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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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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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