviors with ensembles of piriform neurons. Cell 146, 1004–1015 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Uchida, N. & Mainen, Z. F. Speed and accuracy of olfactory discrimination in the rat. Nat. Neurosci. 6, 1224–1229 (2003).

    CAS  PubMed  Google Scholar 

  58. Felsen, G. & Mainen, Z. F. Neural substrates of sensory-guided locomotor decisions in the rat superior colliculus. Neuron 60, 137–148 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Restle, F. Discrimination of cues in mazes: a resolution of the ‘place-vs.-response’ question. Psychol. Rev. 64, 217 (1957).

    CAS  PubMed  Google Scholar 

  60. Lopes, G. et al. Bonsai: an event-based framework for processing and controlling data streams. Front. Neuroinform. 9, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  61. Hill, D. N., Mehta, S. B. & Kleinfeld, D. Quality metrics to accompany spike sorting of extracellular signals. J. Neurosci. 31, 8699–8705 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Dayan, P. & Abbott, L. F. Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems (MIT Press, 2001).

  63. Green, D. M., Swets, J. A. et al. Signal Detection Theory and Psychophysics Vol. 1 (Wiley, 1966).

  64. Rolls, E. T. & Tovee, M. J. Sparseness of the neuronal representation of stimuli in the primate temporal visual cortex. J. Neurophysiol. 73, 713–726 (1995).

    CAS  PubMed  Google Scholar 

  65. Willmore, B. & Tolhurst, D. J. Characterizing the sparseness of neural codes. Network 12, 255–270 (2001).

    CAS  PubMed  Google Scholar 

  66. Fan, R.-E., Chang, K.-W., Hsieh, C.-J., Wang, X.-R. & Lin, C.-J. LIBLINEAR: a library for large linear classification. J. Mach. Learn. Res. 9, 1871–1874 (2008).

    MATH  Google Scholar 

  67. Richard, G. R. et al. Speed modulation of hippocampal theta frequency correlates with spatial memory performance. Hippocampus 23, 1269–1279 (2013).

    PubMed  PubMed Central  Google Scholar 

  68. Kay, L. M., Beshel, J., Brea, J. & Martin, C. Olfactory oscillations: the what, how and what for. Trends Neurosci. 32, 207–214 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  69. Litaudon, P., Amat, C., Bertrand, B., Vigouroux, M. & Buonviso, N. Piriform cortex functional heterogeneity revealed by cellular responses to odours. Eur. J. Neurosci. 17, 2457–2461 (2003).

    CAS  PubMed  Google Scholar 

  70. Mitra, P. & Bokil, H. Observed Brain Dynamics (Oxford Univ. Press, 2007).

Download references


We thank J. J. Paton, B. A. Atallah, L. L. Glickfeld and J. B. Hales for comments on the manuscript; members of the Mainen laboratory, E. Lottem, M. Murakami, M. G. Bergomi, C. Linster, M. Moita, A. Fleischmann, K. M. Franks, S. R. Datta, C. E. Schoonover, A. J. P. Fink, and R. Axel for helpful discussions; A. S. Cruz and A. C. Rato for assistance with animal training; L. M. Frank and members of the Frank laboratory for experimental advice and assistance; G. Costa for scientific illustrations; Champalimaud Research Hardware Platform for custom components used in the behavioural task and recordings; and Champalimaud Vivarium Platform for animal care. We acknowledge Champalimaud Foundation (Z.F.M.), European Research Council (Advanced Investigator Grant 671251, Z.F.M.), Human Frontier Science Program (LT000402/2012, C.P.), Fundação para a Ciência e a Tecnologia (FCT-PTDC/MED-NEU/28509/2017, C.P.), and Helen Hay Whitney Foundation (C.P.) for financial support. This work was supported by Portuguese national funds, through Fundação para a Ciência e a Tecnologia (FCT)—UIDB/04443/2020, CONGENTO, co-financed by Lisboa Regional Operational Programme (Lisboa2020), and FCT LISBOA-01-0145-FEDER-022170.

Author information

Authors and Affiliations



The project was originally conceptualized by C.P. and Z.F.M. and further developed in collaboration with N.B. The behavioural paradigm was developed and designed by C.P., N.B. and Z.F.M. Task-related hardware was developed and constructed by N.B. and C.P. Task-related software was developed and implemented by N.B. Animal training, behaviour data collection and behavioural data analysis was performed by C.P. Neural recordings were performed by C.P. Neural data analysis was performed by C.P. and G.A. The manuscript was written by C.P., G.A. and Z.F.M. and edited and reviewed by all authors.

Corresponding authors

Correspondence to Cindy Poo or Zachary F. Mainen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review information

Nature thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 Tetrode lesion sites and population summary of pPCx and CA1 neurons.

(a) Tetrode lesion sites for 3 recorded rats in pPCx. See full methods for targeting and verification of tetrode recording sites. Due to the wide range of tetrode lesion sites (from −1.5 mm to −2.5 mm bregma), lesion sites from multiple histological sections were summarized onto 3 representative atlas sections. (b) Summary PETH for all pPCx (n = 995) neurons recorded. Spike timing was aligned to odour onset (left) and goal poke-in (right). Neurons were sorted based on their activity during 1 s time window after odour onset in both left and right panels. For each neuron, the mean z-scored rate during a 2 s time window prior to alignment time point was subtracted from the entire PETH. (c) Tetrode lesion sites for 3 recorded rats in dorsal CA1. See Full Methods for targeting and verification of tetrode recording sites. (d) Summary PETH for all CA1 (n = 154) neurons recorded.

Extended Data Fig. 2 Correct and error trial behavior in odour-cued spatial choice task.

(a) Odour sampling duration in 3 recorded rats. A minimum of 150 ms of odour sampling was enforced. Black: correct trials, 0.519 ± 0.02 s. Red: error trials, 0. 448 ± 0.04 s. (b) Navigation duration in 3 recorded rats, as defined by time between initiation (odour) port poke-out and goal port poke-in. Goal ports were only active (reward available) after a 1.5 s delay after rats poke out of initiation ports. Black: correct trials, 3.67 ± 0.15 s. Red: error trials, 5.35 ± 0.8 s. (c) Rats performed better for trials in which goal location indicated by the odour cue was congruent with odour sampling location (stay trials). n = 3 rats, ANOVA, p = 0.01, Mean ± S.D. (d) Actions performed for error trials. ANOVA, p = 0.07. Mean ± S.D.

Extended Data Fig. 3 pPCx location selectivity remains stable across recording blocks.

(a) Rat position on the maze during two blocks within the same recording session. (b-d). Occupancy normalized firing rate heat map, PETH, and raster plots associated with example cells 1-3 in Fig 2a–c. PETH and raster plots were aligned to initiation port poke-in and sorted by location. (e). Z-scored mean firing rate of neurons for different port locations across blocks. (f) Stability of port location representation between the two blocks was calculated the pairwise Pearson’s correlation coefficient for population activity across different locations in these two blocks. Values along the diagonal band in the correlation coefficient matrix represent the similarity of the pPCx population response to the same location in two recording blocks. (f, top) Similarity matrix obtained by computing the pairwise Pearson’s correlation coefficients between locations population response vectors for blocks 1 and 2 of example session shown in (a-e). Coefficients along the diagonal band are significantly higher than off-diagonal (r_diag = 0.80 ± 0.05, r_offdiag = 0.54 ± 0.09, Wilcoxon rank-sum test, p < 0.01), indicating that location representations were stable between blocks. (f, bottom). Same analysis as in the top panel but for all pPCx neurons recorded (r_diag = 0.81 ± 0.02, r_offdiag = 0.47 ± 0.02, Wilcoxon rank sum test, p < 0.001, n = 44 sessions, 995 neurons). We conclude from these data that uncontrolled odours were unlikely to account for the pPCx location representations observed.

Extended Data Fig. 4 Summary of pPCx and CA1 response properties for odours and port locations in task.

(a, top) Firing rate for individual pPCx neurons throughout recording sessions. Non-selective neurons: median = 0.94, range = 0.03 − 21.48 Hz, n = 531 neurons; selective neurons (for either odour or location): median = 1.95, range = 0.04 − 27.49 Hz, n = 464 neurons. (a, bottom) Firing rate of neurons grouped by selectivity properties. Odour-selective only neurons (Odour), n = 120 neurons, median = 1.93, range = 0.05 − 17.84 Hz. Location-selective only neurons (Loc.), n= 238, median = 2.06, range = 0.04 − 27.49 Hz. Odour and location selective neurons (Both): n = 106, median = 1.88, range = 0.14 − 17.41 Hz. (b, top) Firing rates for individual neurons across 4 odour identities during 0 − 1.0 s post odour onset. Neurons are grouped by selectivity properties. Red tick: median; edges of the bar indicate the 25th and 75th percentiles; circles: outliers. (b, bottom) Same as (b, top) but across port locations. (c, left) Fraction of recorded pPCx neurons in a session that was activated by different odours. Activation was measured by comparing mean firing rate for 1 s after odour onset time compared to a 1 s baseline immediately preceding odour onset (n = 33 sessions). Wilcoxon rank-sum test, p < 0.05, corrected for multiple comparisons (see Full Methods). (c, right) Histogram of number of odours that activate pPCx cells in a session. (d) Similar to (c) but for port locations. Location selectivity of individual neurons was obtained from comparing firing rates in 20 x 20 cm position bins centered around individual port locations on maze using ANOVA-test, p<0.01 (e) Sparseness across odour identity for simultaneously recorded populations. Population sparseness = 0.36 ± 0.09; lifetime sparseness = 0.21 ± 0.05, Mean ± S.D. (n = 33 sessions). (f) Similar to (e) but for port locations. Population sparseness = 0.40 ± 0.12; lifetime sparseness = 0.29 ± 0.06. (g) Coefficient of variation for odours aligned to odour onset. Trial-to-trial coefficient of variation for each neuron was calculated for different odour trials and averaged. (h) Similar to (g) but calculated for different port locations. (i-o) Same analysis as (a-h) for CA1 population. (i, top) Non-selective neurons: median = 1.22, range = 0.12 − 38.15 Hz, n = 88 neurons; selective neurons (for either odour or location): median = 2.35, range = 0.11 − 36.11 Hz, n = 67 neurons. (i, bottom) Location-selective only neurons (Loc.), n = 44, median = 1.84, range = 0.11 − 36.11 Hz. Odour and location selective neurons (Both): n = 23, median = 3.20, range = 0.26 − 20.23 Hz. (m) Population sparseness = 0.11 ± 0.05; lifetime sparseness = 0.16 ± 010, Mean ± S.D. (n = 15 sessions). (n) Population sparseness = 0.13 ± 0.08; lifetime sparseness = 0.20 ± 0.10.

Extended Data Fig. 5 Population correlations matrices and single cell decoding accuracies.

(a) Full similarity matrix of correlation coefficients of all odour and location population vector pairs for all recorded pPCx (n = 995) and CA1 (n = 154) neurons. Off-diagonal correlation coefficients on the lower right quadrants of each matrix show that CA1 location representations are more dissimilar from each other than pPCx location representations. Off-diagonal correlation coefficients on the top right and lower left quadrants show that there were no systematic relationship population responses for individual odours and locations. (b) Population correlation coefficients for odour and locations were shown in similarity matrices. Odour: top left quadrant; location: bottom right quadrant, excluding autocorrelation coefficients (diagonal band). Wilcoxon rank-sum test, ** p < 0.01, *** p < 0.001. (c) L1 regularization pseudo-population decoding curves in Fig. 2g (black lines) were overlaid on the same x-axis for comparison. (d) Cumulative distribution function of single cell decoding accuracies for all recorded pPCx (left) and CA1 (right) neurons show that individual pPCx neurons location is significantly better coding than odour. Kolmogorov–Smirnov test, *** p < 0.001.

Extended Data Fig. 6 Pseudo-population decoding.

(a) Odour decoding accuracy for a simultaneously recorded pPCx population (example session from Fig. 2a–d) using a wide range of time windows aligned to odour onset time (left), or first respiration after odour onset (right). The black and red dots indicate time windows used for black and red lines in (c), respectively. (b) Location decoding accuracy aligned to initiation port poke-in time across a wide range of time windows. (c) Pseudo-population decoding of odour identity with different time windows and regularization. Red and purple lines use L2 regularization, while black and grey use L1 regularization (shown as black and grey lines in Fig. 2g). By increasing the sparsity of the L1-decoder (scanning the ‘cost’ parameter over the range 2^[−7:8]) and plotting decoding accuracy as a function of the number of contributing neurons (# of neurons with non-zero weights in the decoder), we can minimize the contribution of uninformative neurons. Here, the x-axis indicates the number of neurons being used by the decoder (i.e. neurons with non-zero kernel weights), which was controlled by changing the L1 penalty. When this penalty is large, the decoder selectively uses only the most informative neurons, leading to a much steeper rise than seen for the L2 regularization pseudo-population curves, which sample neurons randomly. Using this sparse L1-decoding approach, it is clear that odour identity can accurately be decoded from a small population of pPCx neurons (90% decoding accuracy for ~150 neurons). Dotted line indicates 90% accuracy. Chance level is 25%. (d) Same analysis as in (c) but for port locations. Note that while it conveys that relatively few neurons are needed to encode odour information, the steepness of the grey curve is sensitive to the number of recorded neurons (since a larger pool is more likely to contain an informative neuron). (e, f) Yellow lines show pseudo-population decoding using neurons identified by auROC (see Methods) as responsive to either odour or location. Black and grey lines are the same as those plotted in (c, d), and Fig. 2g, reproduced here for ease of comparison.

Extended Data Fig. 7 Location decoding accuracy is independent of olfactory drive.

(a) Distance from port locations aligned to odour onset. (b) Population decoding accuracy for odour and port locations across time for simultaneously recorded pPCx (n = 33 sessions; 8-53 cells/session) . Pre-odour decoding used population activity from 1.5 s before odour onset; post-odour decoding used population activity 1.5 s after odour onset. Cross-validated, chance is 25%. The filled-in data points indicate the example session shown in Fig. 2. (c) Same analysis as in (b) for simultaneously recorded CA1 population (n=12 sessions, 3–14 neurons/session).

Extended Data Fig. 8 Example cells and location decoding for correct and error trials.

(a) PETH and rastors for 4 example neurons aligned to initiation port poke-in (left) and goal poke-in time (right). Black: correct trials; red: error trials. (b) Firing rate heat maps for example cells. Heat maps were normalized to occupancy and generated by concatenating all trials from −1 to 2 s window around initiation and goal port entry. Peak firing rates noted to the right of heat maps. (c) Location decoding accuracy of a classifier trained on neural activity −0.5 − 0.5 s around initiation port poke-in for correct trials. The classifier was tested on neural activity −0.5 − 0.5 s around initiation and goal port poke-ins for correct (black) and error (red) trials (cross-validated, mean ± S.E.M., n = 33 sessions; 30 ± 13 neurons/session).

Extended Data Fig. 9 Sniffing and hippocampal theta-band activity in task.

(a) Sniffing behavior on maze in one example session. Heat map of basal sniffing behavior (2 Hz power) and high frequency active sniffing behavior (7-10 Hz power) on maze. Colorbar is power. (b) Sniffing behavior for one example session. Sniff phase was time-locked to odour port poke-in. Gray color is inhalation, white is exhalation. (c) Coherence between CA1-LFP and sniffing. Time is aligned to odour onset. Colorbar is coherence. (d, top) Average coherence of spike of all pPCx neurons to sniff (left) and CA1-LFP (right). Time is aligned to odour onset time. (d, bottom) Average coherence of spike of all recorded CA1 neurons to sniffing (left) and CA1-LFP (right).

Extended Data Fig. 10 Preferential coupling of odour cells to sniffing and location cells to hippocampal theta.

(a) Odour and location decoding accuracy of individual neurons. Neurons decoded odour only (red), location only (cyan), both (magenta), or neither (open circles). Decoding significance was defined as accuracy greater than the 95th percentile of classifiers trained on shuffled labels. (b) Mean difference in coherence between the ‘top n’ best odour decoding neuron vs ‘top n’ best location decoding neuron. Top panels of each ‘top n’ analysis show differences in spike-sniff coherence, while bottom panels show differences in spikes-to-CA1-LFP coherence. Red pixels indicate frequency-time bins in which spikes from odour decoding cells are significantly more coherent than spikes from location decoding neurons, while blue pixels indicate the bins in which location cells are more significantly coherent than odour cells. Gray scale is coherence. (see Full Methods).

Extended Data Fig. 11 Noise correlations between pPCx neurons are consistent with distinct functional subgroups.

Noise correlations between 3 groups of neurons (6 group pairings): odour-selective (O), location-selective (L), non-selective (N) neurons. Overall noise correlation between groups increased with longer time windows. O-O and L-L correlations are higher than for N-N. Mean ± S.E.M., ** p < 0.01, *** p < 0.001.

Supplementary information

Supplementary Information

This file contains Supplementary Discussion.

Reporting Summary

Supplementary Video 1

Supplementary Video. Three example trials of a rat behaving on odour-cued spatial navigation task.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Poo, C., Agarwal, G., Bonacchi, N. et al. Spatial maps in piriform cortex during olfactory navigation. Nature 601, 595–599 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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