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Olfactory network dynamics and the coding of multidimensional signals

Key Points

  • The brain faces many complex problems when dealing with odorant signals. Odours are multidimensional objects that we usually experience as unitary percepts. They are also noisy and variable, but we can classify and identify them well. The olfactory system therefore solves complicated pattern-learning and pattern-recognition problems.

  • I propose that part of the solution relies on a particular architecture that imposes a dynamic format on odour codes. According to this hypothesis, the olfactory system actively creates a large coding space in which to place odour representations and simultaneously optimize their distribution within it.

  • This process uses both oscillatory and non-periodic dynamic processes that serve complementary roles: slow non-periodic processes allow decorrelation (that is, the reduction of the overlap between odour representations); fast oscillations allow sparsening (that is, a reduction in the size of the coding assemblies) and feature binding (that is, the representation of multiple and co-occurring features by the spikes of single neurons).

  • The prominent role of oscillatory synchronization in the process of sparsening is reviewed. Briefly, sparsening is achieved through a process that involves periodic input, coincidence detection, fan-in and fan-out connection patterns, and delayed feedforward inhibition. These mechanisms together lead to the appearance of rare but highly selective neuronal responses, which synthesize specific combinations of input features.

  • The coding aspects, advantages, disadvantages and possible uses of these interlocked and dynamic integrative phenomena are discussed in the context of olfaction and other systems in which complex sensory objects must be represented, learned and recognized.

Abstract

The brain faces many complex problems when dealing with odorant signals. Odours are multidimensional objects, which we usually experience as unitary percepts. They are also noisy and variable, but we can classify and identify them well. This means that the olfactory system must solve complicated pattern-learning and pattern-recognition problems. I propose that part of the solution relies on a particular architecture that imposes a dynamic format on odour codes. According to this hypothesis, the olfactory system actively creates a large coding space in which to place odour representations and simultaneously optimizes their distribution within it. This process uses both oscillatory and non-periodic dynamic processes with complementary functions: slow non-periodic processes underlie decorrelation, whereas fast oscillations allow sparsening and feature binding.

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Figure 1: Cellular elements underlying lateral inhibitory connections in the vertebrate OB and insect AL.
Figure 2: Slow temporal patterning of odour responses.
Figure 3: Schematic representation of the possible functions of olfactory circuit dynamics and their organization in time and space.
Figure 4: The locust olfactory circuits and the transformation of response properties between the first and second relay.
Figure 5: Mechanisms and possible consequences of sparsening of sensory representations by oscillatory patterning and coincidence detection.

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References

  1. Kauer, J. S. & White, J. Imaging and coding in the olfactory system. Annu. Rev. Neurosci. 24, 963–979 (2001).

    Article  CAS  Google Scholar 

  2. Hansson, B. S. (ed.) Insect Olfaction (Springer, Berlin, 1999).

    Book  Google Scholar 

  3. Knudsen, J. T., Tollsten, L. & Bergstrom, L. G. Floral scents — a checklist of volatile compounds isolated by headspace techniques. Phytochemistry 33, 253–280 (1993).

    Article  CAS  Google Scholar 

  4. Laing, D. G. in The Human Sense of Smell (eds Laing, D. G., Doty, R. L. & Breipohl, W.) 241–259 (Springer, Berlin, 1991).

    Google Scholar 

  5. Chandra, S. & Smith, B. H. Analysis of synthetic processing of odour mixtures in the bee (Apis mellifera). J. Exp. Biol. 201, 3113–3121 (1998).

    CAS  PubMed  Google Scholar 

  6. Livermore, A. & Laing, D. G. Influence of training and experience on the perception of multicomponent odor mixtures. J. Exp. Psychol. 22, 267–277 (1996).

    CAS  Google Scholar 

  7. Duchamp-Viret, P. & Duchamp, A. Odor processing in the frog olfactory system. Prog. Neurobiol. 53, 561–602 (1997).

    Article  CAS  Google Scholar 

  8. Friedrich, R. & Laurent, G. Dynamical optimization of odor representations in the olfactory bulb by slow temporal patterning of mitral cell activity. Science 291, 889–894 (2001).A paper showing the decorrelation of odour representations by MC assemblies resulting from slow MC temporal patterning. The data indicate that activity across MCs is redistributed across the population so that overlap between the representations of similar odours decreases as a function of time within the first 800 ms of a response.

    Article  CAS  Google Scholar 

  9. Wellis, D. P., Scott, J. W. & Harrison, T. A. Discrimination among odorants by single neurons of the rat olfactory bulb. J. Neurophysiol. 61, 1161–1177 (1989).

    Article  CAS  Google Scholar 

  10. Burrows, M., Boeckh, J. & Esslen, J. Physiological and morphological properties of interneurons in the deutocerebrum of male cockroaches with responses to female pheromones. J. Comp. Physiol. A 145, 447–457 (1982).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  12. Buonviso, N., Chaput, M. A. & Berthommier, F. Temporal pattern analyses in pairs of neighboring mitral cells. J. Neurophysiol. 68, 417–424 (1992).

    Article  CAS  Google Scholar 

  13. Laurent, G., Wehr, M. & Davidowitz, H. Temporal representations of odors in an olfactory network. J. Neurosci. 16, 3837–3847 (1996).

    Article  CAS  Google Scholar 

  14. Macrides, F. & Chorover, S. L. Olfactory bulb units, activity correlated with inhalation cycles and odor quality. Science 175, 84–87 (1972).References 14 and 19 are among the first papers to indicate that mammalian and non-mammalian vertebrate MCs show slow temporal patterning in response to odours.

    Article  CAS  Google Scholar 

  15. 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 

  16. Motokizawa, F. Odor representation and discrimination in mitral tufted cells of the rat olfactory bulb. Exp. Brain Res. 112, 24–34 (1996).

    Article  CAS  Google Scholar 

  17. Adrian, E. Olfactory reactions in the brain of the hedgehog. J. Physiol. (Lond.) 100, 459–473 (1942).One of the first papers to indicate the existence of oscillatory dynamics in the mammalian olfactory system.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  19. Kauer, J. S. & Moulton, D. Responses of olfactory bulb neurones to odour stimulation of small nasal areas in the salamander. J. Physiol. (Lond.) 243, 717–737 (1974).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  21. Stopfer, M., Bhagavan, S., Smith, B. H. & Laurent, G. Impaired odour discrimination on desynchronization of odour-encoding neural assemblies. Nature 390, 70–74 (1997).This paper indicates that oscillatory synchronization is required for fine odour discrimination. The authors used selective pharmacological blockade of oscillatory synchronization in the honeybee AL, and behavioural assessments to determine its consequences for odour discrimination.

    Article  CAS  Google Scholar 

  22. Perez-Orive, J. et al. Oscillations and sparsening of odor representations in the mushroom body. Science 297, 359–365 (2002).The first description of responses of MB KCs to odours. This paper provides direct physiological evidence that oscillatory synchronization is a dynamic mechanism used by a brain circuit to bind separate elements in a sensory representation and so sparsen that representation.

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  24. Friedrich, R. W. & Korsching, S. I. Combinatorial and chemotopic odorant coding in the zebrafish olfactory bulb visualized by optical imaging. Neuron 18, 737–752 (1997).

    Article  CAS  Google Scholar 

  25. MacLeod, K. & Laurent, G. Distinct mechanisms for synchronization and temporal patterning of odor-encoding neural assemblies. Science 274, 976–979 (1996).

    Article  CAS  Google Scholar 

  26. Bazhenov, M. et al. Model of transient oscillatory synchronization in the locust antennal lobe Neuron 30, 553–567 (2001).

    Article  CAS  Google Scholar 

  27. Bazhenov, M. et al. Model of cellular and network mechanisms for temporal patterning in the locust antennal lobe. Neuron 30, 569–581 (2001).

    Article  CAS  Google Scholar 

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

  29. Gray, C. Synchronous oscillations in neuronal systems, mechanisms and function. J. Comput. Neurosci. 1, 11–38 (1994).

    Article  CAS  Google Scholar 

  30. Engel, A. K., Fries, P. & Singer, W. Dynamic predictions, oscillations and synchrony in top-down processing. Nature Rev. Neurosci. 2, 704–716 (2001).References 29 and 30 are good reviews of synchronous periodic phenomena in various brain areas and circuits.

    Article  CAS  Google Scholar 

  31. 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 

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

    Article  CAS  Google Scholar 

  33. MacLeod, K., Bäcker, A. & Laurent, G. Who reads temporal information contained across synchronized and oscillatory spike trains? Nature 395, 693–698 (1998).The authors examine the consequences of a pharmacological blockade of synchronization on the specificity of neuronal responses downstream of the desynchronized assemblies. This paper complements references 21 and 22 in testing directly the possible function of oscillatory synchronization in a brain circuit.

    Article  CAS  Google Scholar 

  34. Laurent, G. et al. Odor encoding as an active, dynamical process: experiments, computation and theory. Annu. Rev. Neurosci. 24, 263–297 (2001).

    Article  CAS  Google Scholar 

  35. Abeles, M. Role of the cortical neuron, integrator or coincidence detector? Isr. J. Med. Sci. 18, 83–92 (1982).This paper and reference 36 make the case for coincidence detection as a key to understanding transformations by cortical neurons. According to this view, neurons exploit cellular and biophysical properties, as well as the spatiotemporal format of their inputs, to transform representations.

    CAS  PubMed  Google Scholar 

  36. König, P., Engel, A. K. & Singer, W. Integrator or coincidence detector? The role of the cortical neuron revisited. Trends Neurosci. 19, 130–137 (1996).

    Article  Google Scholar 

  37. Von der Malsburg, C. & Schneider, W. A neural cocktail party processor. Biol. Cybern. 54, 29–40 (1986).

    Article  CAS  Google Scholar 

  38. Linster, C. & Smith, B. H. Generalization between binary odor mixtures and their components in the rat. Physiol. Behav. 66, 701–707 (1999).

    Article  CAS  Google Scholar 

  39. Schidberger, K. Local interneurons associated with the mushroom bodies and the central body in the brain of Acheta domesticus. Cell Tissue Res. 230, 573–586 (1983).

    Article  Google Scholar 

  40. Grünewald, B. Morphology of feedback neurons in the mushroom body of the honeybee, Apis mellifera. J. Comp. Neurol. 404, 114–126 (1999).

    Article  Google Scholar 

  41. Marr, D. A theory of cerebellar cortex. J. Physiol. (Lond.) 202, 437–470 (1969).Marr examines the consequences of cerebellar architecture on the possible format of memories and on the management of overlaps between memories.

    Article  CAS  Google Scholar 

  42. Kanerva, P. Sparse Distributed Memory (MIT Press, Cambridge, Massachusetts, 1988).Kanerva examines (more generally than Marr) the case of sparse and distributed representations and the application of this thinking to cerebellar architecture. This book and reference 41 are interesting reading in general, and especially in view of experimental findings summarized in the current review.

    Google Scholar 

  43. Freeman, W. J. Neurodynamics: an Exploration in Mesoscopic Brain Dynamics (Springer, London, 2000).

    Book  Google Scholar 

  44. Buck, L. B. Information coding in the vertebrate olfactory system. Annu. Rev. Neurosci. 19, 517–544 (1996).

    Article  CAS  Google Scholar 

  45. Axel, R. The molecular logic of smell. Sci. Am. 273, 130–137 (1995).

    Article  Google Scholar 

  46. Mombaerts, P. et al. Visualizing an olfactory sensory map. Cell 87, 675–686 (1996).

    Article  CAS  Google Scholar 

  47. Vassar, R. et al. Topographic organization of sensory projections to the olfactory bulb. Cell 79, 981–991 (1994).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  49. Dong, D. W. & Atick, J. J. Temporal decorrelation — a theory of lagged and nonlagged responses in the lateral geniculate nucleus. Netw. Comput. Neural Syst. 6, 159–178 (1995).

    Article  Google Scholar 

  50. Dan, Y., Atick, J. J. & Reid, R. C. Efficient coding of natural scenes in the lateral geniculate nucleus: experimental test of a computational theory. J. Neurosci. 16, 3351–3362 (1996).

    Article  CAS  Google Scholar 

  51. Vinje, W. E. & Gallant, J. L. Sparse coding and decorrelation in primary visual cortex during natural vision. Science 287, 1273–1276 (2000).

    Article  CAS  Google Scholar 

  52. Bergman, H. & Bar-Gad, I. Stepping out of the box, information processing in the neural networks of the basal ganglia. Curr. Opin. Neurobiol. 11, 689–695 (2001).

    Article  Google Scholar 

  53. Schürmann, F. W. On the functional anatomy of the corpora pedunculata in insects. Exp. Brain Res. 19, 406–432 (1974).

    Article  Google Scholar 

  54. Zars, T. et al. Localization of a short-term memory in Drosophila. Science 288, 672–675 (2000).

    Article  CAS  Google Scholar 

  55. Dubnau, J., Grady, L., Kitamoto, T. & Tully, T. Disruption of neurotransmission in Drosophila mushroom body blocks retrieval but not acquisition of memory. Nature 411, 476–480 (2001).

    Article  CAS  Google Scholar 

  56. McGuire, S. E., Le, P. T. & Davis, R. L. The role of Drosophila mushroom body signaling in olfactory memory. Science 293, 1330–1333 (2001).References 54–56 report recent experiments geared towards identifying the locus or loci of olfactory/associative memories in Drosophila . All point to KCs as being crucial cellular elements, although it is still unclear which synaptic sites are modified and what their roles are for memory or recall.

    Article  CAS  Google Scholar 

  57. Cajal, S. R. Histology of the Nervous System (Oxford Univ. Press, New York, 1995).

    Google Scholar 

  58. DeVries, S. H. & Baylor, D. A. Synaptic circuitry of the retina and olfactory bulb. Cell 72, 139–149 (1993).A good review summarizing the proposal that local circuits in the OB serve to sharpen the tuning curves of MCs in a process akin to that which occurs in retinal local circuits. This paper and reference 15 are focused on single neuron data and on the traditional tuning-curve assessment of neuronal responses; they are to be contrasted with the systems and dynamic perspective that is proposed in the current review.

    Article  Google Scholar 

  59. Mori, K., Kishi, K. & Ojima, H. Distribution of dendrites of mitral, displaced mitral, tufted and granule cells in the rabbit olfactory bulb. J. Comp. Neurol. 219, 339–355 (1983).

    Article  CAS  Google Scholar 

  60. Orona, E., Rainer, E. C. & Scott, J. W. Dendritic and axonal organization of mitral and tufted cells in the rat olfactory bulb. J. Comp. Neurol. 226, 346–356 (1984).

    Article  CAS  Google Scholar 

  61. Hansson, B. S., Anton, S. & Christensen, T. A. Structure and function of antennal lobe neurons in the male turnip moth, Agrotis segetum (Lepidoptera, Noctuidae). J. Comp. Physiol. A 5, 547–562 (1994).

    Google Scholar 

  62. Stocker, R. F. The organization of the chemosensory system in Drosophila melanogaster — a review. Cell Tissue Res. 275, 3–26 (1994).

    Article  CAS  Google Scholar 

  63. Chen, W., Midtgaard, J. & Shephard, G. Forward and backward propagation of dendritic impulses and their synaptic control in mitral cells. Science 278, 463–467 (1997).

    Article  CAS  Google Scholar 

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Acknowledgements

The work from my laboratory reviewed here was funded by the National Science Foundation, the National Institute on Deafness and other Communication Disorders, and the McKnight, Keck, Sloan and Sloan-Swartz Foundations. I thank M. Stopfer, R. Friedrich, K. McLeod, M. Wehr, J. Perez-Orive, O. Mazor, S. Cassenaer, R. Wilson, G. Turner, C. Pouzat, V. Jayaraman, S. Farivar, H. Davidowitz, R. Jortner, A. Holub, M. Rabinovich, H. Abarbanel, R. Huerta, T. Nowotny, V. Zighulin, A. Bäcker, M. Bazhenov, P. Perona and E. Schuman for the privilege of working on these problems with them. I thank P. Cariani for pointing me to Kanerva's book on sparse distributed memories, K. Heyman for secretarial assistance and S. Farivar for Golgis in figure 4.

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Glossary

LABELLED LINES

A term that is used to describe a simple connectivity, whereby a set of identically and sharply tuned receptor neurons converge uniquely onto a set of postsynaptic neurons, which in turn project uniquely onto a set of common targets (and so on). Each channel (labelled line) can unambiguously inform the brain about the presence or absence of the signal it conveys.

CODING SPACE

An abstract space that is defined by the features used to embody the code. If a neural system contains n neurons, one coding space can be viewed as an n-dimensional space, where each dimension represents the state of each neuron.

LOCAL FIELD POTENTIAL

The extracellular potential between two points in a brain region, resulting from synaptic and other current flow at and around the recording electrodes. It usually reflects input better than output.

ORBIT

The trajectory that is defined by a dynamical system, or its motion within state space. When applied to a system of neurons, an orbit is an abstract description of the states of all the neurons and the evolution of those states as a function of time.

BODIAN STAIN

A reduced-silver impregnation technique that is used for neuroanatomical studies of fixed brain tissue.

TETRODE

An extracellular electrode that comprises four juxtaposed recording channels, which can be used to disambiguate the signals emitted by individual point sources. Because each neuron occupies a unique position in space, its spikes are 'seen' slightly differently by each electrode, providing a unique signature. This technique allows the identification of many more neurons than there are sampling electrodes.

AFTERHYPERPOLARIZATION

The membrane hyperpolarization that follows the occurrence of an action potential.

HAMMING DISTANCE

The number of bits by which two n-bit vectors differ. For example, the Hamming distance between 001101 and 001110 is 2. It is also the square of the Euclidian distance.

HOPFIELD NETWORK

A type of trainable, asynchronous artificial neural network with symmetrical connections that defines sets of attractor states. Given a certain input set, a Hopfield network can therefore be made to settle into a given attractor, in a process akin to pattern completion.

ATTRACTOR

Given a dynamical system and the state space in which it lives (that is, all the possible states that this system can occupy), an attractor is a preferred region of state space; that is, a state or set of states to which the system moves inexorably as time approaches infinity.

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Laurent, G. Olfactory network dynamics and the coding of multidimensional signals. Nat Rev Neurosci 3, 884–895 (2002). https://doi.org/10.1038/nrn964

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